An error-propagation spiking neural network compatible with neuromorphic processors
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
Spiking neural networks have shown great promise for the design of low-power sensory-processing and edge-computing hardware platforms. However, implementing onchip 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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published
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Book title
2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Journal / series
Volume
Pages / Article No.
84 - 88
Publisher
IEEE
Event
2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2020) (virtual)
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Organisational unit
09699 - Indiveri, Giacomo / Indiveri, Giacomo
Notes
Conference postponed due to Corona virus (COVID-19). Due to the Corona virus (COVID-19) the conference was conducted virtually.