Free Energy and Dendritic Self-Organization
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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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Active Inference has been applied to synaptic remodelling, by considering the minimization of prediction errors at the level of the dendritic tree.
“Indeed, Active Inference has even been applied to synaptic remodelling, by considering the minimization of prediction errors at the level of the dendritic tree (Kiebel and Friston, 2011).”
From Active Inference, homeostatic regulation and adaptive behavioural control (2015) · cites this paper as Kiebel and Friston, 2011
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References 43 · Cited by 68
Research works printed but not linked: 27
- Branco T. Clark B. A. Hausser M. (2010). Dendritic discrimination of temporal input sequences in cortical neurons. Science 329, 1671– 1675 10.1126/science.1189664 20705816 PMC6354899 · DOI · Paper page
- Daunizeau J. Friston K. J. Kiebel S. J. (2009). Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models. Physica D 238, 2089– 2118 10.1016/j.physd.2009.08.002 19862351 PMC2767160 · DOI
- Dayan P. Abbott L. F. (2005). Theoretical Neuroscience. Cambridge, MA: The MIT Press
- Destexhe A. (2010). Dendrites do it in sequences. Science 329, 1611– 1612 10.1126/science.1196743 20929837 · DOI
- Destexhe A. Huguenard J. R. (2000). Nonlinear thermodynamic models of voltage-dependent currents. J. Comput. Neurosci. 9, 259– 270 11139042
- Friston K. (2005). A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 815– 836 15937014 PMC1569488
- Friston K. (2010). Is the free-energy principle neurocentric? Nat. Rev. Neurosci. 11, 607– 608 20631713
- Friston K. Kiebel S. (2009). Predictive coding under the free-energy principle. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 1211– 1221 19528002 PMC2666703
- Friston K. Kilner J. Harrison L. (2006). A free energy principle for the brain. J. Physiol. Paris 100, 70– 87 10.1016/j.jphysparis.2006.10.001 17097864 · DOI · Paper page
- Friston K. Penny W. (2011). Post hoc Bayesian model selection. Neuroimage 56, 2089– 2099 10.1016/j.neuroimage.2011.03.062 21459150 PMC3112494 · DOI
- Friston K. J. (2009). Modalities, modes, and models in functional neuroimaging. Science 326, 399– 403 10.1126/science.1174521 19833961 · DOI · Paper page
- Friston K. J. Dolan R. J. (2010). Computational and dynamic models in neuroimaging. Neuroimage 52, 752– 765 10.1016/j.neuroimage.2009.12.068 20036335 PMC2910283 · DOI · Paper page
- Gulledge A. T. Kampa B. M. Stuart G. J. (2005). Synaptic integration in dendritic trees. J. Neurobiol. 64, 75– 90 10.1002/neu.20144 15884003 · DOI
- Gutig R. Sompolinsky H. (2006). The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9, 420– 428 16474393
- Hertz J. Krogh A. Palmer R. G. (1991). Introduction to the Theory of Neural Computation. Redwood City, CA: Addison-Wesley Publishing Company
- Herz A. V. M. Gollisch T. Machens C. K. Jaeger D. (2006). Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science 314, 80– 85 10.1126/science.1127240 17023649 · DOI
- Hinton G. E. van Camp D. (1993). “Keeping the neural networks simple by minimizing the description length of the weights,” in COLT ‘93 Proceedings of the Sixth Annual Conference on Computational Learning Theory, 5– 13
- Katz L. C. Shatz C. J. (1996). Synaptic activity and the construction of cortical circuits. Science 274, 1133– 1138 10.1126/science.274.5290.1133 8895456 · DOI · Paper page
- Kiebel S. J. Garrido M. I. Moran R. Chen C. C. Friston K. J. (2009a). Dynamic causal modeling for EEG and MEG. Hum. Brain Mapp. 30, 1866– 1876 19360734 PMC6870752
- Kiebel S. J. von Kriegstein K. Daunizeau J. Friston K. J. (2009b). Recognizing sequences of sequences. PLoS Comput. Biol. 5, e1000464 10.1371/journal.pcbi.1000464 19680429 PMC2714976 · DOI
- Lendvai B. Stern E. A. Chen B. Svoboda K. (2000). Experience-dependent plasticity of dendritic spines in the developing rat barrel cortex in vivo. Nature 404, 876– 881 10.1038/35009107 10786794 · DOI · Paper page
- London M. Hausser M. (2005). Dendritic computation. Annu. Rev. Neurosci. 28, 503– 532 16033324
- Mel B. W. (2008). “Why have dendrites? A computational perspective,” in Dendrites, eds Stuart G. Spruston N. Hausser M. (New York: Oxford University Press), 421– 440
- Pienkowski M. Shaw G. Eggermont J. J. (2009). Wiener-Volterra characterization of neurons in primary auditory cortex using Poisson-distributed impulse train inputs. J. Neurophysiol. 101, 3031– 3041 10.1152/jn.91242.2008 19321635 PMC2694119 · DOI
- Spruston N. (2008). Pyramidal neurons: dendritic structure and synaptic integration. Nat. Rev. Neurosci. 9, 206– 221 18270515
- Torben-Nielsen B. Stiefel K. M. (2009). Systematic mapping between dendritic function and structure. Network 20, 69– 105 10.1080/09548980902984833 19568982 · DOI
- Vetter P. Roth A. Hausser M. (2001). Propagation of action potentials in dendrites depends on dendritic morphology. J. Neurophysiol. 85, 926– 937 11160523
- Authors
- Kiebel, S. J., & Friston, K. J.
- Affiliations
- Max Planck Society (DE)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Kiebel, Stefan J.
- Venue
- Frontiers in Systems Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2011
- Volume
- 5
- Pages
- 80-80
- ISSN
- 1662-5137 (ISSN-L)
- PMC
- PMC3190184
- OpenAlex
- W1981905332
- Cited by
- 68
- References
- 43
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 7 · 2023: 4 · 2022: 6 · 2021: 5 · 2020: 1 · 2019: 7 · 2018: 3 · 2017: 2 · 2016: 3 · 2015: 4 · 2014: 5 · 2013: 4 · 2012: 7
- FWCI
- 2.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Systems Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Funders
- Wellcome Trust (088130)
- Max-Planck-Gesellschaft
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- free energy principle, dendritic spines, surprise minimization, Bayesian model selection, automatic relevance determination, generative models, neural information processing
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