Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Patrik Hoyer, Aapo Hyvärinen


The responses of cortical sensory neurons are notoriously variable, with the number of spikes evoked by identical stimuli varying significantly from trial to trial. This variability is most often interpreted as ‘noise’, purely detrimental to the sensory system. In this paper, we propose an al- ternative view in which the variability is related to the uncertainty, about world parameters, which is inherent in the sensory stimulus. Specifi- cally, the responses of a population of neurons are interpreted as stochas- tic samples from the posterior distribution in a latent variable model. In addition to giving theoretical arguments supporting such a representa- tional scheme, we provide simulations suggesting how some aspects of response variability might be understood in this framework.