Authors:
Jochen Kerdels
and
Gabriele Peters
Affiliation:
University of Hagen, Germany
Keyword(s):
Noise Resilience, Grid Cell Model, Input Space Representation, Recursive Growing Neural Gas.
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
;
Neuroinformatics and Bioinformatics
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Self-Organization and Emergence
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Grid cells are neurons in the entorhinal cortex of mammals that are known for their peculiar, grid-like firing patterns. We developed a generic computational model that describes the behavior of neurons with such firing patterns in terms of a competitive, self-organized learning process. Here we investigate how this process can cope with increasing amounts of noise in its input signal. We demonstrate, that the firing patterns of simulated neurons are mostly unaffected with regard to their structure even if high levels of noise are present in the input. In contrast, the maximum activity of the corresponding neurons decreases significantly with increasing levels of noise. Based on these results we predict that real grid cells can retain their triangular firing patterns in the presence of noise, but may exhibit a noticeable decrease in their peak firing rates.