nature neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Human hippocampal ripples tune cortical responses based on predicted uncertainty Received: 11 February 2026 Darya Frank 1,2 , Stephan Moratti 3, Robin Hellerstedt 1, Johannes Sarnthein 4,5, Ningfei Li 6, Andreas Horn 6,7,8, Lukas Imbach5,9, Accepted: 20 May 2026 Lennart Stieglitz 4, Antonio Gil-Nagel 10, Rafael Toledano10, Published online: xx xx xxxx Karl J. Friston 11 & Bryan A. Strange 1,12 Check for updates To encode information efficiently, our perceptual system should detect when situations are unpredictable (that is, informative) and modulate brain dynamics to prepare for encoding. Under uncertainty, there is an increased need to generate predictions about upcoming information, a process that has been proposed to require coordinated activity between the hippocampus and neocortex. Here we show, with direct recordings from the human hippocampus and visual cortex, that after exposure to unpredictable visual stimulus streams, hippocampal ripple activity increases in frequency and duration before stimulus presentation. Prestimulus hippocampal ripples suppress changes in visual cortex gamma activity associated with uncertainty and modulate poststimulus prediction error gamma responses in higher-level visual cortex to surprising stimuli. We reveal a function of hippocampal ripples in facilitating the propagation of visual stimuli based on the expected information gain. These results, therefore, link hippocampal ripples with predictive coding accounts of neuronal message passing and precision-weighted prediction errors, revealing a mechanism relevant for perceptual synthesis and subsequent memory encoding. An efficient recognition system should be able to prioritize the infor- there is a mismatch between the predicted and the observed input, a mation that will constrain or inform perceptual representations1 and, bottom-up prediction error is returned to update or revise the source eventually, be accumulated or retained. One way to facilitate this is of predictions at a higher hierarchical level4,5. Crucially, prediction to continuously generate predictions about upcoming inputs and to errors are weighted by the level of uncertainty (that is, their precision) retain information—that is, revise beliefs—when these predictions are associated with the given context6, balancing top-down and bottom-up violated. Prediction is considered central in this account of the brain, information streams to scale the influence of prior predictions and by building a generative model of the world to minimize prediction sensory evidence, respectively7. This is sometimes framed in terms of error when sampling the sensorium2,3. In predictive coding formula- precision-weighted prediction errors that instantiate the Kalman gain tions, predictions are transmitted in a top-down manner, and, when in Bayesian filtering formulations of predictive coding1,4. The implicit 1 Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, Madrid, Spain. 2Andrew Mayes Centre for Cognitive Neuroscience, University of Manchester, Manchester, UK. 3Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain. 4Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland. 5Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland. 6Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany. 7Department of Neurosurgery, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA. 8Brigham & Women’s Hospital, Center for Brain Circuit Therapeutics, Boston, MA, USA. 9Swiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland. 10Epilepsy Unit, Department of Neurology, Hospital Ruber Internacional, Madrid, Spain. 11Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK. 12IDG/McGovern Institute for Brain Research, Peking University, Beijing, China. e-mail: darya.frank@manchester.ac.uk; bryan.strange@upm.es Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 encoding of uncertainty lends a dual aspect to predictive processing While ripples represent possible outcomes, there is mixed evidence that encompasses both the predictions of a particular sensation and regarding whether they influence subsequent behavior48–50. Notably, predictions of its predictability (that is, precision) that modulate the the functional role of ripples is often examined by using navigational influence of the ensuing prediction error8–13. Notably, these genera- tasks in rodents, which heavily tap episodic memory and are biased tive properties of predictive processing rely on ongoing integration by the provision of reward at the goal location. In humans, although of sensory inputs with internally generated, experience-dependent there is emerging evidence for ripples supporting memory recall sequences, and are therefore thought to involve hippocampal– processes51–53, it remains unclear to what extent they have a role in neocortical interactions14–16. prediction, in the absence of memory demands. A hippocampal role in prediction is likely related to its function We hypothesized that high-order predictions—namely, predic- in extracting statistical regularities17–20 that can be applied to new tions of predictability or precision—would be generated by the hip- situations21,22. Therefore, the hippocampus should represent the pocampus before stimulus presentation, as a function of uncertainty expected information gain of an event before it occurs—as a function (that is, predictability), and subsequently modulate cortical process- of predictability16,20—and estimate the validity of the prediction upon ing, or lower-level prediction errors. Ripples are a promising candidate observation of the event23. Indeed, the hippocampus has long been pos- to mediate the requisite precision weighting of prediction errors. We tulated to hold a cognitive map that is used to form predictions about tested this hypothesis using intracranial local field potential recordings upcoming inputs24–27. Such predictive information follow successor-like from patients with epilepsy to examine how the hippocampus and ven- representation28,29, for example, in place cell firing30. The cortex may tral visual stream regions (the occipital cortex and the fusiform gyrus) also have its own predictive role through communication across deep implement predictive processing. We used a simple paradigm—free layers feeding-back predictions to the superficial layers of preceding of any explicit demand on episodic memory—in which participants regions in the processing hierarchy29,30. Mechanistically, predictions were presented with a sequence of colored shapes and performed a and their violation have been associated with spiking activity31 and visuomotor selection task, given a target stimulus and four options oscillatory dynamics in the cortex32,33. Specifically, gamma-band activ- presented on-screen simultaneously. The probability distribution ity has been associated with bottom-up prediction errors (reflecting of stimulus presentation varied across task blocks, allowing for the surprise signals from primary sensory cortices) and alpha/beta oscil- quantification of stimulus-bound information-theoretic measures of lations with top-down predictions (from higher-level regions such as entropy (that is, uncertainty or unpredictability of an outcome before prefrontal cortex)34–36. it occurs) and self-information (that is, surprise or violation of predic- However, the mechanism through which predictions of future tions reflecting the improbability of a particular event) within each sensory inputs are generated in the hippocampus and communicated block. One might ask, how one can disambiguate the effects of surprise to the cortex is still unclear16. Irrespective of these mechanisms, they and entropy if—in the absence of entropy—there can be no surprise? should manifest as a differential modulation of prediction–error The answer is straightforward—surprise is an attribute of a particular responses in the visual cortex, depending on the predictability of the event, whereas entropy is an attribute of events that could happen. In current context, which we hypothesize is itself recognized and broad- other words, the surprise or self-information of an outcome scores the cast through hippocampal processing. Specifically, when upcoming degree to which the event was ‘predicted’, whereas entropy scores the stimuli are unpredictable, they are inherently informative in the sense ‘predictability’ of an event before it is observed. Technically, entropy that they resolve uncertainty when observed (technically, they have is the time average or an integral of surprise. This means entropy is a greater expected information gain). This leads to the hypothesis the change in surprise and, therefore, over time, surprise and entropy that the hippocampus has a role in precision weighting by modulat- are decorrelated (Fig. 1b). In the context of the current task, they can ing the electrophysiological correlates of prediction errors—that is, therefore be dissociated with respect to stimulus onset. event-related gamma activity in the visual hierarchy—as a function of predictability (or entropy). Results Hippocampal sharp-wave ripples (SWRs; ripples henceforth) are Seventeen participants successfully completed the task, with found in every investigated mammalian brain and consist of sharp trial-by-trial measures of entropy and surprise modulating reaction waves (large-amplitude, negative-polarity activity resulting from time (RT) in accordance with Hick’s law54. Replicating results from this synchrony in the apical dendritic layer of CA1 pyramidal neurons) task in healthy adults20, participants’ RTs increased significantly per and ripples (~140 to 200 s−1 in rodents, resulting from interactions bit of surprise (t(16) = 10.61, P < 0.001, Cohen’s d = 2.57) and entropy between excitatory and inhibitory neurons in CA3 (refs. 37,38)). They (t(16) = 4.86, P < 0.001, Cohen’s d = 1.18; Fig. 1c). are viewed as a preconscious mechanism to explore the organism’s options, searching for past experiences to extrapolate and predict Prestimulus ripples increase with entropy future outcomes27,39. This is in addition to their role in replay of past In view of a possible generative role for hippocampal ripples in predict- experiences38,40. As prospection relies on past experiences and is medi- ing future events, we tested whether ripple occurrence was associ- ated by the hippocampus25, ripples may also underlie anticipation of ated with increased uncertainty. Using a previously established ripple future outcomes to guide subsequent behavior. Indeed, ripples have detection method52, a total of 2,728 hippocampal ripples were detected been shown to portend behavior in the immediate future, in the form of in all participants while they performed the visuomotor task (Fig. 2a experienced trajectories41 and new paths42,43, as well as preplay of future and Extended Data Fig. 1). On examining the two-dimensional (2D) events during sleep44. Specifically, the sequential firing pattern that distribution of ripple peak time as a function of peri-stimulus time and occurs during rodent SWRs, in addition to ‘replay’ of spatial trajecto- entropy (Fig. 2b), we found a large proportion of ripples occurred in ries, has been shown to reflect all physically available trajectories within high entropy (1.7–1.9 bits) trials between −800 and −400 ms prestimu- the environment not realized in prior behavior43, and trajectories taken lus. There was also a peak in ripple probability just before the stimulus by participants in subsequent goal-directed navigation42. Furthermore, onset (−200 to 0 ms) for entropy values from 1.6–1.7 to 1.8–1.9 bits. there is evidence that cortical activity is modulated in a peri-ripple We tested for the significance of this observation in several ways. manner, both through enhancement