Probabilistic inference and learning: from behavior to neural representations

Abstract

Event Abstract Back to Event Probabilistic inference and learning: from behavior to neural representations Jozsef Fiser1* 1 Brandeis University, Department of Psychology and Volen Center for Complex Systems, United States Recent behavioral studies provide steadily increasing evidence that humans and animals perceive sensory input, make decisions and control their movement by optimally considering the uncertainty of the surrounding environment. Such behavior is best captured in a statistical framework, as making probabilistic inference based on the input stimulus and the stored representations of the cortex. The formalism of Probabilistic Population Codes (PPC) has emerged as one such framework that can explain how optimal cue-combination can happen in the brain. However, there is a notable lack of evidence highlighting how stored representation used in this process are obtained, whether this learning is optimal, and PPC provides little guidance as to how it might be implemented neurally.In this talk, I will argue that inference and learning are two facets of the same underlying principle of statistically optimal adaptation to external stimuli, therefore, they need to be treated together under a unified approach. First, I will present evidence that humans learn unknown hierarchical visual structures by developing a minimally sufficient representation instead of encoding the full correlational structure of the input. I will show that this learning cannot be characterized as a hierarchical associative learning process recursively linking pairs of lower-level subfeatures, but it is better captured by optimal Bayesian model comparison. Next, I will discuss how such abstract learning could be implemented in the cortex. Motivated by classical work on statistical neural networks, I will present a new probabilistic framework based on the ideas that neural activity represents samples from the posterior probability distribution of possible interpretations, and that spontaneous activity in the cortex is not noise but represents internal-state-dependent prior knowledge and assumptions of the system. I will contrast this sample-based framework with PPCs and derive predictions from the framework that can be tested empirically. Finally, I will show that multi-electrode recordings from awake behaving animals confirm these predictions by demonstrating that the structure of spontaneous activity becomes similar with age to that of visually evoked activity in the primary visual cortex. Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009. Presentation Type: Oral Presentation Topic: Learning and plasticity Citation: Fiser J (2009). Probabilistic inference and learning: from behavior to neural representations. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.165 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 07 Sep 2009; Published Online: 07 Sep 2009. * Correspondence: Jozsef Fiser, Brandeis University, Department of Psychology and Volen Center for Complex Systems, Waltham, United States, fiser@brandeis.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jozsef Fiser Google Jozsef Fiser Google Scholar Jozsef Fiser PubMed Jozsef Fiser Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.