Authors:
Jochen Kerdels
and
Gabriele Peters
Affiliation:
FernUniversitt in Hagen – University of Hagen, Human-Computer Interaction, Faculty of Mathematics and Computer Science, Universitätsstrasse 1 , D-58097 Hagen and Germany
Keyword(s):
Cortical Column, Autoassociative Memory, Grid Cells, Attractor Dynamics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Self-Organization and Emergence
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The cortex of mammals has a distinct, low-level structure consisting of six horizontal layers that are vertically connected by local groups of about 80 to 100 neurons forming so-called minicolumns. A well-known and widely discussed hypothesis suggests that this regular structure may indicate that there could be a common computational principle that governs the diverse functions performed by the cortex. However, no generally accepted theory regarding such a common principle has been presented so far. In this position paper we provide a novel perspective on a possible function of cortical columns. Based on our previous efforts to model the behaviour of entorhinal grid cells we argue that a single cortical column can function as an independent, autoassociative memory cell (AMC) that utilizes a sparse distributed encoding. We demonstrate the basic operation of this AMC by a first set of preliminary simulation results.