and inhibition of activity45,46, and First, a two-sample Kolmogorov–Smirnov test showed a significant as fluctuations in resting-state networks47. This is in line with predictive difference between the ripple distribution and a uniform distribution processing frameworks in which predictions and prediction errors are with the same minimum and maximum counts (k = 0.241, P = 0.001). exchanged across levels in cortical hierarchies, with a special role for Next, we performed a Spearman’s correlation between the observed regions such as the hippocampus in contextualizing this exchange5,16. and permuted distributions to ensure that ripple distribution was not Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 a 2 d Entropy (bits) 1.5 1 50 100 150 200 250 300 350 400 450 2 Mean entropy (bits) 1.9 1.8 1.7 1.6 50 100 150 200 250 300 350 400 450 6 x = –40 Surprise (bits) 4 2 e 0 50 100 150 200 250 300 350 400 450 Trial number Entropy Surprise Entropy Surprise Entropy Surprise 2 2 2 2 2 2 1 1 1 1.92 2.32 1.92 2.32 1.92 2.32 1.92 2.58 1.91 1.58 1.92 2.58 1.84 1.22 1.84 1.81 1.84 1.22 Block 4 Block 9 1.75 1 Block 1 1.41 1.75 2 trials 1.81 trials trials 1.65 3.17 1.75 1.16 1.75 2.17 y = –47; z = –24 1.76 2.32 1.68 1 1.84 1.73 ... ... ... b c 5 300 250 4 200 Surprise (bits) 3 150 ms bits–1 100 2 50 1 0 –50 x = 46 0 0.8 1 1.2 1.4 1.6 1.8 2 –100 Entropy Surprise Entropy (bits) Fig. 1 | Task design and electrode contact localization. a, Distribution of For a comparison of model fits showing a linear correlation (r = 0.3) is inferior entropy, mean entropy and surprise values across trials for one example to nonlinear models, see Supplementary Note—Relationship between entropy participant. Bottom, example trials from 3 of 12 different blocks, and their and surprise. c, Group average (n = 17 patients) RT as an increase in ms per bit associated entropy (blue) and surprise (red) values. Please note that the four of information-theoretic measure. Error bars reflect 95% CI. d,e, Illustration of colored shapes presented were unique to each block, with the probability of contacts in the hippocampus (smoothed hippocampus mask from the AAL atlas occurrence of these four stimuli varying between blocks. Participants were asked for visualization; blue), fusiform (red) and occipital cortex (green) overlaid to perform a visuomotor selection task, choosing the corresponding colored on a 100-μm T1 scan of an ex vivo human brain, acquired on a 7T MRI scanner shape from four alternatives with a button press, as depicted for block 4 in the (https://openneuro.org/datasets/ds002179/versions/1.1.0); each color current example. b, Correlation between entropy and surprise values across all represents a patient. See Supplementary Fig. 1 for individual patients’ contacts. patients. Each patient is represented by a color. By design, entropy and surprise Unless otherwise stated, error bars represent the s.e.m. AAL, Automated are decorrelated, with high surprise occurring in the context of low entropy. Anatomical Labeling. correlated with random noise. The correlation between the two was on the normalized count values to identify the bins showing the computed in every permutation (1,000 permutations in total) per largest number of ripples, using entropy and time bin as predictors. participant, and the mean correlation across participants was com- We found a significant interaction between entropy and ripple peak pared to 0 using a one-sample t test. The observed 2D distribution was time (χ2(99) = 207.6, P < 0.001), with the five largest estimated marginal not correlated with the random permutations (t(16) = 1.24, P = 0.23). means identified around −1,000 to –400 ms and 1.6 to 1.8 entropy Third, we used a mixed-effects gamma generalized linear model (GLM) bins, as well as just before stimulus onset (−0.2 to 0, entropy values of Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 a b 300 200 100 Raw LFP 0 (uV) –100 25 –200 6 –300 20 –1 –0.8 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 0.8 1 150 5 10 15 Frequency (Hz) 5 4 Voltage (uV) Ripple band 80–120 Hz Power 0 100 3 10 –5 –10 2 5 –1 –0.8 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 0.8 1 50 4 1 0 Standardized amplitude 2 Hilbert –400 –200 0 200 400 –5 0 –400 –200 0 200 400 Time relative to ripple peak (ms) Time relative to ripple peak (ms) –2 –1 –0.8 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 0.8 1 Time (s) c d 600 500 Frequency 400 300 Ripple peak time (–0.2,0] (0,0.2] 200 1.9–2 100 1.8–1.9 0.045 0.045 0 * Normalized ripple count 20 40 60 80 100 120 140 3 Ripple duration (ms) 1.7–1.8 1.6–1.7 Ripple duration (S) Ripple duration (S) 1.5–1.6 0.040 0.040 Entropy 2 1.4–1.5 1.3–1.4 1.2–1.3 0.035 0.035 1 1.1–1.2 1–1.1 0.9–1 0.030 0.030 0.8–0.9 0 0 8 8 6 2 6 2 4 4 1 – – – – 0.025 0.025 0. 0. 0. 0. 0. 0. 0. 0. to to to to to to 8 2 to o to to 0. – 0. – 1t 0 6 4 2 1.00 1.25 1.50 1.75 2.00 1 2 3 4 4 8 6 0. 0. 0. – – – 0. 0. 0. Entropy Surprise Ripple peak time Fig. 2 | The frequency and duration of hippocampal ripples increase with occurred prestimulus and in high (but not highest) entropy levels (see Extended uncertainty or expected information gain. a, An example ripple detected using Data Fig. 3 for peri-stimulus ripple distribution). d, Ripple duration as a function the method discussed in ref. 52, showing the raw LFP trace (top), the ripple band of peri-stimulus time and information-theoretic measures. Ripple duration signal (middle) and the standardized envelope (bottom). b, Grand average peri- increased with entropy (that is, expected information gain for prestimulus ripple wavelet spectrogram (left) and raw field potential centered on ripple peak ripples), but decreased for poststimulus ripples; result from mixed-effects (n = 2,728 ripple events from 17 participants). c, Normalized ripple distribution gamma regression and subsequent omnibus χ2 Wald test for the interaction across peri-stimulus time and entropy levels, accounting for the total number of P = 0.0416). Ripple duration histogram, overall recorded ripples, is shown above trials in each entropy bin. Normalized ripple count for each time and entropy bin right. Lines represent the mean and shaded areas represent the 95% CI. *P < 0.05 is color coded; average counts for each time and entropy bin are plotted as bars (two tailed). See Extended Data Fig. 1 for replication using a different ripple above and to the right of the 2D distribution, respectively. The majority of ripples detection method51. LFP, local field potential. 1.8–1.9) and just after stimulus onset (0–0.2, entropy values of 1.8–1.9). effect, we applied a Poisson GLM predicting ripple counts as a func- This is in line with the visual representation of the 2D distribution tion of entropy, surprise and mean entropy for each time bin. The β shown in Fig. 2c (all estimated marginal mean values are shown in values across participants were then tested against zero in a two-tailed Extended Data Fig. 2). Next, we examined ripple frequency as a function t test using a cluster-based Monte Carlo permutation to correct for of time bin and peri-stimulus time using a two (pre/post) by five (time multiple comparisons as implemented in FieldTrip. In line with the bin) analysis of variance. We found a main effect of peri-stimulus time analyses reported above, we found a significant effect in the −800 to (F(1.16) = 6.02, P = 0.026), with more ripples observed prestimulus −600 ms time bin such that ripple count increased with entropy (clus- (Extended Data Fig. 3c), whereas the main effect of bins and the interac- ter t = 2.66, P = 0.009, 95% confidence interval (CI) = 0.002). No other tion were not significant (all Ps > 0.073). Finally, a threshold-free cluster significant effects were observed for entropy. For surprise, we found enhancement (TFCE) analysis of the 2D histogram showed significant a negative association with ripple count in the same time bin (−800 to effects again primarily in the entropy range of 1.8–1.9 bits, albeit for a −600 ms; t(16) = −1.89, P = 0.039, uncorrected), indicating that as sur- longer time period (Extended Data Fig. 3e). prise increases, there is a decrease in ripple count for this time window In view of a proposal55 that awake replay during SWRs increases (note the effect of surprise did not survive a correction for multiple following prediction error (that is, surprise), we then provide evi- comparisons). This analysis confirms that the increased prestimulus dence that human hippocampal ripple frequency is increased by ripple count is related to entropy, as opposed to the effect of surprise, unpredictability and not surprise. If ripples reflected processing and demonstrates statistical independence—we observed significant of prediction errors postpresentation of surprising stimuli, rather effects of entropy on ripple counts, but no significant effects of surprise than the predictability of upcoming stimuli, one would expect to across all time bins (Extended Data Fig. 2). Finally, we analyzed data see an increased ripple rate with surprise. However, the 2D distribu- from an independent control experiment56 using a classical ‘oddball’ tion of ripple peak time as a function of peri-stimulus time and sur- paradigm (Supplementary Note (Independent data from oddball task) prise (Extended Data Fig. 3d) shows cluster of ripples at low surprise and Supplementary Table 2). Participants were presented with lists (at 1–1.5 bits). Next, to further demonstrate that the increased ripple of words where occasional perceptually distinct stimuli (perceptual count observed in the prestimulus time bin is not driven by a surprise oddballs) appeared in a different font among standard control words. Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 These oddball stimuli were highly surprising by design (that is, this is a cortex, lower down in the visual cortical hierarchy than the fusiform standard paradigm for eliciting prediction errors), yet we observed no and putatively supplying it with bottom-up information (for example, increased ripple count for oddball stimuli compared to control words prediction errors). Hippocampal activity in the gamma range (57.5– (Supplementary Fig. 2 and Supplementary Table 3). The dissocia- 97.5 Hz; Fig. 3a) was negatively associated with entropy in the pres- tion between oddball surprise (no ripple increase) and entropy-based timulus period, around −990 to −670 ms prestimulus (summed cluster uncertainty (clear ripple increase) supports our interpretation that t = −762.8, P = 0.0218; smallest t value in cluster = −4.57, P = 0.0218, ripples encode predictions about predictability rather than responses Cohen’s d = 1.08), showing reduced gamma power preceding more to unpredicted events. uncertain outcomes (see Extended Data Fig. 4 for split by the presence The duration of ripples has been shown to be functionally relevant of prestimulus ripple). This time window partly overlaps with peaks in for memory consolidation in rodents57. In predictive coding formula- ripple occurrence, although, notably, the gamma power around this tions, this rests on the augmentation of associative (that is, activity or time window was reduced under high entropy. Further examination of experience dependent) plasticity by precision weighting that increases the negative association with entropy, as a function of trial in block, presynaptic (prediction error) afferents4,58. To test for changes in rip- showed that the reduction in gamma power was mostly concentrated ple duration with uncertainty, we then examined the relationship in the first few trials of the block (Extended Data Fig. 5), before the between ripple duration and the information-theoretic measures. emergence of prestimulus ripples. As ripple duration followed a right-skewed distribution (Fig. 2d), a In the occipital cortex, on