@ARTICLE{NesslerETAL2013,
  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 = {
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}
}