Biology subjects

Devore, S.

Publications and source records attributed to Devore, S..

16 recordsLinked to original sources

bioRxiv2026

Normalization accounts for temporal dynamics in human somatosensory cortex

Sensory processing is fundamentally shaped by stimulation history. For example, in visual cortex, neural responses are reduced for repeated or sustained stimuli (adaptation). These phenomena are well characterized and effectively modeled by divisive normalization. We asked whether these same computational principles govern somatosensory processing. We used fMRI (6 participants) and intracranial electroencephalography (iEEG, 2 participants) to measure responses to time-varying vibrotactile stimuli in human somatosensory cortex. Stimuli consisted of single- and paired-pulses with durations and interstimulus intervals ranging from 0.05 to 1.2 s. We extracted BOLD time courses to capture neural response amplitudes, and high-frequency iEEG broadband envelopes to capture fast neural dynamics. In both experiments, we observed pronounced sub-additive temporal summation. Responses to longer or repeated stimuli were consistently lower than predicted by linear integration. Computational modeling revealed that divisive normalization models outperformed linear models in cross-validated accuracy across both datasets. These results demonstrate that somatosensory temporal dynamics closely mirror those in the visual system. Our findings suggest that the nervous system employs similar computational principles across modalities to encode sensory information across time. Significance statementHow the brain integrates sensory information over time is a fundamental question in neuroscience. While nonlinear temporal integration is well documented in visual cortex, it has not been extensively mapped in the human somatosensory system. By combining fMRI with intracranial EEG in humans, we demonstrate that somatosensory responses to tactile stimulation exhibit subadditive temporal summation. This nonlinearity is accurately captured by a divisive normalization model, matching observations in the visual system. Our results suggest that normalization is a canonical computation shared across different modalities to manage temporal dynamics, providing a unified framework for understanding how the brain encodes dynamic sensory stimuli.

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bioRxiv2025

The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension

Naturalistic electrocorticography (ECoG) data are a rare but essential resource for studying the brains linguistic capabilities. ECoG offers a high temporal resolution suitable for investigating processes at multiple temporal timescales and frequency bands. It also provides broad spatial coverage, often along critical language areas. Here, we share a dataset of nine ECoG participants with 1,330 electrodes listening to a 30-minute audio podcast. The richness of this naturalistic stimulus can be used for various research endeavors, from auditory perception to semantic integration. In addition to the neural data, we extract linguistic features of the stimulus ranging from phonetic information to large language model word embeddings. We use these linguistic features in encoding models that relate stimulus properties to neural activity. Finally, we provide detailed tutorials for preprocessing raw data, extracting stimulus features, and running encoding analyses that can serve as a pedagogical resource or a springboard for new research.

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bioRxiv2025

Fast-timescale hippocampal processes bridge between slowly unfurling neocortical states during memory search

Prior behavioral work showed that event structure supports our ability to search through continuous naturalistic memories. We hypothesized that, neurally, this memory search process involves a division of labor between slowly unfurling neocortical states representing event knowledge and fast hippocampal-neocortical communication supporting retrieval of new information at transitions between events. In a sample of ten intracranial EEG patients we tracked slow neural state-patterns as patients viewed a movie and then searched their memories in a structured naturalistic interview. As patients answered questions ("after A, when does B happen next?"), state-patterns from movie-viewing were reinstated in neocortex. During the critical memory-search period, states unfurled in a forward direction, realizing a trajectory from A to B in memory. Punctate moments of state-transition during memory-search were marked by low-frequency power decreases in cortex and preceded by power decreases in hippocampus that correlated with reinstatement. Connectivity-analysis revealed information-flow from hippocampus to cortex underpinning state-transitions.

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bioRxiv2024

Information-making processes in the speaker's brain drive human conversations forward

