Biology subjects

Boboeva, V.

Publications and source records attributed to Boboeva, V..

7 recordsLinked to original sources

bioRxiv2026

From sequences to schemas: low-rank recurrent dynamics underlie abstract relational representations

A hallmark of intelligent behavior is the ability to extract abstract relational structure from temporal sequences -- recognizing, for instance, that aab, ccd, and eef all follow the same underlying pattern, regardless of the specific elements involved. This capacity, observed across species and sensory modalities, is thought to underlie the formation of cognitive schemas: compressed internal models that support rapid generalization to novel experiences. Yet the neural circuit mechanisms by which such abstract, identity-independent representations emerge from sequential experience remain largely unknown. Here, we investigate this question using Recurrent Neural Networks (RNNs) as mechanistic models of neural circuits, trained to classify sequences based on their latent algebraic patterns (e.g., aab, aad [->] AAB; aba, aca [->] ABA) without supervision on intermediate transitions. We demonstrate that RNNs spontaneously learn low-dimensional representations that mirror the hierarchical generative structure of the sequences, and that this abstraction is mechanistically supported by the emergence of low-rank recurrent connectivity. The leading singular component of the recurrent weight matrix integrates relational transition information -- whether consecutive tokens are the same or different -- across time, driving the formation of a structured, tree-like geometry in the population state space. Through singular vector ablation, we establish a causal role for this component: removing it selectively erases memory for earlier transitions while leaving local, single-step sensitivity intact. Finally, while RNNs trained on next-token prediction do not spontaneously acquire these abstract representations, transferring the low-rank scaffold learned from classification significantly accelerates learning and improves generalization -- an effect specific to the abstract structure of the scaffold rather than generic statistical pretraining. These findings offer a computational account of how task demands shape recurrent connectivity to support temporal abstraction, with direct implications for understanding schema formation in biological brains.

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bioRxiv2026

The hippocampus enables abstract structure learning without reward

Statistical learning (SL) allows organisms to infer latent structure from sensory input without instruction, feedback, or reward, yet how the brain accomplishes such abstract, unsupervised learning remains unknown. Here we show that mice, like humans, rapidly acquire multiple forms of statistical structure, including event frequency, sequence identity, and abstract structural rules, and that the hippocampus is essential for this capacity. Pupil dynamics provided a cross-species, implicit readout of expectation formation, revealing spontaneous sensitivity to these regularities during passive listening. In mice, pharmacological and temporally precise optogenetic inactivation of dorsal CA1 abolished all learning-related pupil signatures without affecting baseline pupil size, target-evoked responses, or task performance, demonstrating a causal requirement for the hippocampus in forming and updating internal models of sensory structure. High-density recordings further revealed that dCA1 ensembles track evolving statistical contexts while dynamically reorganising population activity into subspaces that separately encode sensory features and abstract rules, enabling generalisation across distinct but structurally equivalent sequences. Together, these results identify the hippocampus as a critical neural substrate for latent abstract structure learning and offer a mechanistic account of how internal models emerge from unsupervised experience.

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bioRxiv2022

From recency to the central tendency bias in working memory: a unifying attractor network model

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased towards the average of past observations. It is assumed to be an optimal strategy by the brain, and commonly thought of as an expression of the brains ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed, and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats, suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience - producing short-term sensory history biases - naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli, and are not due to a gradual shift of the memory towards the sensory distributions mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

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bioRxiv2021

In Poetry, if Meter has to Help Memory, it Takes its Time

To test the idea that poetic meter emerged as a cognitive schema to aid verbal memory, we have focused on classical Italian poetry and on its three basic components of meter: rhyme, accent and verse length. Meaningless poems were generated by introducing prosody-invariant non-words into passages from Dantes Divina Commedia and Ariostos Orlando Furioso, which were then further manipulated by selectively ablating rhymes, modifying accent patterns or altering the number of syllables. The resulting four versions of each non-poem were presented in a fully balanced design to cohorts of high school educated Italian native speakers, who were then asked to retrieve 3 target non-words. Surprisingly, we found that the integrity of Dantes meter has no significant effect on memory performance. With passages derived from Ariosto, instead, removing each component downgrades memory by an amount proportional to its contribution to perceived metric plausibility, with rhymes having the strongest effects, followed by accents and then by verse length. Counterintuitively, the fully metric versions required longer reaction times, implying that activating metric schemata involves a cognitive cost. Within schema theories, this finding provides evidence for high-level interactions between procedural and episodic memory.

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bioRxiv2021

Latching dynamics as a basis for short-term recall

We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state - latching dynamics. We show that in one such model, and in simple spatial memory tasks we have given to human subjects, short-term memory can be limited to similar low capacity by interference effects, in tasks terminated by errors, and can exhibit similar sublinear scaling, when errors are overlooked. The same mechanism can drive serial recall if combined with weak order-encoding plasticity. Finally, even when storing randomly correlated patterns of activity the network demonstrates correlation-driven latching waves, which are reflected at the outer extremes of pattern space.

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bioRxiv2020

Free recall scaling laws and short-term memory effects in a latching attractor network

Despite the complexity of human memory, paradigms like free recall have revealed robust qualitative and quantitative characteristics, such as power laws governing recall capacity. Although abstract random matrix models could explain such laws, the possibility of their implementation in large networks of interacting neurons has so far remained unexplored. We study an attractor network model of long-term memory endowed with firing rate adaptation and global inhibition. Under appropriate conditions, the transitioning behaviour of the network from memory to memory is constrained by limit cycles that prevent the network from recalling all memories, with scaling similar to what has been found in experiments. When the model is supplemented with a heteroassociative learning rule, complementing the standard autoassociative learning rule, as well as short-term synaptic facilitation, our model reproduces other key findings in the free recall literature, namely serial position effects, contiguity and forward asymmetry effects, as well as the semantic effects found to guide memory recall. The model is consistent with a broad series of manipulations aimed at gaining a better understanding of the variables that affect recall, such as the role of rehearsal, presentation rates and (continuous/end-of-list) distractor conditions. We predict that recall capacity may be increased with the addition of small amounts of noise, for example in the form of weak random stimuli during recall. Moreover, we predict that although the statistics of the encoded memories has a strong effect on the recall capacity, the power laws governing recall capacity may still be expected to hold.

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bioRxiv2018

The capacity for correlated semantic memories in the cortex

1. Introduction 1. Introduction 2. Results 3. Discussion: a new... References One of the most fascinating aspects of the human brain is its ability to ascribe significance to and recognize meaning in objects and events, and more generally to make sense of the world. Semantic memory, comprising our acquired knowledge about the world, can be imagined to reflect, in its statistical structure, the complex, distributed, policentric structure of the neocortex where it resides. In contrast, the relatively much simpler network structure of the hippocampus, in particular of its CA3 field, where episodic memories have long been thought to be at least initially represented by unique patterns of neural activity, may lead to the limited set of outcomes of episodic memory retrieval: either the pattern is retrieved, or not. In the first case, retrieval, subjects reme ...

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