Research article summary (published 28 Feb 2008):

A principle for learning egocentric-allocentric transformation.

Full Abstract

Numerous single-unit recording studies have found mammalian hippocampal neurons that fire selectively for the animal's location in space, independent of its orientation. The population of such neurons, commonly known as place cells, is thought to maintain an allocentric, or orientation-independent, internal representation of the animal's location in space, as well as mediating long-term storage of spatial memories. The fact that spatial information from the environment must reach the brain via sensory receptors in an inherently egocentric, or viewpoint-dependent, fashion leads to the question of how the brain learns to transform egocentric sensory representations into allocentric ones for long-term memory storage. Additionally, if these long-term memory representations of space are to be useful in guiding motor behavior, then the reverse transformation, from allocentric to egocentric coordinates, must also be learned. We propose that orientation-invariant representations can be learned by neural circuits that follow two learning principles: minimization of reconstruction error and maximization of representational temporal inertia. Two different neural network models are presented that adhere to these learning principles, the first by direct optimization through gradient descent and the second using a more biologically realistic circuit based on the restricted Boltzmann machine (Hinton, 2002; Smolensky, 1986). Both models lead to orientation-invariant representations, with the latter demonstrating place-cell-like responses when trained on a linear track environment.

Author information

Author/s: Byrne, Patrick (P); Becker, Suzanna (S);

Affiliation: Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton, Ontario, L8S 4K1, Canada. pbyrne(-atsign-)yorku.ca

Journal and publication information

Publication Type: Letter; Research Support, Non-U.S. Gov't

Journal: Neural computation (Neural Comput), published in United States. (Language: eng)

Reference: 2008-Mar; vol 20 (issue 3) : pp 709-37

Dates: Created 2008/03/28; Completed 2008/05/06;

PMID: 18045016, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Action Potentials - physiology Animals Brain - physiology Computer Simulation Humans Learning - physiology Memory - physiology Models, Neurological

Models, Neurological Nerve Net - physiology Neural Networks (Computer) Neurons - physiology Orientation - physiology Psychomotor Performance - physiology Space Perception - physiology Time Factors

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