the other hand, a positive associa- mixed-effects gamma regression was used. First, we tested whether tion between gamma activity (35–117.5 Hz; Fig. 3c) and entropy was ripple duration was modulated by peri-stimulus time, entropy and observed around −510 to −130 ms prestimulus (summed t = 1,694.1, surprise. We did not observe any significant main effects (all Ps > 0.381) P = 0.0078; largest t value in cluster = 10.67, P = 0.0078, Cohen’s or interactions (time window by entropy—χ2(1) = 0.665, P = 0.414; time d = 3.77). No significant responses to entropy were found in the fusi- window by surprise—χ2(1) = 0.171, P = 0.679; Extended Data Fig. 3j). form, or in the poststimulus time period for all three areas (Fig. 3). However, comparing ripples occurring just before (−200 to 0 ms, where Under the simplifying assumption that gamma activity reflects the there was an increase in ripple frequency; Fig. 2b) and the correspond- amplitude of precision-weighted prediction errors, these results are ing period just after the stimulus (0 to 200 ms) revealed a significant consistent with an increase in the precision of visual prediction errors, main effect of peri-stimulus time window (χ2(1) = 5.04, P = 0.0248), as with a concomitant decrease in the precision of hippocampal predic- well as an interaction between time window and entropy (χ2(1) = 4.15, tion errors, before stimuli with higher expected information gain. This P = 0.0416; Fig. 2d). This interaction suggests that as entropy increased, could be read as instantiating the right kind of attentional set, when prestimulus ripple duration increased, but decreased for poststimulus salient information can be anticipated in advance59. ripples. Ripple duration was again not modulated by surprise (main effect—χ2(1) = 0.279, P = 0.597; interaction—χ2(1) = 0.31, P = 0.577), sug- Ripple-triggered cortical responses gesting that ripple duration reports predictability (that is, expected Next, we investigated whether ripple occurrence instates a tempo- information gain) as opposed to violations of predictions (that is, ral relationship between the hippocampus and visual processing observed information gain). regions in which prestimulus modulations of gamma activity were also observed as a function of entropy. First, we compared peri-ripple RT as a function of ripples cortical time–frequency (TF) responses, dichotomized by whether the Given that there is a relationship between entropy and RT20,54, as well hippocampal ripple occurred before or after stimulus onset (Fig. 4a,b). as between ripples and entropy, we examined whether the presence We found an overall increase in high gamma power in the fusiform of ripples modulates the relationship between RT and entropy—and (−200 to 200 ms peri-ripple, 105–160 Hz, summed cluster t = 2,171.1, surprise—on a trial-by-trial basis. To ensure that sufficient prestimulus P = 0.0156; largest t value in cluster = 4.44, P = 0.0156, Cohen’s d = 1.67) ripple trials were included, this analysis was restricted to trials with as well as in occipital cortex (−180 to 200 ms peri-ripple, 95–160 Hz, 1.6–1.9 bits of entropy, where most prestimulus ripples were observed summed cluster t = 1,474, P = 0.0039; largest t value in cluster = 9.15, (50% prestimulus ripple trials and 50% no ripple trials are within these P = 0.0039, Cohen’s d = 3.27) locked to hippocampal ripples. Further- values). We found a significant main effect of ripple status, with faster more, in occipital cortex, prestimulus and poststimulus ripples modu- responses in trials with a prestimulus ripple (χ2(1) = 4.33, P = 0.037). This lated activity differently (−10 to 200 ms peri-ripple, 35–160 Hz; Fig. 4c; is an important observation because the precision of prediction errors summed t = −1,094.5, P = 0.0039; smallest t value in cluster = −8.99, corresponds to the rate of evidence accumulation that underwrites P = 0.0039, Cohen’s d = 3.17). Specifically, occipital gamma activity reaction speed. In other words, prediction errors that are afforded was reduced after prestimulus ripples compared to poststimulus rip- more precision exert their effects on belief updating more rapidly. ples. This effect was partly driven by a power suppression, compared There was also a main effect of surprise, with faster responses for to a baseline period, of occipital gamma around prestimulus ripples lower levels of surprise (χ2(1) = 91.3, P < 0.001), as per Hick’s law. The (Fig. 4d shows raw power suppression in relation to baseline). That is, interaction between ripple status and surprise, however, was not sig- when examining occipital gamma activity in the prestimulus period nificant (χ2(1) = 2.58, P = 0.107), indicating that ripples did not modu- in which a positive association with entropy was found (indicating an late the relationship between speed and surprise. There was a trend overall power increase), there was reduced gamma power in trials with toward an interaction between entropy and ripple status (χ2(1) = 3.41, a hippocampal ripple in the period before the significant cluster com- P = 0.064), with simple slopes of entropy in trials without ripples (esti- pared to trials with no ripples (t(7) = 2.51, P = 0.040, Cohen’s d = 0.889; mate = 0.2, t(16) = 1.55, P = 0.12) and in trials with prestimulus ripples Extended Data Fig. 6a). Taken together, these findings are indicative of (estimate = 0.59, t(16) = 3.28, P = 0.001), indicating a steeper positive a hippocampal prestimulus ripple-induced suppression of prestimulus slope in trials with prestimulus ripples. gamma power in the occipital cortex (see Extended Data Fig. 7 for repli- cation using a different ripple detection method). No differences were Region-specific responses to uncertainty observed between prestimulus and poststimulus ripple modulation To further characterize hippocampal and cortical correlates of entropy of fusiform activity, or in lower frequencies in either cortical region. and surprise, we then examined time-resolved spectral responses as Next, we examined whether ripple-modulated cortical activ- a function of these measures. The focus was on two cortical regions— ity was correlated with entropy or surprise. There were no signifi- the fusiform gyrus, previously shown with functional magnetic reso- cant associations between peri-ripple cortical activity and either nance imaging (fMRI) to respond to surprise in this task20, and occipital information-theoretic measure, suggesting that the hippocampal Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 a Hippocampus 160 4 3 140 0.15 2 0.1 120 Frequency (Hz) 0.05 Parameter estimates 1 T value 0 100 0 –0.05 80 –1 –0.1 –0.15 –2 60 –0.2 –3 –0.25 40 –4 –0.3 –1 –0.8 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 0.8 –0.35 Time (s) Entropy Surprise b Occipital cortex 160 4 0.2 3 140 0.15 2 Parameter estimates Frequency (Hz) 120 0.1 1 T value 100 0 0.05 80 –1 0 –2 60 –3 –0.05 40 –4 –0.1 –1 –0.8 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 0.8 Entropy Surprise Time (s) c Fusiform 160 4 3 140 2 Frequency (Hz) 120 1 T value 100 0 80 –1 –2 60 –3 40 –4 –1 –0.8 –0.6 –0.4 -0.2 0 0.2 0.4 0.6 0.8 Time (s) Fig. 3 | Hippocampal and occipital cortical gamma activity is modulated by Right, parameter estimates for each predictor within the significant cluster; entropy ‘before’ stimuli are presented. a, Left, time-resolved spectral power each point pertains to one patient, horizontal bars represent the group mean in the hippocampus (n = 17 patients) shows a significant negative association and vertical error bars represent 95% CI. b, In occipital cortex (n = 8), there was between gamma power and entropy around 800 ms prestimulus (significant an increase in gamma power as entropy increased, around 400 ms prestimulus. permutation-based cluster from one-sample two-tailed t test against a value of 0 Again, entropy was not associated with changes in the poststimulus time outlined in black here and in Figs. 4 and 5). During highest entropy levels, gamma windows. Each point pertains to one patient, horizontal bars represent the group power was lowest (see Extended Data Fig. 5 for gamma power as a function of trial mean and vertical error bars represent 95% CI. c, In the fusiform cortex (n = 7), no in block). No significant clusters were found in the poststimulus time window. effects of entropy were observed either prestimulus or poststimulus. ripple modulation of cortical response was not affected by uncertainty observations23. In the fusiform, there was a positive association between or surprise, and thus ripples may act as a general mechanism for hip- surprise and gamma activity (40–95 Hz; Fig. 5b) around 200–510 pocampal modulation of cortical activity, with context dependence of poststimulus (summed cluster t = 741.9, P = 0.0234; largest t value in this modulation affected by ripple rate and/or duration. cluster = 6.79, P = 0.0234, Cohen’s d = 2.56), and a negative association between surprise and alpha/beta power (10–15 Hz; Fig. 5c) around Hippocampal and fusiform gamma to surprise 510–840 ms poststimulus (summed cluster t = −199.5, P < 0.001; larg- We expected that prediction error, in the form of surprise, would elicit est negative t value in cluster = −5.41, P < 0.001, Cohen’s d = 2.05). hippocampal and cortical responses. In line with previous findings, No significant associations between surprise and occipital activity a positive association between surprise and hippocampal activity in were observed. the gamma range (52.5–90 Hz; Fig. 5a and see Extended Data Fig. 8 for replication using a different ripple detection method) was observed Ripple-modulated responses to surprise around 160–540 ms poststimulus (summed cluster t = 888.2, P = 0.016; Under the predictive processing framework, a cardinal function of largest t value in cluster = 4.91, P = 0.016, Cohen’s d = 1.2) and a nega- neural activity is to minimize precision-weighted prediction error5. tive association between surprise and hippocampal activity in theta A possible role for hippocampal ripples is to broadcast the predicted band (5–12.5 Hz; Extended Data Fig. 9a) later in the trial (530–810 ms precision of forthcoming prediction errors. Specifically, we hypoth- poststimulus; summed cluster t = −169.4, P = 0.011; smallest t value esized that prestimulus ripples would amplify precision-weighted in cluster = −3.19, P = 0.011, Cohen’s d = 0.773) in line with previous prediction errors, evoked by stimuli in an unpredictable (high entropy) Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 a Fusiform b Occipital cortex all ripples all ripples 160 4 160 4 Peri-ripple OCC power (t value) Peri-ripple OCC power (t value) 3 3 140 140 2 2 Frequency (Hz) 120 120 Frequency (Hz) 1 1 100 0 100 0 80 –1 80 –1 –2 –2 60 60 –3 –3 40 40 –4 –4 –0.2 –0.1 0 0.1 0.2 –0.2 –0.1 0 0.1 0.2 Time relative to HPC ripple (s) Time relative to HPC ripple (s) c Occipital cortex prestimulus d Occipital cortex versus poststimulus ripples Post < pre prestimulus ripples 160 4 Pre Post 160 0.3 OCC power relative to baseline Peri-ripple OCC power (t value) 140 140 2 0.2 120 120 Frequency (Hz) Frequency (Hz) 100 0 100 0.1 80 80 –2 0 60 60 40 40 –4 –0.1 –0.2 –0.1 0 0.1 0.2 Pre < post –0.2 –0.1 0 0.1 0.2 Time relative to HPC ripple (s) Time relative to HPC ripple (s) Fig. 4 | Peri-hippocampal ripple cortical activity. a,b, Cortical TF analyses time d, Further examination of this effect revealed that it was driven by a suppression locked to the hippocampal ripple peak time. Across all ripple trials, there was an of gamma power relative to baseline in trials with prestimulus hippocampal overall increase in high gamma power in the fusiform (a) and occipital cortex (b) ripples and compared to trials with no ripples (Extended Data Fig. 6). See time locked to hippocampal ripple. c, Comparing trials with prestimulus