Turing complete neural computation based on synaptic plasticity.

ABSTRACT: In neural computation, the essential information is generally encoded into the neurons via their spiking configurations, activation values or (attractor) dynamics. The synapses and their associated plasticity mechanisms are, by contrast, mainly used to process this information and implement the crucial learning features. Here, we propose a novel Turing complete paradigm of neural computation where the essential information is encoded into discrete synaptic states, and the updating of this information achieved via synaptic plasticity mechanisms. More specifically, we prove that any 2-counter machine-and hence any Turing machine-can be simulated by a rational-weighted recurrent neural network employing spike-timing-dependent plasticity (STDP) rules. The computational states and counter value

SUBMITTER: Cabessa J 

PROVIDER: S-EPMC6795493 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature