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Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825x
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
2020 (English)In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 14, article id 150Article in journal (Refereed) Published
Abstract [en]

Spiking neural networks are well suited for spatiotemporal feature detection and learning, and naturally involve dynamic delay mechanisms in the synapses, dendrites and axons. Dedicated delay neurons and axonal delay circuits have been considered when implementing such pattern recognition networks in dynamic neuromorphic processors. Inspired by an auditory feature detection circuit in crickets, featuring a delayed excitation by postinhibitory rebound, we investigate disynaptic delay elements formed by inhibitory--excitatory pairs of dynamic synapses. We configured such disynaptic delay elements in the DYNAP-SE neuromorphic processor and characterized the distribution of delayed excitations resulting from device mismatch. Interestingly, we found that the disynaptic delay elements can be configured such that the timing and magnitude of the delayed excitation depend mainly on the efficacy of the inhibitory and excitatory synapses, respectively, and that a neuron with multiple delay elements can be tuned to respond selectively to a specific pattern. Furthermore, we present a network with one disynaptic delay element that mimics the auditory feature detection circuit of crickets, and we demonstrate how varying synaptic weights, input noise and processor temperature affect the circuit. Dynamic delay elements of this kind open up for synapse level temporal feature tuning with configurable delays of up to 100 ms.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2020. Vol. 14, article id 150
Keywords [en]
pattern recognition, Spiking neural network (SNN), Neuromorphic, delay line, embedded intelligence, DYNAP, Insect-inspired computing
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electronic systems
Identifiers
URN: urn:nbn:se:ltu:diva-77666DOI: 10.3389/fnins.2020.00150ISI: 2-s2.0-85082714457PubMedID: 32180698Scopus ID: 2-s2.0-85082714457OAI: oai:DiVA.org:ltu-77666DiVA, id: diva2:1392843
Funder
The Kempe Foundations, JCK-1809The Kempe Foundations, SMK-1429The Swedish Foundation for International Cooperation in Research and Higher Education (STINT), IG2011-2025
Note

Validerad;2020;Nivå 2;2020-04-14 (alebob)

Available from: 2020-02-13 Created: 2020-02-13 Last updated: 2020-04-22Bibliographically approved

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Publisher's full textPubMedScopushttps://www.frontiersin.org/articles/10.3389/fnins.2020.00150

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Sandin, FredrikNilsson, Mattias

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