versus Extended Data Fig. 7 for replication using a different ripple detection method. poststimulus hippocampal ripples, there was a reduction in occipital gamma OCC, occipital cortex; HPC, hippocampus. power in trials with prestimulus ripples, compared to poststimulus ripples. context. To test this hypothesis, we examined whether the occur- This observation raised the question of what the impact of pres- rence of prestimulus hippocampal ripples modulated poststimulus timulus hippocampal ripples could be on cortical dynamics in the (0–1 s) cortical responses to surprise (for example, the increase in fusiform that would later modulate prediction error gamma response. gamma power in the fusiform). This hypothesis was confirmed in We therefore examined aperiodic exponents in the fusiform cortex the fusiform cortex. We found an early (250–550 ms poststimulus, (30–70 Hz, from −750ms to +250 ms around stimulus onset) as a meas- summed t = 936.9, P = 0.0078) increase in fusiform gamma power ure of cortical excitation/inhibition (E/I) balance60,61. This analysis (47.5–122.5 Hz) in response to higher surprise in trials that were revealed that the aperiodic exponent—and hence the underlying preceded by a prestimulus hippocampal ripple. By contrast, a later cortical excitability state—differs significantly based on the occur- (510–710 ms) increase in gamma power (50–72.5 Hz, summed t = 473.8, rence of a prestimulus hippocampal ripple (t(6) = 2.63, P = 0.039). P = 0.0078) was observed to higher surprise when there was no pres- Specifically, trials without prestimulus ripples show smaller exponents timulus ripple (Fig. 5d and Extended Data Fig. 10a,b). The presence of (mean = 2.485, s.d. = 0.405; that is, flatter spectral slopes), indicat- a faster fusiform response to surprise in trials with prestimulus rip- ing a shift toward excitation (E>>I), whereas trials with prestimulus ples could simply reflect a co-occurrence of the two. To rule out this ripples show steeper slopes (mean = 2.549, s.d. = 0.371), indicating a possibility, we examined whether there was a double dissociation of more balanced E/I state (Extended Data Fig. 10h). Critically, the most the surprise parameter estimate using a repeated measures analysis neurobiologically grounded accounts of precision encoding appeal of variance with ripple status (no ripple, prestimulus ripple) and time to fast-spiking inhibitory interneurons see ref. 16 for a particular focus window (early, later) as factors. This showed a significant interaction on the hippocampus). In this framework, the more balanced E/I state (F(1,6) = 19.6, P = 0.004, η2P = 0.766), confirming the double dissocia- following ripples reflects increased inhibitory control, which directly tion (Extended Data Fig. 10c) such that an early effect of surprise was implements higher precision weighting of prediction errors. Con- observed for trials with prestimulus ripples, but not for trials without versely, the E>>I state in trials without ripples reflects reduced inhibi- ripples; conversely, a later surprise effect was found in trials without tory control and correspondingly lower precision weighting. ripples, but not in trials with prestimulus ripples. This result suggests that the presence of a prestimulus hippocampal ripple modulates the Directed information flow fusiform response to surprise, such that it is faster—and of greater Finally, we examined information flow between the hippocampus and amplitude—compared to trials without a prestimulus ripple. The fusiform and occipital cortex by testing for Granger causality (GC) as presence of prestimulus hippocampal ripples did not modulate post­ a function of surprise and entropy. To do so, a median split for each stimulus occipital responses to surprise. information-theoretic measure was applied and a nonparametric Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 a Hippocampus b Fusiform c Fusiform 160 4 4 4 160 3 30 3 140 140 2 2 2 25 Frequency (Hz) 120 Frequency (Hz) Frequency (Hz) 120 1 1 20 t value 100 0 100 0 0 15 80 80 –1 –1 –2 –2 10 –2 60 60 –3 5 –3 40 40 –4 –4 –4 0 0.2 0.4 0.6 0.8 0 0.2 0.4 0.6 0.8 0 0.2 0.4 0.6 0.8 Time (s) Time (s) Time (s) 0.2 0.1 0.4 Parameter estimates Parameter estimates Parameter estimates 0.1 0 0.2 0 –0.1 0 –0.2 –0.2 –0.1 –0.3 –0.4 –0.2 –0.4 –0.6 Entropy Surprise Entropy Surprise Entropy Surprise d Hippocampus Fusiform e Fusiform to hippocampus 0.1 High surprise –5 Low surprise Surprise parameter estimates –6 0.05 –7 log(GC) 0 –8 –9 –0.05 –10 Prestimulus ripple No ripple –0.1 –11 0 0.2 0.4 0.6 0.8 1 20 40 60 80 Prestimulus Time (s) Frequency (Hz) Fig. 5 | Surprise-driven responses. a, Hippocampal increase in gamma power as low-frequency responses to surprise. d, The increase in fusiform gamma power surprise increases. Bottom, parameter estimates for each predictor within the of 40–95 Hz (extracted from significant cluster) occurs earlier in trials with significant cluster (n = 17). Each point pertains to one patient, horizontal bars prestimulus hippocampal ripples (red) compared to trials without ripples (blue). represent the group mean and vertical error bars represent 95% CI. b, Fusiform Lines represent the mean, and shaded areas represent the s.e.m. See Extended gamma power increases as surprise increases (n = 7). Each point pertains to Data Fig. 8 for replication using a different ripple detection method. e, Directed one patient, horizontal bars represent the group mean and vertical error bars information flow from fusiform to hippocampus for high (orange) versus low represent 95% CI. c, Fusiform beta oscillations decrease as surprise increases (green) surprise trials was observed in the gamma band (shaded rectangle). Lines (n = 7). Each point pertains to one patient, horizontal bars represent the group represent the mean, and shaded areas represent the s.e.m. mean and vertical error bars represent 95% CI. See Extended Data Fig. 9 for spectral GC analysis was performed. Guided by the TF effects above, Subsequent GC analyses showed no significant effects in the we focused on hippocampus–occipital cortex for entropy, and hip- reverse direction (hippocampus → fusiform; Extended Data Fig. 10f). pocampus–fusiform cortex for surprise, each centered in time around Thus, top-down effects are expressed as hippocampal ripple-dependent the observed TF effect (−1000 to −500 ms prestimulus for entropy, modulation of fusiform cortical dynamics ‘before’ surprise, whereas and 150 to 650 ms poststimulus for surprise). For surprise, we found bottom-up effects manifest as fusiform to hippocampal-directed con- increased bottom-up information flow (fusiform → hippocampus) in nectivity in the gamma range ‘after’ surprise. No GC effects between the gamma range (52–60 Hz; Fig. 5e and Extended Data Fig. 10a–c) for the hippocampus and occipital cortex as a function of entropy were high versus low surprise (summed t = 13.2, P = 0.0078). To ensure that significant in either direction (Extended Data Fig. 10d,e). The absence this effect was valid, we ran two further analyses—first, a GC analysis on of directed information flow alongside clear functional modulation the time-reversed data, showing an effect in the same frequency ranges (prestimulus ripple-associated reduction in occipital gamma activity) in the opposite direction, as expected, and, second, a partial directed suggests that hippocampal influence on uncertainty processing either coherence analysis that revealed a significant information flow effect operates through state preparation rather than continuous informa- from the fusiform to the hippocampus in high versus low surprise trials tion transfer or is mediated along the processing hierarchy to enable for the same frequency range of 52–59 Hz (cluster t(6) = 4, P < 0.001; such state preparation. As there were few participants with contacts in Extended Data Fig. 10g). both cortical regions (n = 4), GC between occipital and fusiform cortex Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Hippocampalripple Gamma activity GC High entropy (~1.6–1.9 bits) High surprise (>2 bits) Temporal association –1,000 –800 –600 –400 –200 0 200 400 600 800 1,000 Hippocampus Occipital cortex log10(power) Fusiform gyrus Frequency [Hz] Fig. 6 | Human hippocampal–cortical dialog in precision weighting of a more inhibitory state. We interpret these prestimulus effects to reflect the prediction errors. Peri-stimulus time is indicated in ms, and involvement setting of cortical dynamics to imbue precision to subsequently surprising of relevant brain areas (hippocampus in blue, occipital cortex in green and stimuli in the high entropy context. In line with these predictions, different fusiform cortex in red) at different time points is indicated by the inclusion of levels of entropy are not associated with any poststimulus changes in spectral that structure in the top row. Before stimulus presentation (left), unpredictable power. Surprising stimuli, however, lead to poststimulus increases in gamma visual stimulus streams modulate hippocampal gamma activity and ripple power in fusiform and hippocampus (right), with directed functional occurrence, with a change in dynamics in visual cortical areas temporally connectivity following fusiform to hippocampus in this gamma range. The associated with hippocampal ripples. These effects are observed before critical observation is that a prestimulus hippocampal ripple is associated stimulus presentation. From ~1,000 ms before stimulus presentation, there is with a change in excitatory–inhibitory balance in the fusiform cortex (left), a reduction in hippocampal gamma activity, coinciding with a peak in ripple and a subsequent poststimulus gamma response in the fusiform to surprising rate at ~800 ms prestimulus. By contrast, occipital cortex shows an increase stimuli (right) that is greater in amplitude and of shorter latency (~200 ms). No in gamma power ~500 ms prestimulus, but this increase is suppressed if the poststimulus responses to surprising stimuli are observed in occipital cortex, hippocampus generates a ripple. Spectral power in the fusiform cortex is not in keeping with an established role of higher-order visual cortices (that is, modulated by entropy prestimulus. However, the occurrence of a prestimulus fusiform) in generating prediction error neuronal responses. For illustrative hippocampal ripple (which is most frequent under high entropy) shifts the purposes, gamma activity is shown as a continuous oscillation. excitatory–inhibitory balance of the prestimulus fusiform field potential to was not examined. All hippocampal–cortical interactions reported The properties and distribution of ripples as a function of above are summarized schematically for high entropy (prestimulus peri-stimulus time and entropy strongly suggest that they have an time periods) and high surprise (poststimulus time periods) in Fig. 6. important role in generating a certain kind of prediction; namely, a prediction of predictability. Previous work in rodents has demon- Discussion strated that ripples represent future trajectories of experienced and We measured hippocampal and cortical activity using depth electrodes new paths41–43, indicative of a generative process. Our results provide in patients with epilepsy as they observed sequences of simple visual support for, and extend, these previous studies by showing increased stimuli drawn from a block-specific distribution, yielding varying lev- ripple probability in uncertain contexts before stimulus onset, in awake els of stimulus-bound uncertainty and surprise. Electrophysiological human patients, as they anticipate upcoming sensory input. Specifi- intracranial activity was examined prestimulus and poststimulus to cally, predictive processing under uncertainty may represent an inte- assess prediction under