Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

PloS One
Christian AlbersKlaus Pawelzik

Abstract

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in exp...Continue Reading

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Citations

Aug 18, 2016·PloS One·Brian Gardner, André Grüning
Apr 14, 2018·Neural Computation·Friedemann Zenke, Surya Ganguli
Nov 28, 2019·Physical Review. E·Paul ManzRaoul-Martin Memmesheimer
Aug 1, 2020·Frontiers in Computational Neuroscience·Hubert Löffler, Daya Shankar Gupta
Apr 30, 2021·Frontiers in Computational Neuroscience·Brian Gardner, André Grüning
Jun 29, 2021·Frontiers in Neuroinformatics·Jonas StapmannsDavid Dahmen

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Software Mentioned

Python
ReSuMe
Learning
Tempotron
Perceptron
HTP
Matlab
Readme
PBSNLR
MPDP

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