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Searching for Principles of Brain Computation

Wolfgang Maass
doi: https://doi.org/10.1101/094102
Wolfgang Maass
Graz University of Technology, Institute for Theoretical Computer Science,Inffeldgasse 16b/I, A-8010 Graz, Austria; email:
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  • For correspondence: maass@igi.tugraz.at
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Abstract

Highlights

  • Hints for computational principles from experimental data

  • Computational role of diverse network components

  • Emergence and computational role of assemblies

  • Probabilistic inference through stochastic network dynamics

  • Ongoing network rewiring and compensation through synaptic sampling

Abstract Experimental methods in neuroscience, such as calcium-imaging and recordings with multielectrode arrays, are advancing at a rapid pace. They produce insight into the simultaneous activity of large numbers of neurons, and into plasticity processes in the brains of awake and behaving animals. These new data constrain models for neural computation and network plasticity that underlie perception, cognition, behavior, and learning. I will discuss in this short article four such constraints: Inherent recurrent network activity and heterogeneous dynamic properties of neurons and synapses, stereotypical spatio-temporal activity patterns in networks of neurons, high trial-to-trial variability of network responses, and functional stability in spite of permanently ongoing changes in the network. I am proposing that these constraints provide hints to underlying principles of brain computation and learning.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 14, 2016.
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Searching for Principles of Brain Computation
Wolfgang Maass
bioRxiv 094102; doi: https://doi.org/10.1101/094102
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Searching for Principles of Brain Computation
Wolfgang Maass
bioRxiv 094102; doi: https://doi.org/10.1101/094102

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