different levels of uncertainty and subsequent gral part of the proposed role for ripples in planning future actions40,41. validation against observed inputs, respectively. Our results highlight Our findings are in keeping with a recent synthesis of rodent studies an important hippocampal function in generating putative predictions suggesting that ripple trajectories comprise not only the path to be cho- and communicating them downstream to the cortex. Specifically, our sen or the path just completed, but also many of the potential options findings suggest that hippocampal ripples are key to this generative available62. With an increasing number of options—and implicit unpre- process—ripple probability was highest in the prestimulus window dictability of ensuing outcomes—the ripple rate would be expected to when uncertainty was high but not at its maximum, and prestimulus increase, as observed here. This is also in line with recent fMRI work in ripple duration increased with entropy. These prestimulus ripples humans showing successor-like representations in the hippocampus were associated with subsequent faster responses to poststimulus and visual cortex29. Notably, our visuomotor mapping task did not surprise in the fusiform cortex. This is exactly consistent with a role impose memory demands, as both stimulus and response mapping of hippocampal ripples reporting the predictability of an upcoming appeared on the screen simultaneously. The key manipulation was stimulus and thereby affording the ensuing prediction errors greater the underlying probability of the stimulus distribution within block, precision, in the context of greater expected information gain (that which we suggest elicited an increased need for precise predictive is, higher entropy). processing, and thus more ripple events, with increased uncertainty Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 regarding the upcoming stimulus. This allowed us to elucidate an processing35,36, so that the faster response to surprise in the presence important functional role that ripples have in anticipation of sensory of prestimulus ripples indicates that a prior prediction facilitates inputs and associated action planning48. signaling of prediction errors31, and their propagation of the visual The increased frequency of prestimulus ripples occurred around processing hierarchy. Further support for this claim arises from the the same time as the negative association between entropy and gamma E/I analyses, which provided direct neurobiological evidence for our power (overlapping in 80–97.5 Hz), namely, around 800 ms prestimu- precision-weighting hypothesis—prestimulus hippocampal ripples lus. The early hippocampal gamma effect (that is, negative association enhance the precision afforded to subsequent prediction errors with entropy) is—on a predictive coding narrative—consistent with an through enhanced inhibitory control mechanisms, resulting in faster, attenuation of neuronal fluctuations in populations reporting predic- stronger and more controlled cortical responses to surprising stimuli. tion errors (for example, superficial pyramidal cells) in proportion to Furthermore, we also observed increased fusiform to hippo­ the predicted precision of subsequent (stimulus-bound) prediction campus information flow in high surprise as reflected in GC for errors11. Such an early effect is consistent with (Bayesian belief) an gamma activity from 150 to 650 poststimulus, which we associate updating of information from the previous trial and broadcasting the with bottom-up prediction error signaling. This is compatible with a ensuing predictions down the cortical hierarchy. This finding also view that prestimulus hippocampal ripples modulate (or prime) the suggests that detected ripples were likely discrete events, dissociable fusiform to respond at earlier latencies to surprising stimuli (top-down from gamma activity37,63. The observed reduction in gamma activity effect), and the fusiform poststimulus gamma response to surprise (spanning frequencies lower than that of the ripple activity) occurred feeds back to the hippocampus (bottom-up effect). The precise nature in the prestimulus period on trials in which prestimulus ripples were of hippocampal ripple-triggered changes in cortical state remains to be evident (Extended Data Fig. 4). This co-occurrence might be driven by determined (for example, cortical layer-specific engagement of inhibi- the opposing effects of acetylcholine on ripple generation and gamma tory interneurons), although the simplicity of the current behavioral power (increase in ripple rate accompanied by decreased gamma-band task lends itself to further mechanistic interrogation using nonhuman activity, and vice versa37). This is interesting because acetylcholine has animal models, where homologous predictive coding mechanisms been implicated in the encoding of precision in predictive processing are evident66. in several computational studies9,58,64. Finally, we also observed poststimulus hippocampal ripples, but To demonstrate that ripples carry predictive information that is did not, however, find a relationship between these and behavioral subsequently propagated to the cortex, it is important to establish measures or cortical activity. Different cortical effects linked to pres- peri-ripple modulation of behavior or cortical activity, as suggested timulus versus poststimulus hippocampal ripples could imply that by predictive processing5,16. There are mixed findings in the literature, these ripples arise in different neuronal populations whose outputs are with some studies showing no ripple occurrence-related modulation routed differently. Therefore, it is possible that poststimulus ripples of subsequent behavior in rodents48–50. Nevertheless, hippocampal serve a different functional role38,67, or facilitate communication with ripples modulated cortical activity in several ways; in line with previ- other brain regions, such as the prefrontal cortex45,46. ous findings from rodents and nonhuman primates45–47, we found an In conclusion, our findings speak to an important function of hip- overall increase in high gamma power in fusiform and occipital cortex pocampal ripples in predictive processing, reporting the predictability around the ripple time, supporting the notion that the occurrence of or expected information gain of stimuli (that is, prediction errors) ripples facilitates enhanced interaction between the hippocampus and before they are encountered, thereby facilitating their propagation cortex. These findings demonstrate that ripples implement general through the visual cortex (in the absence of any memory demands). precision-weighting mechanisms that modulate cortical readiness More specifically, our results reveal an increase in ripple events in across different computational contexts, rather than being tied to uncertain trials, before stimulus onset, and subsequent modulation any single variable. of cortical activity, pointing to enhanced hippocampal–cortical com- In addition to the cortical modulation across all ripple trials, a munication facilitated by ripples that serve the propagation of precise unique modulation of prestimulus ripples on cortical processing was prediction errors. These findings reveal complementary mechanisms observed, in keeping with their predictive role. First, we found a sup- through which hippocampal ripples achieve their modulatory effects— pression of occipital gamma activity that was time locked to the hip- preparing cortical states for upcoming uncertain stimuli (entropy pocampal ripple event. Together with the overall positive association pathway) and enhancing the precision of prediction–error responses between entropy and occipital gamma power prestimulus, this sug- when surprising events occur (surprise pathway). gests that the ripple-triggered suppression may reflect an overriding hippocampal signal over lower-level cortical prediction of the upcom- Online content ing stimulus, or facilitate a sharper representation of the possible Any methods, additional references, Nature Portfolio reporting sum- outcomes in occipital cortex14,65. 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Ripples reflect a spectrum of synchronous spiking activity in human anterior temporal lobe. eLife 10, e68401 (2021). © The Author(s) 2026 Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Methods were then applied to the brain mask from SPM8 to transform the brain Participants mask into the native space of the pre-MRI. All voxels in the pre-MRI Eighteen patients with medication-resistant presurgical epilepsy (mean that were outside the brain mask and with a signal value in the top age = 37.3 years, s.d. = 11.7; eight males) with depth electrodes surgically 15% were filtered out. The skull-stripped pre-MRI was then coregis- implanted to aid seizure focus localization took part in the experiment. tered and resliced to the skull-stripped post-CT. Next, the pre-MRI Data were acquired at two epilepsy centers, one in Madrid and one in was affine registered to the post-CT, thus transforming the pre-MRI Zurich. All patients signed informed consent before participating in the image into a native post-CT space. The two images were then overlaid, experiment. Implantation sites were chosen solely on the basis of clini- with the post-CT thresholded such that only electrode contacts were cal criteria. Patients had normal or corrected-to-normal vision and had visible. For all patients, only contacts in the hippocampus head and no history of head trauma or encephalitis. All patients had electrodes body were selected. For patients with multiple electrodes localized implanted in the hippocampus. For patients showing unilateral hip- in the hippocampus, only the most anterior contacts were used in the pocampal sclerosis, only the nonpathological side was included in the analyses (Fig. 1c). Electrode contacts for each patient are shown in analysis; in all other patients, hippocampi were radiologically normal Supplementary Fig. 1. on preoperative MRI. Of the 18 patients who completed the task, 1 patient was excluded Electrode localization: cortical regions from any analysis due to poor performance on the task (less than 25% To localize electrodes with contacts in the occipital and fusiform cor- accuracy on all trials). We therefore analyzed electrophysiological tices, we used a semiautomatic procedure with Lead-DBS69 (https:// responses from 17 patients. In total, we analyzed contacts in the www.lead-dbs.org/). First, the postoperative CT was registered to the right hippocampus from seven patients, and eight patients with preoperative MRI using a two-stage linear registration (rigid followed contacts on the left side. Of these 17 patients, 8 patients also had by affine) as implemented in Advanced Normalization Tools70. The electrodes in the occipital cortex, and 7 patients also had contacts images were then normalized to the MNI template based on the pre-MRI in the fusiform. Our sample size was not determined before the using the SyN registration approach as implemented in Advanced experiment, but it is equal or larger than that reported in previ- Normalization Tools. To reduce bias introduced by brain shift, the ous publications using cognitive tasks with intracranial electroen- brain shift correction implemented in Lead-DBS was performed on cephalography (iEEG)51,68. This study has been approved by the local the postoperative CT. Manual prereconstruction was then performed, ethics committees of Hospital Ruber Internacional and Kantonale in which the tip of each electrode and another point along its trajec- Ethikkommission (PB-2016-02055). tory were manually marked, followed by automatic reconstruction guided by the electrode specification (that is, the number of