Giancarlo La Camera

This paper introduces a neural circuit model that learns to segment relevant stimuli from continuous sensory input using reinforcement learning in spiking neuron populations.

The paper investigates how linear and categorical coding neurons in the mouse gustatory cortex contribute to population dynamics and behavior during taste decision-making.

This paper introduces a sticky Poisson Hidden Markov Model (sPHMM) to address over-segmentation and rapid state switching in cortical datasets by enforcing large self-transition probabilities.

This study shows that a local plasticity rule in a spiking neural network can generate metastable dynamics and maintain synaptic stability while allowing plasticity for sensory representation remapping.

The paper presents a model of decentralized vision in sea urchins that explains how light is processed to produce oriented movement.

This study analyzes metastable dynamics in mouse gustatory cortex during decision-making and shows how these dynamics encode sensory and decision information over time.

The paper uses hidden Markov models to analyze neural activity in rhesus monkeys during a distance discrimination task, finding that slower prefrontal metastable dynamics predict error trials.

The paper proposes a computational mechanism explaining how expectation accelerates sensory coding in the gustatory cortex of rats by modulating metastable activity.

This study examines the roles of the lateral prefrontal cortex and orbitofrontal cortex in abstract rule learning and reversal in monkeys, finding distinct impairments in each region.

This study examines how neural activity dimensionality changes in alert rats during ongoing and stimulus-evoked activity in the sensory cortex, finding that dimensionality grows faster during ongoing activity and is influenced by network structure and correlations.