Fisica statistica

Learning and memory in brain networks: Insights from Statistical Physics

by Nicolas Brunel (Duke University)

Aula Conversi (Dipartimento di Fisica - Ed.G.Marconi)

Aula Conversi

Dipartimento di Fisica - Ed.G.Marconi


Memories are thought to be stored in brain networks thanks to
modifications of synaptic connectivity.  Mathematical
models of synaptic plasticity (sometimes called `synaptic plasticity
rules' or `learning rules') capture experimental data on plasticity
with increasing accuracy, but it is still unclear how realistic
synaptic plasticity rules shape network dynamics and information
storage.  In this talk, I will first review approaches for inferring
learning rules from neurophysiological data. I will describe in
particular a new approach that infers learning rules from in vivo
electrophysiological data, using experiments that compare the
statistics of responses of neurons to sets of novel and familiar
stimuli.  I will then focus on how the inferred learning rules shape
the dynamics of networks, leading to a diversity of attractors,
depending on parameters (fixed point attractors, chaotic attractors,
or transient sequential activity). Finally, I will show that learning
rules inferred from data are close to maximizing information storage
in a space of unsupervised learning rules.

Organized by

Luca Leuzzi