Natural human conversation is driven by the exchange of information-rich messages that surprise the listener and deviate from predictable context. While extensive research has characterized how the brain processes unexpected linguistic input during comprehension, the neural mechanisms underlying the generation of such information by the speaker remain poorly understood. In this study, we hypothesize that the speakers brain actively creates information through extra neural computation, transforming internal thoughts into novel, meaningful linguistic outputs. Utilizing a unique 24/7 dataset of continuous electrocorticography (ECoG) comprising approximately 100 hours of spontaneous natural conversations, we contrast the neural basis of speech production and comprehension within the same participants. Behaviorally, we find that speakers take longer pausing for an additional 100-150 milliseconds before producing information-rich, improbable words, even when controlling for word identity and frequency. Neurally, we provide converging evidence for a previously unreported process: generating information-rich words elicits significantly stronger neural activity (ERP) and enhanced neural encoding and decoding in language-related areas starting 100-500 ms before articulation. This pattern contrasts sharply with speech comprehension, where enhanced neural activity for predictable words occurs before onset, while responses to improbable words emerge only after onset as prediction errors. Furthermore, we demonstrate that large language models (LLMs) mirror this biological process, requiring deeper internal computation across layers to generate improbable versus probable words. Together, these results reveal that the speakers brain is not merely a transmission channel but an active generator of information, recruiting extra neural resources to create novel content that diverges from listener expectations.

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bioRxiv2024

Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language

Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. However, neuroscience researchers havent kept up with the quick progress in LLM development. Here, we utilized several families of transformer-based LLMs to investigate the relationship between model size and their ability to capture linguistic information in the human brain. Crucially, a subset of LLMs were trained on a fixed training set, enabling us to dissociate model size from architecture and training set size. We used electrocorticography (ECoG) to measure neural activity in epilepsy patients while they listened to a 30-minute naturalistic audio story. We fit electrode-wise encoding models using contextual embeddings extracted from each hidden layer of the LLMs to predict word-level neural signals. In line with prior work, we found that larger LLMs better capture the structure of natural language and better predict neural activity. We also found a logarithmic relationship where the encoding performance peaks in relatively earlier layers as model size increases. We also observed variations in the best-performing layer across different brain regions, corresponding to an organized language processing hierarchy.

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bioRxiv2023

Temporal dynamics of neural adaptation across human visual cortex

Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventraland lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses across the human visual hierarchy and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences. Author summaryNeural responses in visual cortex adapt over time, with reduced responses to prolonged and repeated stimuli. Here, we examine how adaptation patterns differ across the visual hierarchy in neural responses recorded from human visual cortex with high temporal and spatial precision. To identify possible neural computations underlying short-term adaptation, we fit the response time courses using a temporal divisive normalization model. The model accurately predicts prolonged and repeated responses in lower and higher visual areas, and reveals differences in temporal adaptation across the visual hierarchy and stimulus categories. Our model suggests that differences in adaptation patterns result from differences in divisive normalization dynamics. Our findings shed light on how information is integrated in the brain on a millisecond-time scale and offer an intuitive framework to study the emergence of neural dynamics across brain areas and stimuli.

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bioRxiv2023

A shared linguistic space for transmitting our thoughts from brain to brain in natural conversations

Effective communication hinges on a mutual understanding of word meaning in different contexts. The embedding space learned by large language models can serve as an explicit model of the shared, context-rich meaning space humans use to communicate their thoughts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We demonstrate that the linguistic embedding space can capture the linguistic content of word-by-word neural alignment between speaker and listener. Linguistic content emerged in the speakers brain before word articulation, and the same linguistic content rapidly reemerged in the listeners brain after word articulation. These findings establish a computational framework to study how human brains transmit their thoughts to one another in real-world contexts.

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bioRxiv2023

Deep speech-to-text models capture the neural basis of spontaneous speech in everyday conversations

Humans effortlessly use the continuous acoustics of speech to communicate rich linguistic meaning during everyday conversations. In this study, we leverage 100 hours (half a million words) of spontaneous open-ended conversations and concurrent high-quality neural activity recorded using electrocorticography (ECoG) to decipher the neural basis of real-world speech production and comprehension. Employing a deep multimodal speech-to-text model named Whisper, we develop encoding models capable of accurately predicting neural responses to both acoustic and semantic aspects of speech. Our encoding models achieved high accuracy in predicting neural responses in hundreds of thousands of words across many hours of left-out recordings. We uncover a distributed cortical hierarchy for speech and language processing, with sensory and motor regions encoding acoustic features of speech and higher-level language areas encoding syntactic and semantic information. Many electrodes--including those in both perceptual and motor areas--display mixed selectivity for both speech and linguistic features. Notably, our encoding model reveals a temporal progression from language-to-speech encoding before word onset during speech production and from speech-to-language encoding following word articulation during speech comprehension. This study offers a comprehensive account of the unfolding neural responses during fully natural, unbounded daily conversations. By leveraging a multimodal deep speech recognition model, we highlight the power of deep learning for unraveling the neural mechanisms of language processing in real-world contexts.

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bioRxiv2023

An adversarial collaboration to critically evaluate theories of consciousness

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, yet, confirmation bias and dependence on design choices hamper progress in the field3. Here, we present an open science adversarial collaboration which directly juxtaposes Integrated Information Theory (IIT)4,5 and Global Neuronal Workspace Theory (GNWT)6-10, employing a theory-neutral consortium approach11,12. We investigate neural correlates of the content and duration of visual experience. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes, and their interpretation12. 256 human subjects viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography, and electrocorticography. We find information about conscious content in visual, ventro-temporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results confirm some predictions of IIT and GNWT, while substantially challenging both theories: for IIT, a lack of sustained synchronization within posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in prefrontal cortex. Beyond challenging the theories themselves, we present an alternative approach to advance cognitive neuroscience through a principled, theory-driven, collaborative effort. We highlight the challenges to change peoples mind 13 and the need for a quantitative framework integrating evidence for systematic theory testing and building.

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bioRxiv2023

Spatial Tuning of Alpha Oscillations in Human Visual Cortex

AO_SCPLOWBSTRACTC_SCPLOWNeuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz) but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention. Significance StatementThe alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention. Grant support: NIH R01 MH111417 (Petridou, Winawer, Ramsey, Devinsky); JSPS Overseas Research Fellowship (Yuasa) The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

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bioRxiv2022

Correspondence between the layered structure of deep language models and temporal structure of natural language processing in the human brain

Deep language models (DLMs) provide a novel computational paradigm for how the brain processes natural language. Unlike symbolic, rule-based models described in psycholinguistics, DLMs encode words and their context as continuous numerical vectors. These "embeddings" are constructed by a sequence of computations organized in "layers" to ultimately capture surprisingly sophisticated representations of linguistic structures. How does this layered hierarchy map onto the human brain during natural language comprehension? In this study, we used electrocorticography (ECoG) to record neural activity in language areas along the superior temporal gyrus and inferior frontal gyrus while human participants listened to a 30-minute spoken narrative. We supplied this same narrative to a high-performing DLM (GPT2-XL) and extracted the contextual embeddings for each word in the story across all 48 layers of the model. We next trained a set of linear encoding models to predict the temporally-evolving neural activity from the embeddings at each layer. We found a striking correspondence between the layer-by-layer sequence of embeddings from GPT2-XL and the temporal sequence of neural activity in language areas. In addition, we found evidence for the gradual accumulation of recurrent information along the linguistic processing hierarchy. However, we also noticed additional neural processes in the brain, but not in DLMs, during the processing of surprising (unpredictable) words. These findings point to a connection between human language processing and DLMs where the layer-by-layer accumulation of contextual information in DLM embeddings matches the temporal dynamics of neural activity in high-order language areas.

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bioRxiv2022

Brain embeddings with shared geometry to artificial contextual embeddings, as a code for representing language in the human brain

Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics. Do language areas in the human brain, similar to DLMs, rely on a continuous embedding space to represent language? To test this hypothesis, we densely recorded the neural activity in the Inferior Frontal Gyrus (IFG, also known as Brocas area) of three participants using dense intracranial arrays while they listened to a 30-minute podcast. From these fine-grained spatiotemporal neural recordings, we derived for each patient a continuous vectorial representation for each word (i.e., a brain embedding). Using stringent, zero-shot mapping, we demonstrated that brain embeddings in the IFG and the DLM contextual embedding space have strikingly similar geometry. This shared geometry allows us to precisely triangulate the position of unseen words in both the brain embedding space (zero-shot encoding) and the DLM contextual embedding space (zero-shot decoding). The continuous brain embedding space provides an alternative computational framework for how natural language is represented in cortical language areas.

