An error-propagation spiking neural network compatible with neuromorphic processors
Spiking neural networks have shown great promise for the design of low-power sensory-processing and edge-computing hardware platforms. However, implementing on-chip learning algorithms on such architectures is still an open challenge, especially for multi-layer networks that rely on the back-propaga...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
12.04.2021
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Subjects | |
Online Access | Get full text |
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Summary: | Spiking neural networks have shown great promise for the design of low-power
sensory-processing and edge-computing hardware platforms. However, implementing
on-chip learning algorithms on such architectures is still an open challenge,
especially for multi-layer networks that rely on the back-propagation
algorithm. In this paper, we present a spike-based learning method that
approximates back-propagation using local weight update mechanisms and which is
compatible with mixed-signal analog/digital neuromorphic circuits. We introduce
a network architecture that enables synaptic weight update mechanisms to
back-propagate error signals across layers and present a network that can be
trained to distinguish between two spike-based patterns that have identical
mean firing rates, but different spike-timings. This work represents a first
step towards the design of ultra-low power mixed-signal neuromorphic processing
systems with on-chip learning circuits that can be trained to recognize
different spatio-temporal patterns of spiking activity (e.g. produced by
event-based vision or auditory sensors). |
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DOI: | 10.48550/arxiv.2104.05241 |