Synaptic Integration of Spatiotemporal Features with a Dynamic Neuromorphic Processor

Conference Proceedings

Document identifier: oai:DiVA.org:ltu-77686
Access full text here:10.1109/IJCNN48605.2020.9207210
Keyword: Engineering and Technology, Spiking Neural Networks, Maskininlärning, Cyber-Physical Systems, Cyberfysiska system, DYNAP, Synaptic and Dendritic Integration, Feature Detection, Spatiotemporal, Neuromorphic, Datavetenskap (datalogi), Electrical Engineering, Electronic Engineering, Information Engineering, Data- och informationsvetenskap, Naturvetenskap, Computer Sciences, Computer and Information Sciences, Natural Sciences, Annan elektroteknik och elektronik, Elektroteknik och elektronik, Teknik och teknologier, Other Electrical Engineering, Electronic Engineering, Information Engineering, Machine Learning
Publication year: 2020
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SDG 3 Good health and wellbeing
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Abstract:

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 offers 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.

Authors

Mattias Nilsson

Luleå tekniska universitet; EISLAB
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Foteini Liwicki

Luleå tekniska universitet; EISLAB
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Fredrik Sandin

Luleå tekniska universitet; EISLAB
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