Duke Physics Colloquium: Storing and retrieving memories in models of neuronal networks

Wednesday, January 16, 2019 - 3:30pm

Nicolas Brunel (Duke Neurobiology and Duke Physics)

"Storing and retrieving memories in models of neuronal networks"

Memories are thought to be stored in brain networks thanks to modifications of synaptic connectivity between neurons. 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 in inferring thelearning 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 potential dynamics that allow the network to retrieve the stored information (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.

Faculty host: Anselm Vossen

Refreshments will be available before the event in room 130.

View this event's recording here.

Physics 130

Location Info