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Multi-Layer Neuromorphic Synapse for Reconfigurable Networks

Conference Paper (2019)
Author(s)

Amir Zjajo (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Sumeet Kumar (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Rene Van Leuken (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Research Group
Signal Processing Systems
DOI related publication
https://doi.org/10.1109/ICSP.2018.8652360 Final published version
More Info
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Publication Year
2019
Language
English
Research Group
Signal Processing Systems
Article number
8652360
Pages (from-to)
997-1000
ISBN (print)
978-1-5386-4674-8
ISBN (electronic)
978-1-5386-4673-1
Event
14th IEEE International Conference on Signal Processing, ICSP 2018 (2018-08-12 - 2018-08-16), Beijing, China
Downloads counter
256

Abstract

In pulse-based neural networks, synaptic dynamics can have direct influence on learning of neural codes, and encoding of spatiotemporal spike patterns. In this paper, we propose an adaptive synapse circuit for increased flexibility and efficacy of signal processing units in neuromorphic structures. The synapse acts as a multi-layer computational network, and includes multi-compartment dendrites and different types of post-synaptic back propagating signals. With built-in temporal control mechanisms, the resulting reconfigurable network allows the implementation of synaptic homeostatics.