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Synaptic dynamics in analog VLSI


Bartolozzi, C; Indiveri, G (2007). Synaptic dynamics in analog VLSI. Neural Computation, 19(10):2581-2603.

Abstract

Synapses are crucial elements for computation and information transfer in both real and artificial neural systems. Recent experimental findings and theoretical models of pulse-based neural networks suggest that synaptic dynamics can play a crucial role for learning neural codes and encoding spatio-temporal spike patterns. Within the context of hardware implementations of pulse based neural networks, several analog VLSI circuits modeling synaptic functionality have been proposed. We present an overview of previously proposed circuits and describe a novel analog VLSI synaptic circuit suitable for integration in large VLSI spike-based neural systems. The circuit proposed is based on a computational model that fits the real post-synaptic currents with exponentials. We present experimental data showing how the circuit exhibits realistic dynamics and show how it can be connected to additional modules for implementing a wide range of synaptic properties.

Abstract

Synapses are crucial elements for computation and information transfer in both real and artificial neural systems. Recent experimental findings and theoretical models of pulse-based neural networks suggest that synaptic dynamics can play a crucial role for learning neural codes and encoding spatio-temporal spike patterns. Within the context of hardware implementations of pulse based neural networks, several analog VLSI circuits modeling synaptic functionality have been proposed. We present an overview of previously proposed circuits and describe a novel analog VLSI synaptic circuit suitable for integration in large VLSI spike-based neural systems. The circuit proposed is based on a computational model that fits the real post-synaptic currents with exponentials. We present experimental data showing how the circuit exhibits realistic dynamics and show how it can be connected to additional modules for implementing a wide range of synaptic properties.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2007
Deposited On:18 Feb 2014 13:49
Last Modified:08 Dec 2017 04:15
Publisher:MIT Press
Series Name:Neural Computation
Number of Pages:23
ISSN:0899-7667
Publisher DOI:https://doi.org/10.1162/neco.2007.19.10.2581
PubMed ID:17716003

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