Stochastic variational learning in recurrent spiking networks.
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.
Laboratory of Cognitive Neuroscience, School of Life Sciences, Brain Mind Institute, Ecole Polytechnique Federale de Lausanne Lausanne, Vaud, Switzerland ; Laboratory of Computational Neuroscience, School of Computer and Communication Sciences, Ecole Polytechnique Federale de Lausanne Lausanne, Vaud, Switzerland.
Danilo
D
Jimenez Rezende
Laboratory of Cognitive Neuroscience, School of Life Sciences, Brain Mind Institute, Ecole Polytechnique Federale de Lausanne Lausanne, Vaud, Switzerland ; Laboratory of Computational Neuroscience, School of Computer and Communication Sciences, Ecole Polytechnique Federale de Lausanne Lausanne, Vaud, Switzerland.
Wulfram
W
Gerstner
action potentials; neural networks; spiking neurons; synapses; variational learning