Biology subjects

Lakretz, Y.

Publications and source records attributed to Lakretz, Y..

4 recordsLinked to original sources

bioRxiv2025

Semantic Tuning of Single Neurons in the Human Medial Temporal Lobe

The Medial Temporal Lobe (MTL) is key to human cognition, supporting memory, emotional processing, navigation, and semantic coding. Rare direct human MTL recordings revealed concept cells, which were proposed as episodic memory building blocks due to their context- and modality-invariant response. However, there is a long-standing debate whether concept cells encode information in a discrete, all-or-none fashion, or as a graded, continuous function of semantic dimensions. To resolve this, we developed a closed-loop protocol that analyzes neuronal spiking in real time and adaptively presents new stimuli based on semantic similarity to response-eliciting previous ones. We found that human concept cells show graded responses, following semantic tuning curves. Furthermore, the tuning width and steepness of MTL neurons vary, with the hippocampus exhibiting the steepest tuning. Regional analysis of these tuning curves yields region-specific categorical response profiles (e.g., food responses in the amygdala). Finally, we show that at the population level, MTL activity is best captured by continuous semantic vector models, which outperform categorical and network-based metrics. Together, these findings establish that concept cells exhibit semantic tuning curves, providing new mechanistic insights into the neural organization of semantic information in the human brain.

neuroscience↗

bioRxiv2023

Dimensionality and ramping: Signatures of sentence integration in the dynamics of brains and deep language models

A sentence is more than the sum of its words: its meaning depends on how they combine with one another. The brain mechanisms underlying such semantic composition remain poorly understood. To shed light on the neural vector code underlying semantic composition, we introduce two hypotheses: First, the intrinsic dimensionality of the space of neural representations should increase as a sentence unfolds, paralleling the growing complexity of its semantic representation, and second, this progressive integration should be reflected in ramping and sentence-final signals. To test these predictions, we designed a dataset of closely matched normal and Jabberwocky sentences (composed of meaningless pseudo words) and displayed them to deep language models and to 11 human participants (5 men and 6 women) monitored with simultaneous magneto-encephalography and intracranial electro-encephalography. In both deep language models and electrophysiological data, we found that representational dimensionality was higher for meaningful sentences than Jabberwocky. Furthermore, multivariate decoding of normal versus Jabberwocky confirmed three dynamic patterns: (i) a phasic pattern following each word, peaking in temporal and parietal areas, (ii) a ramping pattern, characteristic of bilateral inferior and middle frontal gyri, and (iii) a sentence-final pattern in left superior frontal gyrus and right orbitofrontal cortex. These results provide a first glimpse into the neural geometry of semantic integration and constrain the search for a neural code of linguistic composition. Significance statementStarting from general linguistic concepts, we make two sets of predictions in neural signals evoked by reading multi-word sentences. First, the intrinsic dimensionality of the representation should grow with additional meaningful words. Second, the neural dynamics should exhibit signatures of encoding, maintaining, and resolving semantic composition. We successfully validated these hypotheses in deep Neural Language Models, artificial neural networks trained on text and performing very well on many Natural Language Processing tasks. Then, using a unique combination of magnetoencephalography and intracranial electrodes, we recorded high-resolution brain data from human participants while they read a controlled set of sentences. Time-resolved dimensionality analysis showed increasing dimensionality with meaning, and multivariate decoding allowed us to isolate the three dynamical patterns we had hypothesized.

neuroscience↗

bioRxiv2022

Disentangling Hierarchical and Sequential Computations during Sentence Processing.

Sentences in natural language have a hierarchical structure, that can be described in terms of nested trees. To compose sentence meaning, the human brain needs to link successive words into complex syntactic structures. However, such hierarchical-structure processing could co-exist with a simpler, shallower, and perhaps evolutionarily older mechanism for local, word-by-word sequential processing. Indeed, classic work from psycholinguistics suggests the existence of such non-hierarchical processing, which can interfere with hierarchical processing and lead to sentence-processing errors in humans. However, such interference can arise from two, non mutually exclusive, reasons: interference between words in working memory, or interference between local versus long-distance word-prediction signals. Teasing apart these two possibilities is difficult based on behavioral data alone. Here, we conducted a magnetoen-cephalography experiment to study hierarchical vs. sequential computations during sentence processing in the human brain. We studied whether the two processes have distinct neural signatures and whether sequential interference observed behaviorally is due to memory-based interference or to competing word-prediction signals. Our results show (1) a large dominance of hierarchical processing in the human brain compared to sequential processing, and (2) neural evidence for interference between words in memory, but no evidence for competing prediction signals. Our study shows that once words enter the language system, computations are dominated by structure-based processing and largely robust to sequential effects; and that even when behavioral interference occurs, it need not indicate the existence of a shallow, local language prediction system.

neuroscience↗

bioRxiv2020

Spatiotemporal Dynamics of Orthographic and Lexical Processing in the Ventral Visual Pathway

Reading is a rapid, distributed process that engages multiple components of the ventral visual stream. However, the neural constituents and their interactions that allow us to identify written words are not well understood. Using direct intracranial recordings in a large cohort of humans, we comprehensively isolated the spatiotemporal dynamics of visual word recognition across the entire left ventral occipitotemporal cortex. The mid-fusiform cortex is the first region that is sensitive to word identity and to both sub-lexical and lexical frequencies. Its activation, response latency and amplitude, are highly dependent on the statistics of natural language. Information about lexicality and word frequency propagates posteriorly from this region to traditional visual word form regions and to earlier visual cortex. This unique sensitivity of mid-fusiform cortex to the lexical characteristics of written words points to its central role as an orthographic lexicon, which accesses the long-term memory representations of visual word forms.

neuroscience↗