Excitability changes that complement Hebbian learning

Network : Computation in Neural Systems
Maia K Janowitz, Mark C W van Rossum

Abstract

Experiments have shown that the intrinsic excitability of neurons is not constant, but varies with physiological stimulation and during various learning paradigms. We study a model of Hebbian synaptic plasticity which is supplemented with intrinsic excitability changes. The excitability changes transcend time delays and provide a memory trace. Periods of selective enhanced excitability can thus assist in forming associations between temporally separated events, such as occur in trace conditioning. We demonstrate that simple bidirectional networks with excitability changes can learn trace conditioning paradigms.

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Citations

Mar 14, 2007·Neural Computation·Jochen Triesch
Nov 6, 2009·Trends in Neurosciences·Riccardo Mozzachiodi, John H Byrne
May 2, 2014·Frontiers in Synaptic Neuroscience·Philip J TullyAnders Lansner
Jul 15, 2015·PLoS Computational Biology·Daniel HarnackMark C W van Rossum
May 19, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Nikos KomodakisGeorgios Tziritas
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Alexander BauerMarius Kloft
Dec 29, 2017·The Neuroscientist : a Review Journal Bringing Neurobiology, Neurology and Psychiatry·Melissa S Haley, Arianna Maffei
Apr 9, 2008·Physiological Reviews·P Jesper SjöströmMichael Häusser

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