Vascular Dynamics Aid a Coupled Neurovascular Network Learn Sparse Independent Features: A Computational Model

Philips, R.T., Chhabria, K. and Chakravarthy, V.S. (2016) Vascular Dynamics Aid a Coupled Neurovascular Network Learn Sparse Independent Features: A Computational Model. Frontiers in Neural Circuits, 10.

Abstract

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Authors/Creators:
  • Philips, R.T.
  • Chhabria, K.
  • Chakravarthy, V.S.
Copyright, Publisher and Additional Information: © 2016 Philips, Chhabria and Chakravarthy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY)(https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: desynchronized vascular dynamics; vasomotion; vascular driven neural computation; neuronal demand; error estimating neurons; predictive coding
Dates:
  • Published: 26 February 2016
  • Accepted: 2 February 2016
  • Published (online): 26 February 2016
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Cardiovascular Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 19 Jul 2016 10:24
Last Modified: 19 Jul 2016 10:24
Published Version: http://dx.doi.org/10.3389/fncir.2016.00007
Status: Published
Publisher: Frontiers Media
Refereed: Yes
Identification Number: https://doi.org/10.3389/fncir.2016.00007

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