Multi-Layer Neuromorphic Synapse for Reconfigurable Networks

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Multi-Layer Neuromorphic Synapse for Reconfigurable Networks

Conference Paper (2019)

Author(s)

Amir Zjajo (TU Delft - Signal Processing Systems)

Sumeet Kumar (TU Delft - Signal Processing Systems)

Rene Van Leuken (TU Delft - Signal Processing Systems)

Cognitive systems Neuromorphic circuits Synapse

DOI related publication

https://doi.org/10.1109/ICSP.2018.8652360 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:d86210ad-5d28-4823-bf64-fb2a8b6daa58

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Publication Year

2019

Language

English

Article number

8652360

Pages (from-to)

997-1000

ISBN (print)

978-1-5386-4674-8

ISBN (electronic)

978-1-5386-4673-1

Event

Downloads counter

234

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.