TY - JOUR
T1 - Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons
A1 - Buesing, Lars
A1 - Bill, Johannes
A1 - Nessler, Bernhard
A1 - Maass, Wolfgang
Y1 - 2011/11/03
N2 - Author Summary It is well-known that neurons communicate with short electric pulses, called action potentials or spikes. But how can spiking networks implement complex computations? Attempts to relate spiking network activity to results of deterministic computation steps, like the output bits of a processor in a digital computer, are conflicting with findings from cognitive science and neuroscience, the latter indicating the neural spike output in identical experiments changes from trial to trial, i.e., neurons are “unreliable”. Therefore, it has been recently proposed that neural activity should rather be regarded as samples from an underlying probability distribution over many variables which, e.g., represent a model of the external world incorporating prior knowledge, memories as well as sensory input. This hypothesis assumes that networks of stochastically spiking neurons are able to emulate powerful algorithms for reasoning in the face of uncertainty, i.e., to carry out probabilistic inference. In this work we propose a detailed neural network model that indeed fulfills these computational requirements and we relate the spiking dynamics of the network to concrete probabilistic computations. Our model suggests that neural systems are suitable to carry out probabilistic inference by using stochastic, rather than deterministic, computing elements.
JF - PLOS Computational Biology
JA - PLOS Computational Biology
VL - 7
IS - 11
UR - https://doi.org/10.1371/journal.pcbi.1002211
SP - e1002211
EP -
PB - Public Library of Science
M3 - doi:10.1371/journal.pcbi.1002211
ER -