Abstract
During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input.
Highlights
Humans and animals perceive their environment through a stream of data from various highdimensional sensory modalities
In order to assess the network’s ability to detect and integrate highly interdependent correlation structures among features at different locations, we introduced complex dependencies between the input presented to the seven populations: While the two outer input locations served as a left cue (LQ) and right cue (RQ), and were chosen independently, the five inner locations were chosen to be (a) different from the cues, and (b) consistent with each other
Building on the theory of Markov chain Monte Carlo (MCMC) sampling, we have shown how the transient spike response of the recurrent neural network can be understood as an ongoing sampling process from a well-defined Bayesian posterior distribution
Summary
Humans and animals perceive their environment through a stream of data from various highdimensional sensory modalities. Successful behavior requires that the individual dimensions of this data stream are aligned with one another and integrated into a compact representation that promotes rapid decision making and generalization. Bayesian inference has been identified in cognitive science as a powerful normative framework for the description of cognitive processes in face of uncertainty in humans [1,2,3] and animals [4]. The Bayesian framework has been successfully employed for a formal description of learning, for instance in perceptual [5, 6] and sensorimotor [7, 8] learning tasks
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