Year: 2012
Type: conference-paper
Abstract: Recent experiments have demonstrated that humans and animals typically reason probabilistically about their environment.This ability requires a neural code that represents probability distributions and neural circuits that are capable of implementing the operations of probabilistic inference.The proposed probabilistic population coding (PPC) framework provides a statistically efficient neural representation of probability distributions that is both broadly consistent with physiological measurements and capable of implementing some of the basic operations of probabilistic inference in a biologically plausible way.However, these experiments and the corresponding neural models have largely focused on simple (tractable) probabilistic computations such as cue combination, coordinate transformations, and decision making.As a result it remains unclear how to generalize this framework to more complex probabilistic computations.Here we address this short coming by showing that a very general approximate inference algorithm known as Variational Bayesian Expectation Maximization can be naturally implemented within the linear PPC framework.We apply this approach to a generic problem faced by any given layer of cortex, namely the identification of latent causes of complex mixtures of spikes.We identify a formal equivalent between this spike pattern demixing problem and topic models used for document classification, in particular Latent Dirichlet Allocation (LDA).We then construct a neural network implementation of variational inference and learning for LDA that utilizes a linear PPC.This network relies critically on two non-linear operations: divisive normalization and super-linear facilitation, both of which are ubiquitously observed in neural circuits.We also demonstrate how online learning can be achieved using a variation of Hebb's rule and describe an extension of this work which allows us to deal with time varying and correlated latent causes.
Institutions: University of Rochester, University of Geneva, Duke University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2157992638