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A spike-based saccadic recognition system


Oster, M; Lichtsteiner, P; Delbruck, T; Liu, S C (2007). A spike-based saccadic recognition system. In: IEEE International Symposium on Circuits and Systems (ISCAS) 2007, New Orleans, USA, 27 May 2007 - 30 May 2007, 3083-3086.

Abstract

The paper presents a spike-based saccadic recognition system that uses a temporal-derivative silicon retina on a pan-tilt unit and an aVLSI multi-neuron classifier with a time-to-first-spike output coding. By using the spike information during the last 150 ms of a saccadic movement, we generate a reliable, sparse stimulus representation of image patches. The paper describes a novel classification scheme where the retinal spikes during this time influence the time-to-first spike of classifier neurons which receive the same constant input current. The preferred pattern of the neuron is stored in the synaptic connectivity between the retina and the classifier neuron. The authors demonstrates the robustness and real-time performance of this recognition scheme on a saccadic system which uses analog VLSI components.

The paper presents a spike-based saccadic recognition system that uses a temporal-derivative silicon retina on a pan-tilt unit and an aVLSI multi-neuron classifier with a time-to-first-spike output coding. By using the spike information during the last 150 ms of a saccadic movement, we generate a reliable, sparse stimulus representation of image patches. The paper describes a novel classification scheme where the retinal spikes during this time influence the time-to-first spike of classifier neurons which receive the same constant input current. The preferred pattern of the neuron is stored in the synaptic connectivity between the retina and the classifier neuron. The authors demonstrates the robustness and real-time performance of this recognition scheme on a saccadic system which uses analog VLSI components.

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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:30 May 2007
Deposited On:21 Mar 2014 13:41
Last Modified:05 Apr 2016 17:42
Publisher:IEEE
ISBN:1-4244-0921-7
Publisher DOI:https://doi.org/10.1109/ISCAS.2007.378060

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