author = {Nessler, , Bernhard AND Pfeiffer, , Michael AND Buesing, , Lars AND
Maass, , Wolfgang},
title = {Bayesian Computation Emerges in Generic Cortical Microcircuits through
Spike-Timing-Dependent Plasticity},
journal = {PLoS Comput Biol},
year = {2013},
volume = {9},
pages = {e1003037},
number = {4},
month = {04},
abstract = {Author Summary

How do neurons learn to extract information
from their inputs, and perform meaningful computations? Neurons receive
inputs as continuous streams of action potentials or "spikes" that
arrive at thousands of synapses. The strength of these synapses -
the synaptic weight - undergoes constant modification. It has been
demonstrated in numerous experiments that this modification depends
on the temporal order of spikes in the pre- and postsynaptic neuron,
a rule known as STDP, but it has remained unclear, how this contributes
to higher level functions in neural network architectures. In this
paper we show that STDP induces in a commonly found connectivity
motif in the cortex - a winner-take-all (WTA) network - autonomous,
self-organized learning of probabilistic models of the input. The
resulting function of the neural circuit is Bayesian computation
on the input spike trains. Such unsupervised learning has previously
been studied extensively on an abstract, algorithmical level. We
show that STDP approximates one of the most powerful learning methods
in machine learning, Expectation-Maximization (EM). In a series of
computer simulations we demonstrate that this enables STDP in WTA
circuits to solve complex learning tasks, reaching a performance
level that surpasses previous uses of spiking neural networks.

doi = {10.1371/journal.pcbi.1003037},
file = {:NesslerPfeifferBuesingMaass_BayesianComputationEmergesInGenericCorticalMicrocircuitsThroughSTDP_PLOSCompBiol2013.pdf:PDF;:NesslerPfeifferBuesingMaass_BayesianCompEmergesInCorticalCircuitsThroughSTDP_Supplementary1.pdf:PDF},
publisher = {Public Library of Science},
url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1003037}


26 August 2016