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bioRxiv2021

Temporal dynamics of neural responses in human visual cortex

Neural responses to visual stimuli exhibit complex temporal dynamics, including sub-additive temporal summation, response reduction with repeated or sustained stimuli (adaptation), and slower dynamics at low contrast. These phenomena are often studied independently. Here, we demonstrate these phenomena within the same experiment and model the underlying neural computations with a single computational model. We extracted time-varying responses from electrocorticographic (ECoG) recordings from patients presented with stimuli that varied in contrast, duration, and inter-stimulus interval (ISI). Aggregating data across patients yielded 98 electrodes with robust visual responses, covering both earlier (V1-V3) and higher-order (V3a/b, LO, TO, IPS) retinotopic maps. In all regions, the temporal dynamics of neural responses exhibit several non-linear features: peak response amplitude saturates with high contrast and longer stimulus durations; the response to a second stimulus is suppressed for short ISIs and recovers for longer ISIs; response latency decreases with increasing contrast. These features are accurately captured by a computational model comprised of a small set of canonical neuronal operations: linear filtering, rectification, exponentiation, and a delayed divisive normalization. We find that an increased normalization term captures both contrast- and adaptation-related response reductions, suggesting potentially shared underlying mechanisms. We additionally demonstrate both changes and invariance in temporal response dynamics between earlier and higher-order visual areas. Together, our results reveal the presence of a wide range of temporal and contrast-dependent neuronal dynamics in the human visual cortex, and demonstrate that a simple model captures these dynamics at millisecond resolution. Significance StatementSensory inputs and neural responses change continuously over time. It is especially challenging to understand a system that has both dynamic inputs and outputs. Here we use a computational modeling approach that specifies computations to convert a time-varying input stimulus to a neural response time course, and use this to predict neural activity measured in the human visual cortex. We show that this computational model predicts a wide variety of complex neural response shapes that we induced experimentally by manipulating the duration, repetition and contrast of visual stimuli. By comparing data and model predictions, we uncover systematic properties of temporal dynamics of neural signals, allowing us to better understand how the brain processes dynamic sensory information.

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bioRxiv2020

Moment-by-moment tracking of naturalistic learning and its underlying hippocampo-cortical interactions

Every day our memory system achieves a remarkable feat: We form lasting memories of stimuli that were only encountered once. Here we investigate such learning as it naturally occurs during story listening, with the goal of uncovering when and how memories are stored and retrieved during processing of continuous, naturalistic stimuli. In behavioral experiments we confirm that, after a single exposure to a naturalistic story, participants can learn about its structure and are able to recall upcoming words in the story. In patients undergoing electrocorticographic recordings, we then track mnemonic information in high frequency activity (70 - 200Hz) as patients listen to a story twice. In auditory processing regions we demonstrate the rapid reinstatement of upcoming information after a single exposure; this neural measure of predictive recall correlates with behavioral measures of event segmentation and learning. Connectivity analyses on the neural data reveal information-flow from cortex to hippocampus at the end of events. On the second time of listening information-flow from hippocampus to cortex precedes moments of successful reinstatement.

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bioRxiv2020

Thinking ahead: prediction in context as a keystone of language in humans and machines

Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models are trained to generate appropriate linguistic responses in a given context. We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process natural language: 1) both are engaged in continuous next-word prediction before word-onset; 2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise (i.e., prediction error signals); 3) both represent words as a function of the previous context. In support of these three principles, our findings indicate that: a) the neural activity before word-onset contains context-dependent predictive information about forthcoming words, even hundreds of milliseconds before the words are perceived; b) the neural activity after word-onset reflects the surprise level and prediction error; and c) autoregressive DLM contextual embeddings capture the neural representation of context-specific word meaning better than arbitrary or static semantic embeddings. Together, our findings suggest that autoregressive DLMs provide a novel and biologically feasible computational framework for studying the neural basis of language.

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bioRxiv2020

Proteomic and Transcriptomic Analyses of the Hippocampus and Cortex in SUDEP and High-Risk SUDEP Cases

Sudden unexpected death in epilepsy (SUDEP) is the leading type of epilepsy-related death. Severely depressed brain activity in these cases may impair respiration, arousal, and protective reflexes, occurring as a prolonged postictal generalized EEG suppression (PGES) and resulting in a high-risk for SUDEP. In autopsy hippocampus and cortex, we observed no proteomic differences between SUDEP and epilepsy cases, contrasting our previously reported robust differences between epilepsy and controls. Transcriptomics in hippocampus and cortex from surgical epilepsy cases segregated by PGES identified 55 differentially expressed genes (37 protein-coding, 15 lncRNAs, three pending) in hippocampus. Overall, the SUDEP proteome and high-risk SUDEP transcriptome largely reflected other epilepsy cases in the brain regions analyzed, consistent with diverse epilepsy syndromes and comorbidities associated with SUDEP. Thus, studies with larger cohorts and different epilepsy syndromes, as well as additional anatomic regions may identify molecular mechanisms of SUDEP.

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