University of Cambridge > Talks.cam > Computational Neuroscience > Computational Neuroscience Journal Club

Computational Neuroscience Journal Club

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Guillaume Hennequin.

David Barrett will cover: Stochastic variational learning in recurrent spiking networks, Rezende D and Gerstner W, Frontiers in Computational Neuroscience 2014 (http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00038/abstract).

ABSTRACT : The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about “novelty” on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

This talk is part of the Computational Neuroscience series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity