Neural Comput - Emergence of optimal decoding of population codes through STDP.

Tópicos

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Resumo

The brain faces the problem of inferring reliable hidden causes from large populations of noisy neurons, for example, the direction of a moving object from spikes in area MT. It is known that a theoretically optimal likelihood decoding could be carried out by simple linear readout neurons if weights of synaptic connections were set to certain values that depend on the tuning functions of sensory neurons. We show here that such theoretically optimal readout weights emerge autonomously through STDP in conjunction with lateral inhibition between readout neurons. In particular, we identify a class of optimal STDP learning rules with homeostatic plasticity, for which the autonomous emergence of optimal readouts can be explained on the basis of a rigorous learning theory. This theory shows that the network motif we consider approximates expectation-maximization for creating internal generative models for hidden causes of high-dimensional spike inputs. Notably, we find that this optimal functionality can be well approximated by a variety of STDP rules beyond those predicted by theory. Furthermore, we show that this learning process is very stable and automatically adjusts weights to changes in the number of readout neurons, the tuning functions of sensory neurons, and the statistics of external stimuli.

Resumo Limpo

brain face problem infer reliabl hidden caus larg popul noisi neuron exampl direct move object spike area mt known theoret optim likelihood decod carri simpl linear readout neuron weight synapt connect set certain valu depend tune function sensori neuron show theoret optim readout weight emerg autonom stdp conjunct later inhibit readout neuron particular identifi class optim stdp learn rule homeostat plastic autonom emerg optim readout can explain basi rigor learn theori theori show network motif consid approxim expectationmaxim creat intern generat model hidden caus highdimension spike input notabl find optim function can well approxim varieti stdp rule beyond predict theori furthermor show learn process stabl automat adjust weight chang number readout neuron tune function sensori neuron statist extern stimuli

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