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

• Published in: 2020 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 1 - 5
• Main Authors: Baumgartner, Sandro, Renner, Alpha, Kreiser, Raphaela, Liang, Dongchen, Indiveri, Giacomo, Sandamirskaya, Yulia
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2020
• Subjects: Dynamic Vision Sensor; Hardware; Neuromorphic pattern recognition; Neuromorphics; Neurons; Pattern recognition; spiking neural networks; Synapses; Visualization; Voltage control
• ISBN: 9781728133201; 1728133203
• ISSN: 2158-1525; 2158-1525
• DOI: 10.1109/ISCAS45731.2020.9180628

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.