Biology subjects

Chericoni, A.

Publications and source records attributed to Chericoni, A..

8 recordsLinked to original sources

bioRxiv2026

Semantic axes in the brain support analogical representations

ABSTRACTIn language models of word meaning, directions in the embedding space often correspond to semantic features that can be reused across different words. For example, a single direction corresponding to gender may differentiate word pairs like "boy/girl", "uncle/aunt" and "king/queen". Here we show that the same principle governs semantically driven neural responses in the human brain. We recorded populations of single neurons during podcast listening and identified word sets with consistent meaning differences. Across fifteen sets, including gender, plural, and negation, we observed consistent vectorial directions, resulting in parallelogram structures within the neural manifold. Deviation from parallelism in large language models (LLMs) predicted corresponding deviations in brain-derived parallelism. Among pronouns, vectors corresponding to case, number and person exhibited parallelogram structures individually and, collectively, obeyed the principle of commutativity, resulting in a prismatic structure. Finally, different semantic variables were preferentially associated with discrete groups of neurons, consistent with energy-efficiency theories. Together, these results establish a geometric foundation for the neural encoding of word meaning.

neuroscience↗

bioRxiv2025

Shared neural geometries for bilingual semantic representations

The human brain has the remarkable ability to comprehend and express similar concepts in multiple languages. To understand how it does so, we examined responses of hippocampal neurons during passive listening, directed speaking, and spontaneous conversation, in both English and Spanish, in a small group of balanced bilinguals. We find a small number of putative cross-language neurons, whose responses to equivalent words (e.g., "tierra" and "earth") are correlated. However, neurons semantic tunings differed substantially by language, suggesting language-specific neural implementations. Instead, the crucial driver of translation was a preserved geometric organization of neural responses between the two languages, one that did not depend on neuron level functional overlap. Indeed, that geometry was implemented by a common set of neurons along distinct readout axes; this difference in readout may help prevent cross-language interference. Together, these results suggest that hippocampus encodes a language-independent internal model for meaning.

neuroscience↗

bioRxiv2025

A semantotopic map in human hippocampus

The hippocampus plays a central role in encoding abstract conceptual and semantic information. However, little is known about the topography of that encoding. We leveraged the rare opportunity to examine neural responses densely along a small portion of the human hippocampus, specifically, the mediolateral axis of the anterior body. We collected responses to passive language listening using Neuropixels probes in three anesthetized patients during clinically indicated neurosurgical procedures. We computed semantic tuning functions for each recording site by regressing threshold crossing events and single unit responses against semantic embeddings from GPT-2, Word2Vec, and SBERT. We find that tuning functions of more distant recording sites are more dissimilar, supporting the hypothesis semantotopic organization. Multiple semantic features showed systematic changes along that axis, including animacy, concreteness, and familiarity; notably, effects were individual-specific. Surprisingly, we also found a small but significant increase in semantic similarity as a function of distance between recording sites, on a shorter spatial scale, suggesting a modest periodic organization. Together, these results demonstrate the presence of a multiscale functional organization of semantics in the hippocampus.

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bioRxiv2025

Mirror manifolds: partially overlapping neural subspaces for speaking and listening

We utilize internal representations of meaning for two purposes: to understand the words we hear and to generate our own speech. This dual requirement necessitates abstract, modality-agnostic representations. Building on work identifying it as a hub for relational mapping, we hypothesized that the hippocampus supports abstract, cross-person representations, and uses shared semantic geometries to do so. We tested this hypothesis by examining hippocampal activity in a remarkable single-neuron dataset derived from conversational speech. Neurons robustly encoded meanings of both spoken and heard words, and used common geometric embeddings for both, leading to abstract meaning performance. Speaker identity was aligned with meaning via partial subspace alignment, which affords speaker-meaning binding by partitioning meaning by speaker while maintaining cross-speaker generalization. Degrees of subspace rotation varied on a single word level and depended systematically on semantic category. Together, these findings indicate how geometric principles allow for abstract cross-personal meanings while preserving binding to speaker identity.

