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Robust state-dependent computation in neuromorphic electronic systems


Liang, Dongchen; Indiveri, Giacomo (2017). Robust state-dependent computation in neuromorphic electronic systems. In: Biomedical Circuits and Systems Conference (BioCAS), 2017, Turin, 19 October 2017 - 21 October 2017.

Abstract

State-dependent computation is one of the main signatures of cognition. Recently, it has been shown how it can be used as a computational primitive in spiking neural networks for constructing complex cognitive behaviors in neuromorphic agents. However, to achieve the desired computations and behaviors in mixed signal analog-digital neuromorphic electronic systems, these computational primitives should be able to cope with noisy and imprecise components, such as silicon neurons and synapses, with noisy and unreliable external signals, and with interference from the environment. Here we present a spiking neural network model that addresses all these issues while exhibiting both analog signal processing properties and digital symbolic computational abilities. We show how this Neural State Machine (NSM) model can be used for realizing robust state-dependent computation on neuromorphic hardware, and we validate it with experimental results obtained from a recently developed multi-neuron multi-core neuromorphic computing architecture.

Abstract

State-dependent computation is one of the main signatures of cognition. Recently, it has been shown how it can be used as a computational primitive in spiking neural networks for constructing complex cognitive behaviors in neuromorphic agents. However, to achieve the desired computations and behaviors in mixed signal analog-digital neuromorphic electronic systems, these computational primitives should be able to cope with noisy and imprecise components, such as silicon neurons and synapses, with noisy and unreliable external signals, and with interference from the environment. Here we present a spiking neural network model that addresses all these issues while exhibiting both analog signal processing properties and digital symbolic computational abilities. We show how this Neural State Machine (NSM) model can be used for realizing robust state-dependent computation on neuromorphic hardware, and we validate it with experimental results obtained from a recently developed multi-neuron multi-core neuromorphic computing architecture.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:21 October 2017
Deposited On:12 Mar 2019 16:52
Last Modified:30 Oct 2019 08:11
Publisher:IEEE
Number of Pages:1
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/BIOCAS.2017.8325075

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