Stefano Fusi

A new neural code for position: generalizable spatial maps in mouse medial frontal cortex

The paper identifies a novel neural code in the mouse medial frontal cortex that supports spatial generalization through extrapolation of coordinates across regions.

High-dimensional hippocampus representation facilitates learning of low-dimensional abstract representation and supports generalization

This paper shows that high-dimensional hippocampal representations aid in forming low-dimensional abstract representations and improve generalization.

Purkinje cells in Crus I and II encode the visual stimulus and the impending choice as monkeys learn a reinforcement based visuomotor association task

The paper shows that Purkinje cells in Crus I/II encode visual stimuli and impending choices during learning of a visuomotor task, with increased selectivity over time.

The representational geometry of emotional states in basolateral amygdala

The paper investigates how the basolateral amygdala encodes emotional states through representational geometry, finding that valence and motor responses are encoded in orthogonal directions enabling specialized readouts.

The geometry of context-dependent biased decisions during learning

This study shows how reward context is encoded through geometric shifts in neural representations, enabling rapid decision adjustments during learning.

Neural representations supporting generalization under continual learning

The paper develops a factorized model of temporal abstraction to study how abstract knowledge is learned and represented in the brain during a decision task, showing that hippocampus, entorhinal cortex, and orbitofrontal cortex support this process.

The effects of task similarity during representation learning in brains and neural networks

This study shows that task similarity during learning can impair performance, with neural and network models showing reduced orthogonality in representations for similar tasks.

Impact of Training Regimes and Task Similarity on Learning and Cognitive Transfer

The paper investigates how task similarity and training regime affect learning and transfer, finding that blocked training improves performance on similar tasks, while interleaving hinders it, with an asymmetric effect based on task order.

Distinct representations of an anxiogenic environment in different cell types of the ventral hippocampus

The paper examines how different cell types in the ventral hippocampus encode anxiety-related information during an anxiety test, finding distinct encoding patterns in granule cells and mossy cells.

Optimal sparsity in autoencoder memory models of the hippocampus

This study investigates how input compressibility affects optimal sparsity in sparse autoencoders, finding that optimal coding levels depend on memory demand and input statistics.

Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment

The paper shows adaptation increases discriminability of frequent stimuli in mouse V1 by reshaping population response geometry, despite reduced individual neuron responses.

Impaired spatial coding and neuronal hyperactivity in the medial entorhinal cortex of APP NL-G-F mice

This study examines how advanced amyloid beta pathology in APP NL-G-F mice affects spatial coding and neuronal activity in the medial entorhinal cortex, revealing impaired spatial information processing and hyperactivity.

Rarely categorical and highly separable: how neural representations change along the cortical hierarchy

The paper analyzes neural representations across the cortical hierarchy and finds that while categorical encoding is rare within regions, overall cortical structure supports categorical organization and maximal separability.

Prefrontal representations of retrospective spatial working memory in a rodent radial maze task

The paper investigates prefrontal cortex (PFC) neuronal activity during a complex spatial working memory task in rodents and finds retrospective mnemonic representations of spatial information during the delay phase, particularly on error trials.

When and why modular representations emerge

This study shows that modular representations in neural networks emerge for complex behavior when inputs are low-dimensional, without anatomical constraints, and supports rapid learning and generalization.

Perirhinal Cortex Learns A Predictive Map of The Task Environment

The paper identifies neural signatures of a predictive map in perirhinal cortex during task learning in mice using tactile working memory tasks.

Abstract representations emerge in human hippocampal neurons during inference behavior

The paper shows that human hippocampal neurons form abstract, disentangled representations during inferential reasoning tasks, which correlate with inference behavior.

Neural signatures of stress susceptibility and resilience in the amygdala-hippocampal network

The paper identifies and rescues dysfunctional neural dynamics in the vCA1-BLA circuit of stress-susceptible mice, improving anhedonia and resilience.

Ventral CA1 Population Codes for Anxiety

This study identifies population codes in ventral CA1 neurons that represent anxiety-related environmental features and moment-to-moment anxiety states in mice.

Abstract representations emerge naturally in neural networks trained to perform multiple tasks

This paper shows that training neural networks on multiple tasks leads to the emergence of abstract representations, which support generalization and few-sample learning.

The geometry of cortical representations of touch in rodents

The paper examines the geometric structure of neural representations in the somatosensory cortex of mice during touch tasks, revealing non-linear integration of sensory inputs and disentangled variables in orthogonal subspaces.

Face familiarity detection with complex synapses

This paper demonstrates that complex synapses enhance memory capacity for face familiarity detection, outperforming simple synapses in real-world recognition tasks.

Neural representational geometries correlate with behavioral differences in monkeys and recurrent neural networks

The paper shows that differences in neural representational geometry between monkeys correlate with behavioral differences and training levels, using recurrent neural networks to model these strategies.

Signatures of rapid synaptic learning in the hippocampus during novel experiences

The paper investigates how hippocampal neurons rapidly encode new environments through synaptic plasticity mechanisms during novel experiences.

Tuned geometries of hippocampal representations meet the demands of social memory

This study investigates how hippocampal CA2 neurons represent familiar and novel individuals, finding that familiarity alters the geometric structure of neural representations to support different social memory processes.

Are place cells just memory cells? Memory compression leads to spatial tuning and history dependence

This paper proposes a memory model of the hippocampus that explains place cells as a result of memory compression, showing that their spatial tuning and history dependence arise from compressed representations of sensory experiences.

The sensorimotor strategies and neuronal representations of tactile shape discrimination in mice

This study investigates how mice discriminate tactile shapes by analyzing neuronal activity in the barrel cortex during a shape discrimination task.

The neural code for face memory

This study investigates how familiar faces are encoded in the brain, finding that they are represented in a distinct long-latency code in IT and perirhinal cortex that enhances discrimination.

Coding of social novelty in the hippocampal CA2 region and its disruption and rescue in a mouse model of schizophrenia

The paper investigates how hippocampal CA2 neurons encode social novelty and shows that disrupting this coding in a schizophrenia mouse model can be rescued by a TREK-1 antagonist.

The geometry of abstraction in hippocampus and prefrontal cortex

The paper examines how neural representations in monkeys and simulated networks encode abstract variables for generalization in decision-making tasks.