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Synaptic and nonsynaptic plasticity approximating probabilistic inference
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm Brain Institute, Stockholm; University of Edinburgh.ORCID iD: 0000-0001-8796-3237
University of Edinburgh.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm Brain Institute, Stockholm; Stockholm University.
2014 (English)In: Frontiers in Synaptic Neuroscience, ISSN 1663-3563, Vol. 6, no APR, 8Article in journal (Refereed) Published
Abstract [en]

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert.

Place, publisher, year, edition, pages
2014. Vol. 6, no APR, 8
Keyword [en]
Bayes' rule, synaptic plasticity and memory modeling, intrinsic excitability, naïve Bayes classifier, spiking neural networks, Hebbian learning
National Category
Natural Sciences Medical and Health Sciences
Research subject
Computer Science; Theoretical Chemistry and Biology; Biological Physics
URN: urn:nbn:se:kth:diva-165806DOI: 10.3389/fnsyn.2014.00008ScopusID: 2-s2.0-84904738936OAI: diva2:808843
BrainScaleSErasmus Mundus EuroSPIN
VINNOVAEU, FP7, Seventh Framework Programme, EU-FP7-FET-269921Swedish National Infrastructure for Computing (SNIC)Swedish Research Council, VR-621-2009-3807

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.

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Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2015-06-23Bibliographically approved

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