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Visual Pattern Recognition with on On-Chip Learning: Towards a Fully Neuromorphic Approach


Baumgartner, Sandro; Renner, Alpha; Kreiser, Raphaela; Liang, Dongchen; Indiveri, Giacomo; Sandamirskaya, Yulia (2020). Visual Pattern Recognition with on On-Chip Learning: Towards a Fully Neuromorphic Approach. In: 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain, 10 October 2020 - 21 October 2020.

Abstract

We present a spiking neural network (SNN) for visual pattern recognition with on-chip learning on neuromorphic hardware. We show how this network can learn simple visual patterns composed of horizontal and vertical bars sensed by a Dynamic Vision Sensor, using a local spike-based plasticity rule. During recognition, the network classifies the pattern's identity while at the same time estimating its location and scale. We build on previous work that used learning with neuromorphic hardware in the loop and demonstrate that the proposed network can properly operate with on-chip learning, demonstrating a complete neuromorphic pattern learning and recognition setup. Our results show that the network is robust against noise on the input (no accuracy drop when adding 130% noise) and against up to 20% noise in the neuron parameters.

Abstract

We present a spiking neural network (SNN) for visual pattern recognition with on-chip learning on neuromorphic hardware. We show how this network can learn simple visual patterns composed of horizontal and vertical bars sensed by a Dynamic Vision Sensor, using a local spike-based plasticity rule. During recognition, the network classifies the pattern's identity while at the same time estimating its location and scale. We build on previous work that used learning with neuromorphic hardware in the loop and demonstrate that the proposed network can properly operate with on-chip learning, demonstrating a complete neuromorphic pattern learning and recognition setup. Our results show that the network is robust against noise on the input (no accuracy drop when adding 130% noise) and against up to 20% noise in the neuron parameters.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:21 October 2020
Deposited On:16 Feb 2021 08:36
Last Modified:16 Feb 2021 20:30
Publisher:IEEE
ISBN:9781728133201
OA Status:Green
Publisher DOI:https://doi.org/10.1109/iscas45731.2020.9180628

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