Analog VLSI implementation of a nonlinear systems model of the hippocampal brain region
- 17 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The hippocampus is a major brain system involved in learning and memory functions, and consists of multiple populations of neurons with strongly nonlinear properties that are interconnected both locally and non-locally. An analog VLSI design has been developed that allows different classes of nonlinearities specific to each neuron population to define the transfer function of a network of neurons implemented in hardware. Principles of a CNN design have been used to generate local interactions between adjacent processing elements. Non-local interactions will be implemented in future designs with the use of multiple chips. In this manner, we are attempting to better integrate into a hardware device the unique information processing and learning capabilities of real biological neurons known to perform those functions.<>Keywords
This publication has 5 references indexed in Scilit:
- A biologically based model of functional properties of the hippocampusNeural Networks, 1994
- The Mechanism of Expression of Long-Term Enhancement of Hippocampal Synapses: Current Issues and Theoretical ImplicationsAnnual Review of Physiology, 1993
- Hardware Annealing in Analog VLSI NeurocomputingPublished by Springer Nature ,1991
- Cellular neural networks: applicationsIEEE Transactions on Circuits and Systems, 1988
- Identification of nonlinear systems using random impulse train inputsBiological Cybernetics, 1975