contacts Stereotactic electrode implantation and their spacing). The reconstructed electrodes were then visually A contrast-enhanced MRI was performed pre-operatively under stereo- inspected and refined, and the processes were iterated in case of any tactic conditions to map vascular structures before electrode implanta- misalignments. Once all electrodes (for any given patient) have been tion, and to calculate stereotactic coordinates for trajectories using the reconstructed, they were visualized using Lead-Group71. After this pro- Neuroplan system (Integra Radionics). For patients whose data were cess, each electrode contact was associated with MNI coordinates. To collected in Madrid (n = 12), DIXI Medical Microdeep depth electrodes identify contacts in our cortical regions of interest, a custom MATLAB (multicontact, semirigid, diameter of 0.8 mm, contact length of 2 mm, code, together with the FindStructure function (https://alivelearn. intercontact isolator length of 1.5 mm) were implanted based on the net/?p=1456), was used. Each MNI coordinate was associated with a stereotactic Leksell method. For patients whose data were collected label from the Automated Anatomical Labeling atlas. For occipital cor- in Zurich (n = 5), the depth electrodes (diameter of 1.3 mm, eight con- tex, MNI coordinates labeled as ‘inferior occipital’ or ‘middle occipital’ tacts of 1.6 mm length and spacing between contact centers of 5 mm; were used; for fusiform, the ‘fusiform’ label was selected. The selected Ad-Tech, www.adtechmedical.com) were stereotactically implanted contacts were then visually inspected with the Automated Anatomi- in the medial temporal lobes. cal Labeling overlay in MRIcron, as well as in native space (Fig. 1d and Supplementary Fig. 1). Data acquisition In Madrid, iEEG activity was acquired using an XLTEK EMU128FS ampli- Behavioral task fier (XLTEK). iEEG data were recorded at each electrode contact site at Patients performed a visuomotor mapping task, consisting of 12 blocks a 500 Hz sampling rate (online bandpass filter of 0.1–150 Hz) and ref- with 40 trials per block, using the same design as discussed in ref. 20. erenced to linked mastoid electrodes. For four patients, the data were Each trial included a brief presentation of a colored shape, for 500 ms, recorded with a higher sampling rate, but were later downsampled to with an interstimulus interval of 2200 ms. In all trials within a block, two 500 Hz. In Zurich, data were acquired using a Neuralynx ATLAS system colors and two shapes were combined to form four possible outcomes, with a sampling rate of 4000 Hz (online bandpass filter of 0.5–1000 Hz) with different stimuli presented in the different blocks. Patients were against a common intracranial reference, and then downsampled to asked to respond to the sampled item by pressing a key to identify the 500 Hz. Data from the two centers were comparable, and we did not target’s position in the row (Fig. 1a). Each trial used an independent observe differences across centers in any of the analyses reported in sample from a distribution that remained constant within a block, the Results section. but that varied over blocks. There was no underlying sequence gov- erning stimulus presentation; only the relative proportions of stimuli Electrode localization: hippocampus were varied from block to block. Two information theoretic measures, To localize electrodes with contacts in the hippocampus, we used the entropy and surprise, were then calculated (Fig. 1b). Surprise quantifies manual procedure described previously68. For each patient, the post- the improbability of a given event: electrode placement CT (post-CT) was coregistered to the preoperative T1-weighted MRI (pre-MRI). To optimize coregistration, both brain I (xi ) = −ln P (xi ) images were first skull stripped. For CTs, this was done by filtering out all voxels with signal intensities between 100 and 1,300 HU. Skull strip- Entropy quantifies the expected (running average of) surprise ping of the pre-MRI proceeded by first spatially normalizing the image overall the trials: to MNI space using the New Segment algorithm in SPM8 (http://www. fil.ion.ucl.ac.uk/spm). The resultant inverse normalization parameters H(X) = ∑ −P (xi ) ln P (xi ) Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Patients were told the proportion of colored shapes presented a Kolmogorov–Smirnov test. To identify the bins with the highest within block was random, and independent of the other blocks in the ripple counts, we also performed a mixed-effects gamma regression task. To account for nonspecific time effects within block, the mean on the normalized ripple counts (as they follow a right-skewed dis- entropy overall blocks was calculated for each patient. Trial number tribution) with entropy and ripple peak time bin as predictors. This is therefore accounted for by the inclusion of mean entropy values was done using the lme4 package77 in R (https://www.r-project.org/), that are the same across blocks. Therefore, for each trial, three val- and the following model setup: normalised_count ~ entropy × peak_ ues of interest were computed—entropy, surprise and mean entropy. time + (1|subI), family = Gamma. After the significant interaction, the These values were modeled from the perspective of an ideal Bayesian top five bins with the highest estimated marginal means were identi- observer, using the Dirichlet distribution20. Only correct responses fied using the pairwise function from the emmeans package (https:// given within 1 s of stimulus onset were used for subsequent analyses. rvlenth.github.io/emmeans/). Finally, TFCE was calculated on the normalized 2D histogram, with a t test against the average ripple rate Electrophysiological data analysis per patient. Therefore, a positive value indicates a higher-than-average Preprocessing. iEEG data analysis was carried out using the FieldTrip ripple count observed. toolbox72 (https://www.fieldtriptoolbox.org; v20200310) running Finally, to test whether the increased ripple frequency was related on MATLAB R2019b (MathWorks). For all patients and regions of to entropy, over the effect of surprise, we used a standard summary interest, recordings were transformed to a bipolar derivation by sub- statistic approach to mixed-effects modeling, using a separate Poisson tracting signal from adjacent electrode contacts within the region GLM for each participant, for each time bin, that included a constant of interest (hippocampus, occipital cortex and fusiform). Previous term, modeling the ripple rate for that participant and time bin. The studies demonstrate that bipolar referencing optimizes estimates of predictors in this model were entropy and surprise, as well as mean local activity73,74 and connectivity patterns across brain regions75, as entropy as covariate of no interest (as was done in all other TF analyses well as for analysis of SWRs52. Data were epoched from −1 to 1 s with described below). The ensuing parameter estimates were then subject respect to stimulus onset (a time window selected a priori), demeaned to a second-level analysis using cluster-based permutation tests as and detrended. For each region of interest, every epoch was visually implemented in FieldTrip (using the TFCE method did not change inspected for artifacts caused by epileptic spikes or electrical noise, the results). first in the time domain and then in the TF domain. Trials with artifacts Next, we examined ripple duration, calculated as ripple end point were excluded from all subsequent analyses. Of note, for analyses minus ripple start point (both determined in the detection algorithm involving more than one region, only trials that were artifact free in as two z scores above average) as a function of peri-stimulus time all regions were used. and entropy and surprise. This was done using generalized linear mixed-effects models implemented by the lme4 package77 in R (https:// SWR analyses www.r-project.org/). Because ripple duration follows a right-skewed Ripples were detected using a previously established method52 distribution, we used a model from the gamma family with a random (Supplementary Fig. 2a). After trial-wise artifact rejection and basic intercept for patient using the following syntax: preprocessing (described above), the iEEG signal was bandpass fil- model ← glmer(rip_duration ~ entropy × peri_stim_time + sur- tered between 80 and 120 Hz using a second-order Butterworth filter. prise × peri_stim_time + mean_entropy + (1 | patient), family = Gamma). A Hilbert transform was then applied to the filtered signal to extract A similar approach using mixed-effects linear regression was used its instantaneous amplitude. Ripples were identified as events with a to examine RT, which was normalized using log transformation. This maximum amplitude three s.d. above the mean, and with a duration approach accommodates any individual differences among partici- of at least 25 ms. Ripples that were detected within 15 ms of each other pants (for example, the overall ripple rate for each participant). were merged. The start, peak and end time of each ripple were noted. To ensure that artifacts or interictal epileptiform discharges (IEDs; TF analyses Extended Data Fig. 3) were not mistakenly classified as ripples, we used Time-resolved spectral decomposition was computed for each trial an automated procedure as described in ref. 52 to identify and reject using seven Slepian multitapers for high frequencies ( ≥35 Hz) and a IEDs. The iEEG signal was high-pass filtered at 200 Hz and a z score was single Hann taper for low frequencies (<3 5Hz). The selected Slepian calculated based on the gradient and amplitude of the filtered signal. tapers for the analysis of high frequencies were based on windows with Any time-point exceeding a z score of 5 was marked as an IED, together width of 0.4 s and a 10 Hz frequency smoothing. The time-resolved with the 100 ms before and after the event. All IEDs were excluded spectral estimation was done in steps of 2.5 Hz. No baseline correction from the analysis, increasing the likelihood of the detected ripples was performed, given our interest in both prestimulus and poststimu- being physiological52. We have also applied another ripple detection lus activities. Trial-wise TF estimates were then entered into a GLM; method51 and replicated our findings (Extended Data Figs. 1, 7 and 8). two predictors of interest (entropy and surprise) and a covariate (mean For patients who had more than one anterior hippocampal electrode entropy) were used to predict power at each TF point. The parameter (for example, head and body), we examined whether the same ripple estimates for entropy, mean entropy and surprise are proportional to was picked up by the different electrodes (within 20 ms). In line with the range these values can assume. This resulted in a ‘first-level’ beta previous findings in rodents76, we found the vast majority of ripples map (with size time × frequency) per predictor. The spectral activity (96% on average) were not detected in the adjacent electrode along was then averaged over bipolar channels, within each region, for each the hippocampal long axis. patient (in some cases, there was only one bipolar channel in each brain We then examined whether ripple occurrence was influenced by region). These beta maps were then used for statistical inference with peri-stimulus time and information-theoretic measures. To do so, a 2D a cluster-based permutation test with the maximum number of per- histogram of ripple peak count using peri-stimulus time and entropy or mutations allowed by our sample size, up to a maximum of 5,000 surprise was derived for each patient. We normalized each histogram by permutations. In each permutation step, clusters were formed by the total number