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bioRxiv2025

Attention is all you need (in the brain): semantic contextualization in human hippocampus

Word meanings in language are contextualized by surrounding words. Inspired by the self-attention mechanism in transformer-based large language models (LLMs), we hypothesized that structural composition in the brain arises from combining canonical (non-contextual) word representations with those of nearby words. We analyzed single unit activity in the human hippocampus, a region involved in semantic and contextual processing, while n=10 participants listened to podcasts. We found that hippocampal neurons encoded word position within a clause, using both ordinal and frequency-domain positional encoding. Moreover, neural responses to specific words reflected both the words own lexical semantics and a weighted sum of the embeddings of preceding words. The relative weighting of these contextualizing words correlated with LLM self-attention weights. These findings suggest that contextualization in the brain makes use of vectorial shifts that have a resemblance to attentional reweighting in LLMs, and highlight the role of the mesial temporal lobe within the broader language network.

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bioRxiv2025

Complementary roles for hippocampus and anterior cingulate in composing continuous choice

Naturalistic, goal directed behavior often requires continuous actions directed at dynamically changing goals. In this context, the closest analogue to choice is a strategic reweighting of multiple goal-specific control policies in response to shifting environmental pressures. To understand the algorithmic and neural bases of choice in continuous contexts, we examined behavior and brain activity in humans performing a continuous prey-pursuit task. Using a newly developed control-theoretic decomposition of behavior, we find pursuit strategies are well described by a meta-controller dictating a mixture of lower-level controllers, each linked to specific pursuit goals. We find that hippocampal neurons encode the policy blending variable in a value-invariant manner and monitor policy switches after they occur. ACC neurons encode policy switches in a value-dependent manner, with value related modulation detectable several hundred ms before the switch, alongside a ramping increase in mean firing rate toward the switch. Meanwhile, OFC activity is consistent with an encoding of the current value structure of the task, rather than policy switching. Together these results are consistent with a tripartite functional division in which hippocampus serves as a controller over behavior, ACC serves as a meta-controller, and OFC provides a value context signal. Overall, our results shed light onto the complex processes associated with choice during naturalistic continuous interactive behavior.

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bioRxiv2025

Independent Continuous Tracking of Multiple Agents in the Human Hippocampus

The pursuit of fleeing prey is a core element of many species behavioral repertoires. It poses the difficult problem of continuous tracking of multiple agents, including both self and others. To understand how this tracking is implemented neurally, we examined responses of hippocampal neurons while humans performed a joystick-controlled continuous prey-pursuit task involving two simultaneously fleeing prey (and, in some cases, a predator) in a virtual open field. We found neural maps encoding the positions of all the agents. All maps were multiplexed in single neurons and were disambiguated by the use of the population coding principle of semi-orthogonal subspaces, which can facilitate cross-agent generalization. Some neurons, more common in the posterior hippocampus, had narrow tuning functions reminiscent of place cells, lower firing rates, and high information per spike; others, which were found in both anterior and posterior hippocampus, had broad tuning functions, higher firing rates, and less information per spike. Semi-orthogonalization was selectively associated with the broadly tuned neurons. These results suggest an answer to the problem of navigational individuation, that is, how mapping codes can distinguish different agents, and establish the neuronavigational foundations of pursuit.

neuroscience↗

bioRxiv2025

A vectorial code for semantics in human hippocampus

As we listen to speech, our brains track the meanings of the words we hear. Recent successes of large language models suggest that distributed population geometry can capture rich semantic relationships between words. Motivated by this idea, we hypothesized that semantic information in the brain may likewise be expressed in distributed patterns of activity across neurons, rather than in the activity of neurons narrowly tuned to a specific word. We recorded responses of hundreds of neurons in the human hippocampus while participants listened to narrative speech. We find encoding of contextual word meaning in the simultaneous activity of neurons whose individual selectivities span multiple unrelated semantic categories. Decoding and population geometry analyses revealed distinct neural coding principles for low-versus high-frequency words, likely reflecting the greater polysemy of common words. Similar to embedding vectors in semantic language models, distance between neural population responses correlates with semantic distance; however, this effect was only observed in contextual embedding models (GPT-2 and BERT), suggesting that the semantic distance effect depends critically on contextualization. Consistent with this, we find that neural population activity supports a multidimensional semantic subspace that aligns most closely with the contextual structure captured by GPT-2. Moreover, for semantically similar words, even contextual embedders showed an inverse correlation between semantic and neural distances; we attribute this pattern to the noise-mitigating benefits of contrastive coding. Ultimately, these results provide a neurocomputational account for understanding how neural populations track word meaning.

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