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  Free energy and dendritic self-organisation

Kiebel, S. J., & Friston, K. J. (2011). Free energy and dendritic self-organisation. Frontiers in Systems Neuroscience, 5: 80. doi:10.3389/fnsys.2011.00080.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0012-15C9-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-4CAF-5
Genre: Journal Article

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 Creators:
Kiebel, Stefan J.1, Author              
Friston, Karl J.2, Author
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom, ou_persistent22              

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Free keywords: Single neuron; Dendrite; Dendritic computation; Bayesian inference; Free energy; Non-linear dynamical system; Multi-scale; Synaptic reconfiguration
 Abstract: In this paper, we pursue recent observations that, through selective dendritic filtering, single neurons respond to specific sequences of presynaptic inputs. We try to provide a principled and mechanistic account of this selectivity by applying a recent free-energy principle to a dendrite that is immersed in its neuropil or environment. We assume that neurons self-organize to minimize a variational free-energy bound on the self-information or surprise of presynaptic inputs that are sampled. We model this as a selective pruning of dendritic spines that are expressed on a dendritic branch. This pruning occurs when postsynaptic gain falls below a threshold. Crucially, postsynaptic gain is itself optimized with respect to free energy. Pruning suppresses free energy as the dendrite selects presynaptic signals that conform to its expectations, specified by a generative model implicit in its intracellular kinetics. Not only does this provide a principled account of how neurons organize and selectively sample the myriad of potential presynaptic inputs they are exposed to, but it also connects the optimization of elemental neuronal (dendritic) processing to generic (surprise or evidence-based) schemes in statistics and machine learning, such as Bayesian model selection and automatic relevance determination.

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Language(s): eng - English
 Dates: 2011-072011-09-062011-10-11
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.3389/fnsys.2011.00080
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Title: Frontiers in Systems Neuroscience
  Abbreviation : Front Syst Neurosci
Source Genre: Journal
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Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 5 Sequence Number: 80 Start / End Page: - Identifier: ISSN: 1662-5137
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5137