Preprint FZJ-2016-00421

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Inferences from a network to a subnetwork and vice versa under an assumption of symmetry

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2015

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Abstract: This note summarizes some mathematical relations between the probability distributions for the states of a network of binary unitsand a subnetwork thereof, under an assumption of symmetry. These relations are standard results of probability theory, but seem to be rarely used in neuroscience. Some of their consequences for inferences between network and subnetwork, especially in connection with the maximum-entropy principle, are briefly discussed. The meanings and applicability of the assumption of symmetry are also discussed.


Note: bioRxiv preprint

Contributing Institute(s):
  1. Computational and Systems Neuroscience (INM-6)
  2. Theoretical Neuroscience (IAS-6)
  3. JARA-BRAIN (JARA-BRAIN)
Research Program(s):
  1. 574 - Theory, modelling and simulation (POF3-574) (POF3-574)
  2. 571 - Connectivity and Activity (POF3-571) (POF3-571)
  3. MSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018) (HGF-SMHB-2014-2018)
  4. HBP - The Human Brain Project (604102) (604102)
  5. SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) (HGF-SMHB-2013-2017)

Appears in the scientific report 2015
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Creative Commons Attribution CC BY 4.0 ; OpenAccess
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 Record created 2016-01-13, last modified 2024-03-13


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