Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.

ABSTRACT: Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the inp

SUBMITTER: Sanda P 

PROVIDER: S-EPMC5636167 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature