Hippocampal learning and cognitive maps as products of hierarchical latent variable models

Abstract

Event Abstract Back to Event Hippocampal learning and cognitive maps as products of hierarchical latent variable models Adam Johnson1*, Zachary Varberg1 and Paul Schrater2 1 Bethel University , United States 2 University of Minnesota, United States The hippocampus supports inference within early learning regimes and on a variety of complex spatial/context memory tasks. The defining characteristic of hippocampus dependent learning is that it provides the basis for a broad class of inferences in domains where sampling is limited relative to the potential dimensionality of inputs. Standard approaches to spatial learning and context learning have used neural network models to describe generalization behavior at the level of place cell maps and at the level of animal behavior. However, these models often overfit training data and compromise inference for novel test data. And in many cases, pattern classification dynamics observed in neural network models can be easily accommodated within probabilistic inference frameworks. We develop a model-based Bayesian approach to learning hierarchical latent variable structure and dynamics for spatial and context learning tasks. This approach expands probabilistic treatments of classical conditioning (Courville et al., 2003) to spatial learning domains for inference related to context dependent learning rules (e.g. "odor/reward position" paired-associate learning). These context dependent learning rules can be found by computing the posterior probabilities over latent variable hyperparameters. As a consequence, resultant behavioral inferences for novel test items reflect a mixture of latent models and are generally more robust. We model the spatial paired-associate task developed by Morris and colleagues (Day, Langston and Morris, 2003; Tse et al., 2007). Tse et al. (2007) showed that following initial training, rats learn new paired associates in a single trial. Single trial learning was dependent on the hippocampus and the consistency of the previously learned paired-associates within a given context. We show that initial training tunes the hyperparameter posterior density and consequently facilitates learning subsequent novel paired-associates - both in the search for newly learned paired-associate food-sites and for the avoidance of known food-sites following presentation of a novel odor. Furthermore, we show that the structure of the task requires the use of a full conjunctive representation for learning new paired-associates but does not require a full conjunctive representation for retrieval of previously learned paired-associates. Together these results show how the hippocampus facilitates new learning but is not always necessary for retrieval of previously learned information. Morris and colleagues suggest the hippocampus supports schema representations that facilitate new learning within a context. While this perspective has provided important insights into animal learning theory, it has lacked a computational counterpart. We suggest that hippocampus dependent schematic representations and cognitive maps are the consequence of a process that probabilistically infers hierarchical latent structure from input data. This statistical approach supports model optimization for inference across test data rather than model optimization across training data. As a result, the latent variable approach provides a much more robust inference engine than standard neural network models. Finally, the extraction of latent structure from input data allows for a simple explanation of hippocampal function and its important role within early learning regimes. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session II Citation: Johnson A, Varberg Z and Schrater P (2010). Hippocampal learning and cognitive maps as products of hierarchical latent variable models. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00213 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: 04 Mar 2010; Published Online: 04 Mar 2010. * Correspondence: Adam Johnson, Bethel University, St. Paul, United States, adam-johnson@bethel.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 Adam Johnson Zachary Varberg Paul Schrater Google Adam Johnson Zachary Varberg Paul Schrater Google Scholar Adam Johnson Zachary Varberg Paul Schrater PubMed Adam Johnson Zachary Varberg Paul Schrater 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.