Synaptic plasticity is known to be a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the nature of the local changes at individual synapses and their link with emerging network-level computations remain unclear. I will present a numerical, meta-learning approach to deduce plasticity rules from either neuronal activity data and/or prior knowledge about the network's computation. I will first show how to recover known rules, given a human-designed loss function in rate networks, or directly from data, using an adversarial approach. Then I will present how to scale-up this approach to recurrent spiking networks using simulation-based inference.

Analytics consent required

Meta Learning Functional Plasticity

Meta-learning functional plasticity rules in neural networks

Seminar location

Access Seminar

Event Information

Seminar location

Abstract

Topics

About the Speaker

Related Seminars

Continuous guidance of human goal-directed movements

Rett syndrome, MECP2 and therapeutic strategies

Genetic and epigenetic underpinnings of neurodegenerative disorders

World Wide highlights