Samuel Pavio Muscinelli

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Humans and some other animals are able to perform tasks that require coordination of movements across multiple temporal scales, ranging from hundreds of milliseconds to several seconds. The fast timescale at which neurons naturally operate, on the order of ...

While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurr ...

Synaptic changes induced by neural activity need to be consolidated to maintain memory over a timescale of hours. In experiments, synaptic consolidation can be induced by repeating a stimulation protocol several times and the effectiveness of consolidation ...

The neuronal mechanisms generating a delayed motor response initiated by a sensory cue remain elusive. Here, we tracked the precise sequence of cortical activity in mice transforming a brief whisker stimulus into delayed licking using wide-field calcium im ...

We show that Hopfield neural networks with synchronous dynamics and asymmetric weights admit stable orbits that form sequences of maximal length. For N units, these sequences have length T = 2^N; that is, they cover the full state space. We present a mathe ...

A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large corpus o ...

Samuel Pavio Muscinelli

Chattez avec Graph Search

Slow dynamics in structured neural network models

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model

Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response

Exponentially long orbits in Hopfield neural networks

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks