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Mapping arbitrary mathematical functions and dynamical systems to neuromorphic VLSI circuits for spike-based neural computation


Corradi, F; Eliasmith, C; Indiveri, G (2014). Mapping arbitrary mathematical functions and dynamical systems to neuromorphic VLSI circuits for spike-based neural computation. In: IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia, 1 June 2014 - 5 June 2014, 269-272.

Abstract

Brain-inspired, spike-based computation in electronic systems is being investigated for developing alternative, non-conventional computing technologies. The Neural Engineering Framework provides a method for programming these devices to implement computation. In this paper we apply this approach to perform arbitrary mathematical computation using a mixed signal analog/digital neuromorphic multi-neuron VLSI chip. This is achieved by means of a network of spiking neurons with multiple weighted connections. The synaptic weights are stored in a 4-bit on-chip programmable SRAM block. We propose a parallel event-based method for calibrating appropriately the synaptic weights and demonstrate the method by encoding and decoding arbitrary mathematical functions, and by implementing dynamical systems via recurrent connections.

Brain-inspired, spike-based computation in electronic systems is being investigated for developing alternative, non-conventional computing technologies. The Neural Engineering Framework provides a method for programming these devices to implement computation. In this paper we apply this approach to perform arbitrary mathematical computation using a mixed signal analog/digital neuromorphic multi-neuron VLSI chip. This is achieved by means of a network of spiking neurons with multiple weighted connections. The synaptic weights are stored in a 4-bit on-chip programmable SRAM block. We propose a parallel event-based method for calibrating appropriately the synaptic weights and demonstrate the method by encoding and decoding arbitrary mathematical functions, and by implementing dynamical systems via recurrent connections.

Citations

5 citations in Web of Science®
5 citations in Scopus®
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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:5 June 2014
Deposited On:25 Feb 2015 10:35
Last Modified:05 Apr 2016 19:00
Publisher:Proceedings of the 2014 IEEE International Symposium on Circuits and Systems (ISCAS)
Series Name:IEEE International Symposium on Circuits and Systems (ISCAS), 2014
ISBN:978-1-4799-3431-7
Publisher DOI:https://doi.org/10.1109/ISCAS.2014.6865117

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