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Synaptic Integration of Spatiotemporal Features with a Dynamic Neuromorphic Processor
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825x
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Spiking neurons can perform spatiotemporal feature detection by nonlinear synaptic and dendritic integration of presynaptic spike patterns. Multicompartment models of nonlinear dendrites and related neuromorphic circuit designs enable faithful imitation of such dynamic integration processes, but these approaches are also associated with a relatively high computing cost or circuit size. Here, we investigate synaptic integration of spatiotemporal spike patterns with multiple dynamic synapses on point-neurons in the DYNAP-SE neuromorphic processor, which can offer a complementary resource-efficient, albeit less flexible, approach to feature detection. We investigate how previously proposed excitatory--inhibitory pairs of dynamic synapses can be combined to integrate multiple inputs, and we generalize that concept to a case in which one inhibitory synapse is combined with multiple excitatory synapses. We characterize the resulting delayed excitatory postsynaptic potentials (EPSPs) by measuring and analyzing the membrane potentials of the neuromorphic neuronal circuits. We find that biologically relevant EPSP delays, with variability of order 10 milliseconds per neuron, can be realized in the proposed manner by selecting different synapse combinations, thanks to device mismatch. Based on these results, we demonstrate that a single point-neuron with dynamic synapses in the DYNAP-SE can respond selectively to presynaptic spikes with a particular spatiotemporal structure, which enables, for instance, visual feature tuning of single neurons.

Keywords [en]
Spiking Neural Networks, Neuromorphic, Spatiotemporal, Feature Detection, Synaptic and Dendritic Integration, DYNAP
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electronic systems
Identifiers
URN: urn:nbn:se:ltu:diva-77686OAI: oai:DiVA.org:ltu-77686DiVA, id: diva2:1392851
Funder
The Kempe Foundations, JCK-1809, SMK-1429The Swedish Foundation for International Cooperation in Research and Higher Education (STINT), IG2011-2025Available from: 2020-02-13 Created: 2020-02-13 Last updated: 2020-02-13

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https://arxiv.org/abs/2002.04924

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Nilsson, MattiasLiwicki, FoteiniSandin, Fredrik

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