Stefano Recanatesi

Reward changes the use and structure of predictive spatial maps

This study investigates how reward influences hippocampal spatial maps by separating its effects on map usage and structure through a two-pathway model.

Task Engagement Gates Interareal Communication Geometry in the Mouse Thalamocortical-Midbrain Visual Circuit

The paper investigates how task engagement affects interareal communication geometry in the mouse visual thalamocortical-midbrain circuit, showing reduced network activity and dimensionality.

A non-Hebbian code for episodic memory

This paper introduces a non-Hebbian plasticity rule for episodic memory that stores episodes as path vectors in a world model, enabling flexible memory and learning.

A scale-dependent measure of system dimensionality

The paper introduces a scale-dependent measure of system dimensionality and applies it to neural recordings, showing differences in effective dimensionality between behaviorally engaged and spontaneous brain states.

Recurrent neural networks learn robust representations by dynamically balancing compression and expansion

This study shows how recurrent neural networks balance compression and expansion of activity dimensionality during learning to solve discrimination tasks effectively.

Path vectors: a neural code for sequential memory

This paper introduces a model where sequences are represented as path vectors through cognitive states, showing how structured narratives improve memory recall by reducing path interference.

Metastable attractors explain the variable timing of stable behavioral action sequences

The paper uses hidden Markov models to show metastable attractors explain variable timing in stable behavioral action sequences in rats.

Autoencoder networks extract latent variables and encode these variables in their connectomes

The paper uses autoencoder networks to show that regularization can resolve ambiguity in connectivity weights, allowing latent variable extraction through nonlinear dimensionality reduction.

Predictive learning as a network mechanism forextracting low-dimensional latent spacerepresentations

This study investigates how predictive learning in neural networks generates low-dimensional latent representations of sensory inputs by analyzing network dynamics and intrinsic dimensionality.

Strong and localized recurrence controls dimensionality of neural activity across brain areas

The paper shows that recurrent synaptic networks in the mouse cortex strongly control neural activity dimensionality across time and brain areas, mediated by cell-type specific network motifs present in both mouse and human brains.

Single circuit in V1 capable of switching contexts during movement using VIP population as a switch

The paper demonstrates a neural circuit in V1 that can switch between static and moving visual contexts using a VIP population as a switch, achieving efficient context-dependent processing.

Emergence of hierarchical organization in memory for random material

The paper shows that participants spontaneously create hierarchical structures when recalling random word lists, improving memory performance and resembling real-life narrative structures.

Dimensionality in recurrent spiking networks: global trends in activity and local origins in connectivity

This paper investigates how dimensionality in spiking neural networks is influenced by connectivity and stimulus input, revealing principles linking local connectivity to global activity patterns.