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Implementing homeostatic plasticity in VLSI networks of spiking neurons


Bartolozzi, C; Nikolayeva, O; Indiveri, G (2008). Implementing homeostatic plasticity in VLSI networks of spiking neurons. In: IEEE. 15th IEEE International Conference on Electronics, Circuits and Systems, 2008 (ICECS 2008), Malta, St. Julians, 31 August - 3 September 2008. Piscataway, NJ, US: IEEE Service Center, 682-685.

Abstract

Homeostatic plasticity acts to stabilize firing activity in neural systems, ensuring a homogeneous computational substrate despite the inherent differences among neurons and their continuous change. These types of mechanisms are extremely relevant for any physical implementation of neural systems. They can be used in VLSI pulse-based neural networks to automatically adapt to chronic input changes, device mismatch, as well as slow systematic changes in the circuitpsilas functionality (e.g. due to temperature drifts). In this paper we propose analog circuits for implementing homeostatic plasticity mechanisms in VLSI spiking neural networks, compatible with local spike-based learning mechanisms. We show experimental results where a homeostatic control is implemented as a hybrid SoftWare/HardWare (SW/HW) solution, and present analog circuits for a complete on-chip stand-alone solution, validated by circuit simulations.

Homeostatic plasticity acts to stabilize firing activity in neural systems, ensuring a homogeneous computational substrate despite the inherent differences among neurons and their continuous change. These types of mechanisms are extremely relevant for any physical implementation of neural systems. They can be used in VLSI pulse-based neural networks to automatically adapt to chronic input changes, device mismatch, as well as slow systematic changes in the circuitpsilas functionality (e.g. due to temperature drifts). In this paper we propose analog circuits for implementing homeostatic plasticity mechanisms in VLSI spiking neural networks, compatible with local spike-based learning mechanisms. We show experimental results where a homeostatic control is implemented as a hybrid SoftWare/HardWare (SW/HW) solution, and present analog circuits for a complete on-chip stand-alone solution, validated by circuit simulations.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Uncontrolled Keywords:neuromorphic
Language:English
Date:2008
Deposited On:06 Mar 2009 17:04
Last Modified:05 Apr 2016 13:10
Publisher:IEEE Service Center
ISBN:978-1-4244-2181-7
Additional Information:© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Publisher DOI:10.1109/ICECS.2008.4674945
Related URLs:http://www.icecs2008.org/ (Organisation)
Permanent URL: http://doi.org/10.5167/uzh-17606

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