id:"swepub:oai:DiVA.org:kth-165806"
Search: id:"swepub:oai:DiVA.org:kth-165806" > Synaptic and nonsyn...
- 1 of 1
- Previous record
- Next record
- To hitlist
Synaptic and nonsynaptic plasticity approximating probabilistic inference
-
- Tully, Philip, 1988- (author)
- KTH,Beräkningsbiologi, CB,Stockholm Brain Institute, Stockholm; University of Edinburgh
-
- Hennig, Matthias (author)
- University of Edinburgh
-
- Lansner, Anders (author)
- KTH,Beräkningsbiologi, CB,Stockholm Brain Institute, Stockholm; Stockholm University
- (creator_code:org_t)
- 2014-04-08
- 2014
- English.
- In: Frontiers in Synaptic Neuroscience. - : Frontiers Media SA. - 1663-3563. ; 6:APR
- Journal article (peer-reviewed)
Abstract
Subject headings
Close
- 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.
Keyword
- Bayes' rule
- synaptic plasticity and memory modeling
- intrinsic excitability
- naïve Bayes classifier
- spiking neural networks
- Hebbian learning
- Computer Science
- Datalogi
- Teoretisk kemi och biologi
- Theoretical Chemistry and Biology
- Biological Physics
- Biologisk fysik
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
- Frontiers in Synaptic Neuroscience (Search for host publication in LIBRIS)
To the university's database
- 1 of 1
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Tully, Philip, 1 ...
- Hennig, Matthias
- Lansner, Anders
- Articles in the publication
- Frontiers in Syn ...
- By the university
- Royal Institute of Technology
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
