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State-dependent sensory processing in networks of VLSI spiking neurons


Neftci, Emre; Chicca, Elisabetta; Cook, Matthew; Douglas, Rodney (2010). State-dependent sensory processing in networks of VLSI spiking neurons. In: 2010 IEEE International Symposium on Circuits and Systems (ISCAS), Paris, France, 30 May 2010 - 2 June 2010, 2789-2792.

Abstract

An increasing number of research groups develop dedicated hybrid analog/digital very large scale integration (VLSI) devices implementing hundreds of spiking neurons with bio--physically realistic dynamics. However, despite the significant progress in their design, there is still little insight in translating circuitry of neural assemblies into desired (non-trivial) function. In this work, we propose to use neural circuits implementing the soft Winner--Take--All (WTA) function. By showing that recurrently connected instances of them can have persistent activity states, which can be used as a form of working memory, we argue that such circuits can perform state--dependent computation. We demonstrate such a network in a distributed neuromorphic system consisting of two multi--neuron chips implementing soft WTA, stimulated by an event--based vision sensor. The resulting network is able to track and remember the position of a localized stimulus along a trajectory previously encoded in the system.

Abstract

An increasing number of research groups develop dedicated hybrid analog/digital very large scale integration (VLSI) devices implementing hundreds of spiking neurons with bio--physically realistic dynamics. However, despite the significant progress in their design, there is still little insight in translating circuitry of neural assemblies into desired (non-trivial) function. In this work, we propose to use neural circuits implementing the soft Winner--Take--All (WTA) function. By showing that recurrently connected instances of them can have persistent activity states, which can be used as a form of working memory, we argue that such circuits can perform state--dependent computation. We demonstrate such a network in a distributed neuromorphic system consisting of two multi--neuron chips implementing soft WTA, stimulated by an event--based vision sensor. The resulting network is able to track and remember the position of a localized stimulus along a trajectory previously encoded in the system.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:2 June 2010
Deposited On:03 Sep 2014 13:09
Last Modified:08 Dec 2017 07:04
Publisher:IEEE Xplore
ISBN:978-1-4244-5308-5
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/ISCAS.2010.5537007

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