of trials per entropy/surprise bin to account for poten- temporal and frequency adjacency using a cluster-forming threshold tial imbalances. This was done by calculating the 2D count histogram of P = 0.05. In each permutation step, a one-sample two-tailed t test (time and entropy or surprise) and by multiplying by the frequency of against a value of 0 (equivalent to H0— β = 0), using a threshold of observed ripples given available trials in each entropy or surprise bin P = 0.025, was calculated for each TF estimate, separately for low (2.5– (that is, postcleaning). The normalized histograms were then averaged 32.5 Hz) and high (35–160 Hz) frequencies, as well as prestimulus (−1 across participants and tested against a uniform distribution using to 0 s) and poststimulus (0 to 1 s) time windows. Gamma frequency Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 band is used to refer to frequencies of 30–100 Hz, whereas high gamma Reporting summary refers to effects above 100 Hz. The precise frequency ranges (and Further information on research design is available in the Nature associated time windows) that we report for each significant effect Portfolio Reporting Summary linked to this article. are derived from a cluster-based permutation correction for multiple comparisons. Data availability Preprocessed data needed to generate the figures, and over which Ripple-modulated cortical activity statistics were computed, are available in the following GitHub reposi- To examine the temporal relationship between hippocampal ripples tory: https://github.com/frdarya/GenerativeRipples. and cortical responses, we extracted TF estimates in occipital cor- tex and fusiform with respect to peri-ripple peak time (−0.2 to 0.2 s, Code availability selected a priori), as described above for peri-stimulus responses, Analysis codes are available in the following GitHub repository: https:// for high and low frequencies. Due to the shorter time window, the github.com/frdarya/GenerativeRipples. selected Slepian tapers for the analysis of high frequencies were based on windows with width of 0.2 s and a 10 Hz frequency smoothing. The References time-resolved spectral estimation was done in steps of 5 Hz. These TF 68. Méndez-Bértolo, C. et al. A fast pathway for fear in human estimates were baseline corrected by calculating the relative change amygdala. Nat. Neurosci. 19, 1041–1049 (2016). with respect to a baseline period of −1.4 to −1 s peri-stimulus. Averaged 69. Horn, A. & Kühn, A. Lead-DBS: A toolbox for deep brain stimulation TF estimates across patients were used for statistical inference using electrode localizations and visualizations. NeuroImage 107, 127–135 cluster-based permutations, as described above. In each permutation (2014). step, a one-sample two-tailed paired t test was performed between 70. Avants, B., Tustison, N. J. & Song, G. Advanced normalization tools prestimulus and poststimulus periods (threshold of P = 0.025). Next, (ANTS). Insight J. 2, 1–35 (2009). to examine whether prestimulus hippocampal ripples modulated 71. Treu, S. et al. Deep Brain Stimulation: Imaging on a group level. poststimulus cortical responses to surprise, we used the same GLM NeuroImage 219, 3086 (2020). approach as described above, fitting a trial-wise regression at each 72. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J. M. FieldTrip: TF point with entropy, surprise and mean entropy as predictors open source software for advanced analysis of MEG, EEG, and (TF analyses). invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011). E/I balance analysis 73. Hamamé, C. M. et al. Functional selectivity in the human E/I balance analysis of activity in fusiform cortex was performed using occipitotemporal cortex during natural vision: evidence from the ‘fitting oscillations and one over f’ (FOOOF)61 method as imple- combined intracranial EEG and eye-tracking. Neuroimage 95, mented in FieldTrip. To achieve this, we extracted the aperiodic expo- 276–286 (2014). nent value for trials with or without prestimulus hippocampal ripples. 74. Shirhatti, V., Borthakur, A. & Ray, S. Effect of reference scheme on The time series was first epoched from −750 to +250 ms around stimulus power and phase of the local field potential. Neural Comput. 28, onset, as this reflects the majority of hippocampal ripples detected 882–913 (2016). (Fig. 2c), and around the onset of the fusiform gamma response to 75. Trongnetrpunya, A. et al. Assessing Granger causality in electro­ surprise (Fig. 5b). The data were then bandpass filtered between 30 and physiological data: removing the adverse effects of common 70 Hz, filtering out the 50 Hz line noise. Fractal spectral estimation was signals via bipolar derivations. Front. Syst. Neurosci. 9, 189 (2016). performed with a spectral smoothing of 2 Hz. This resulted in a single 76. Patel, J., Schomburg, E. W., Berényi, A., Fujisawa, S. & Buzsáki, G. exponent value for trials with or without prestimulus ripples, for each Local generation and propagation of ripples along the septotemporal patient. At the group level, a paired two-tailed t test was performed to axis of the hippocampus. J. Neurosci. 33, 17029–17041 (2013). compare the exponent values, with smaller exponents reflecting flatter 77. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear spectral slopes, indicating a shift toward excitation (E>>I). mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015). GC analyses Acknowledgements Finally, to evaluate the direction of information flow between the hip- The authors thank the patients who participated in the study, the pocampus and our cortical regions of interest, we calculated spectral electroencephalography technicians at Hospital Ruber Internacional nonparametric GC as a measure of directed functional connectivity and the Swiss Epilepsy Center in Zurich, and members of the using the FieldTrip toolbox, on 500 ms time windows centered on the Laboratory for Clinical Neuroscience for helpful discussions. induced responses observed to surprise in the fusiform gyrus and hippocampus, and the induced responses to entropy in the occipital Author contributions cortex and hippocampus. For patients with multiple bipolar chan- B.A.S. and K.J.F. designed the experiment. B.A.S., J.S. and R.T. collected nels in any of the regions, the most medial and anterior channel was the data. R.T., L.I., L.S. and A.G.-N. monitored patients and performed used to compute GC. Briefly, time-resolved spectral decomposition clinical evaluation. N.L. and A.H. developed software to perform was computed using nine Slepian multitapers (frequency range of electrode localization. D.F., S.M., R.H. and B.A.S. performed analyses. 2–80 Hz and 10 Hz smoothing). The spectral transfer matrix was then D.F., K.J.F. and B.A.S. wrote the paper with input from all other authors. obtained from the Fourier transformation of the data and together with the noise covariance matrix, they were used to calculate the Funding total and intrinsic power through which GC is computed. The result- This project was supported by the European Research Council (ERC) ing GC values were then log-transformed to normalize the data for under the European Union’s Horizon 2020 research and innovation statistical inference. We then compared information in high versus program (ERC-2018-COG 819814 to B.A.S.). D.F. is supported by low entropy and surprise (using a median split) in each direction (hip- a Royal Society University Research Fellowship (URF/R1/241499). pocampus → cortex and cortex → hippocampus) with nonparametric K.J.F. is supported by funding for the Wellcome Centre for Human cluster-based permutations (using a dependent samples of two-tailed Neuroimaging (205103/Z/16/Z), a Canada-UK Artificial Intelligence t test), using the maximum number of permutations available for Initiative (ES/T01279X/1) and the European Union’s Horizon 2020 each pair of regions. Framework Programme for Research and Innovation under the Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Specific Grant Agreement 945539 (Human Brain Project SGA3). This Supplementary information The online version research was supported in part by the Wellcome Trust (205103/Z/16/Z) contains supplementary material available at and the Swiss National Science Foundation (supported by SNSF https://doi.org/10.1038/s41593-026-02345-6. 204651). For the purpose of Open Access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript Correspondence and requests for materials should be addressed to version arising from this submission. Darya Frank or Bryan A. Strange. Competing interests Peer review information Nature Neuroscience thanks the anonymous All authors declare no competing interests. reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Additional information Extended data is available for this paper at Reprints and permissions information is available at https://doi.org/10.1038/s41593-026-02345-6. www.nature.com/reprints. Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 1 | Hippocampal ripples detected using an alternative counts for each time and entropy bin are plotted as bars above and to the right of algorithm. a, An example ripple detected using the method discussed in the 2D distribution, respectively. The majority of ripples occurred prestimulus ref. 51 showing the raw LFP trace (top), the ripple band signal (middle) and the and in high entropy levels. d, Ripple duration as a function of peri-stimulus time standardized envelope (bottom). b, Grand average raw field potential centered and information-theoretic measures. Ripple duration increased with entropy on ripple peak (left) and peri-ripple wavelet spectrogram (n = 4653 ripple events (that is, expected information gain) for prestimulus ripples, but decreased for from 17 participants). c, Normalized ripple distribution across peri-stimulus time poststimulus ripples (χ2(1) = 4.54, p = 0.032). Lines represent the mean, and and entropy levels, accounting for the total number of trials in each entropy bin. shaded areas represent the 95% confidence intervals. *p < 0.05. Normalized ripple count for each time and entropy bin is color-coded; average Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 2 | Estimated marginal means of the 2D ripple count as a function of entropy and time bins. The top 5 estimated marginal means (emmean) are marked in red, error bars represent upper and lower 95% confidence interval of the estimated marginal mean of the model (N = 17 patients). Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 3 | See next page for caption. Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 3 | Hippocampal ripples. a, An example of ripple detection, low surprise (1–1.5 bits) at later time bins. This designation of 1–1.5 bits as ‘low showing the filtered ripple band and Hilbert amplitude thresholds. Gray shading surprise’ is mathematically determined by our task design (maximum surprise indicates ripple duration. b, An example interictal epileptiform discharge ~5 bits, minimum approaching 0). This range of surprise values corresponds, on (IED). c, Ripple frequency as a function of time bin and peri-stimulus time; a average, to a value of 1.67 bits of entropy. This plot is, therefore, consistent with main effect of peri-stimulus time (F(1,16) = 6.02, p = 0.026), with more ripples the increase in ripple count in the prestimulus period for upcoming entropy observed prestimulus. Error bars represent standard error of the mean (N = 17 values around 1.6 to 1.9 bits. Thus, when we plot ripple occurrence as a function patients). d, Normalized distribution of ripples as a function of peri-stimulus of surprise, we observe no increase in ripple count for high surprise trials in the time and level of stimulus-bound surprise. e, Cluster-based statistical test on the poststimulus period. Instead, ripples cluster precisely following low-surprise normalized ripple count in the 2D entropy-time histogram using TFCE revealed trials, very late in the trial (which would correspond to prestimulus bins of the an extended cluster (−800 ms to +800 ms) of increased ripple count for high next trial). g, Distribution of surprise values in the task (error bars depict s.e.m.; levels of entropy, with peaks around −800 to −600 ms prestimulus, −200 to 0 N = 17 patients). h, Speed (1/RT) as a function of prestimulus ripple status and and 0 to 200 ms poststimulus. Color bar indicates t-value. f, Poststimulus ripple entropy. For trials with a prestimulus ripple there was a faster response, more rate as a function of surprise (normalized by the number of trials within each pronounced (at a trend level) for entropy levels around 1.6 to 1.8 bits. Error bars surprise bin). If entropy-associated prestimulus ripples were instead reflecting represent the standard error of the mean. i, Ripple rate as a function of trial in surprise-associated postprocessing of the previous stimulus, an increase in block (N = 17 patients), error bars represented standard error of the mean. ripple count should be observed for high surprise later in the trial. When locked j, Ripple duration as a function peri-stimulus time and information-theoretic to stimulus onset and going forward in time (up to 2.2 s poststimulus onset, measures. Entropy by ripple peak time interaction (χ2(1) = 0.66 p = 0.41) and which corresponds to the time between presentation of successive stimuli), such surprise by ripple peak time interaction (χ2(1) = 0.17, p = 0.678). Lines represent an increase was not observed, that is, the ripple rate in the top right quadrant of the mean and shaded area the 95% CI. the plot shows a low ripple rate. Instead, there is an increase in ripple count for Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 4 | Prestimulus hippocampal response to entropy split between hippocampal gamma and entropy in trials with a prestimulus ripple by whether a prestimulus ripple was present. Examining the hippocampal (around the same time and frequencies as reported in Fig. 3a). Cluster t = −488.1, response to entropy on trials in which a prestimulus ripple was present, p = 0.0248 95% CI = 0.0043. compared to trials without a prestimulus ripple, we found a negative association Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 5 | Entropy-related responses. a, Log-transformed poststimulus. c, Log-transformed gamma power in occipital cortex prestimulus gamma power in hippocampal prestimulus entropy cluster (shown in Fig. 3a), entropy cluster (shown in Fig. 3b), as a function of trial in block. Shaded areas as a function of trial in block. Shaded areas represent standard error of the represent standard error of the mean. d,e, Occipital (d) and fusiform (e) cortex mean. b, Hippocampal lower frequencies entropy responses, prestimulus and lower frequencies entropy responses, prestimulus and poststimulus. Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 6 | Peri-ripple cortical modulation. a, Mean of log power bars the group mean, and vertical error bars represent 95% confidence interval. in occipital cortex cluster (N = 8) positively associated with entropy in the Occipital gamma power was suppressed when there was a hippocampal ripple prestimulus time window, split by whether there was a hippocampal ripple just compared to when there was not. b, T-statistics of peri-ripple activity in occipital before the significant effect or if there was not, two-tailed paired t-test (t(7) = 2,51, cortex for poststimulus ripples. c,d, Peri-ripple activity in the fusiform for (c) p = 0.0401, CI = (0.011, 0.377)); each point pertains to one patient, horizontal prestimulus and (d) poststimulus ripples. *p < 0.05. Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 7 | Peri-ripple cortical modulation using an alternative cluster positively associated with entropy in the prestimulus time window, split detection algorithm. a,b, Cortical time-frequency analyses time-locked to the according to whether there was a hippocampal ripple just before the significant hippocampal ripple peak time, detected using the algorithm discussed in ref. 51. effect or if there was not (N = 8), each point pertains to one patient, horizontal Across all ripple trials there was an overall increase in high gamma power in bars the group mean, and vertical error bars represent 95% confidence interval. the fusiform (a) and occipital cortex (b) time-locked to hippocampal ripple. Occipital gamma power was suppressed when there was a hippocampal ripple c, Comparing trials with prestimulus versus poststimulus hippocampal ripples, compared to when there was not; dependent t-test t(7) = 2.54, p = 0.038, Cohen’s there was a reduction in occipital gamma power in trials with prestimulus ripples, d = 0.9. *p < 0.05 (two-tailed). compared to poststimulus ripples. d, Mean of log power in occipital cortex Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 8 | Hippocampal prestimulus ripples modulate fusiform (extracted from significant cluster in trials with prestimulus hippocampal ripples response to surprise using ripples detected with an alternative algorithm. (red) compared to trials without ripples (blue). Ripples detected using the a,b, Fusiform poststimulus association with surprise in trials with (a) prestimulus algorithm described in ref. 51. Lines represent the mean, and shaded areas the ripple compared to (b) without prestimulus ripples (no significant response standard error of the mean. to surprise observed). c, The increase in fusiform gamma power 40–95 Hz Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 9 | Surprise responses. a, Hippocampal low frequency response to surprise. b,c, Occipital cortex (b) low and (c) high frequency responses to surprise. Nature Neuroscience Article https://doi.org/10.1038/s41593-026-02345-6 Extended Data Fig. 10 | Fusiform poststimulus response to surprise is trials were significantly higher in the gamma band (shaded rectangle, p < 0.001). modulated by prestimulus hippocampal ripples and directed cortical- This is analogous to the results presented in Fig. 5e, using a Granger causal hippocampal information flow. a,b, Fusiform poststimulus association (a) with analysis. PDC was computed using the FieldTrip toolbox in Matlab. The median- surprise in trials with prestimulus ripple earlier than (b) without prestimulus split data were downsampled to 250 Hz, and PDC was calculated for frequencies ripples. c, Double-dissociation of fusiform gamma response to surprise (early ranging between 0 and 80 Hz with a smoothing of 10 Hz. Shaded areas represent and late clusters) in the presence and absence of a prestimulus hippocampal the standard error of the mean. param. est.: parameter estimate of fusiform ripple (n = 7). Error bars represent the standard error of the mean, p = 0.004. response to surprise. h, Trials without prestimulus ripples show smaller d,e, Granger causality (GC) analysis between the hippocampus and occipital exponents (that is, flatter spectral slopes), indicating a shift toward excitation cortex for entropy. f, Granger causality from the hippocampus to fusiform (E>>I), whereas trials with prestimulus ripples show steeper slopes, indicating for surprise. Shaded areas represent the standard error of the mean. g, Partial a more balanced E/I state. Error bars represent the standard error of the mean directed coherence (PDC) values for high (orange) versus low (green) surprise (N = 7 patients). Nature Neuroscience nature portfolio | reporting summary Darya Frank Corresponding author(s): Bryan Strange Last updated by author(s): 30/04/2026 Reporting Summary Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our Editorial Policies and the Editorial Policy Checklist. Statistics For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section. n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one- or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section. 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Software and code Policy information about availability of computer code Data collection Data was collected in two locations: in Madrid ongoing intracranial EEG activity was acquired using XLTEK EMU128FS amplifier (XLTEK, Ontario, Canada). In Zurich, intracranial recordings were acquired using a Neuralynx ATLAS system. Data analysis Data was analysed in Matlab 2019b using the Fieldtrip toolbox (version 20200310) and custom-code available in the following Github repository: https://github.com/frdarya/GenerativeRipples For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information. Data March 2021 Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: - Accession codes, unique identifiers, or web links for publicly available datasets - A description of any restrictions on data availability - For clinical datasets or third party data, please ensure that the statement adheres to our policy All data needed to generate the figures, and over which statistics were computed, are available in the following Github repository: https://github.com/frdarya/ GenerativeRipples. 1 nature portfolio | reporting summary Human research participants Policy information about studies involving human research participants and Sex and Gender in Research. Reporting on sex and gender Of the 18 patients who participated (17 who fulfill the inclusion criteria) 8 were males. Population characteristics Eighteen medication-resistant presurgical epilepsy patients (8 males; age range 24-59, mean age = 37.3 SD = 11.7), see Table S1 for full details Recruitment We included in the study all patients that agreed to participate and that had electrodes implantation in the hippocampus. Implantation sites were chosen solely on the basis of clinical criteria. Ethics oversight All patients signed informed consent. The study had full approval from the local ethics committees of the Hospital Ruber Internacional, Madrid, Spain and Kantonale Ethikkommission, Zurich, Switzerland (PB-2016-02055). Note that full information on the approval of the study protocol must also be provided in the manuscript. Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. Life sciences Behavioural & social sciences Ecological, evolutionary & environmental sciences For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf Life sciences study design All studies must disclose on these points even when the disclosure is negative. Sample size No statistical methods were used to pre-determine sample sizes but our sample sizes are larger or equal to those reported in previous publications (Zheng et al, 2017; Mendez-Bertolo et al 2016; Costa et al., 2022). Data exclusions We included in the analyses all patients that agreed to participate and completed the task (at least 25% of trials after exclusion due to performance or electrophysiological artefacts) and that had electrodes implantation in the hippocampus. Implantation sites were chosen solely on the basis of clinical criteria. Replication No out-of-sample replications were attempted, single-subject datapoints are presented in main and supplementary figures to demonstrate within-study between-subjects effects are consistent. Randomization The design is a within-subject with trial-by-trial variation (no categorical conditions), the underlying probability distribution of stimuli was randomly chosen per patient prior to the task from all possible combinations. Blinding Patients were not allocated into different experimental groups, therefore blinding was not relevant for data collection. Participants were naive to the task goals. Reporting for specific materials, systems and methods We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response. 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