Impact Factor 2.323 | CiteScore 2.60
More on impact ›

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Comput. Neurosci. | doi: 10.3389/fncom.2019.00051

Analysis of Biased Competition and Cooperation for Attention in the Cerebral Cortex

Tatyana Turova1 and  Edmund Rolls2*
  • 1Mathematics Center, Faculty of Engineering, Lund University, Sweden
  • 2and Department of Computer Science, Oxford Centre for Computational Neuroscience, United Kingdom

A new approach to understanding the interaction between cortical areas is provided by a mathematical analysis of biased competition, which describes many interactions between cortical areas, including those involved in top-down attention. The analysis helps to elucidate the principles of operation of such cortical systems, and in particular the parameter values within which biased competition operates. The analytic results are supported by simulations that illustrate the operation of the system with parameters selected from the analysis. The findings provide a detailed mathematical analysis of the operation of these neural systems with nodes connected by feedforward (bottom-up) and feedback (top-down) connections. The analysis provides the critical value of the top-down attentional bias that enables biased competition to operate for a range of input values to the network, and derives this as a function of all the parameters in the model. The critical value of the top-down bias depends linearly on the value of the other inputs, but the coefficients in the function reveal non-linear relations between the remaining parameters. The results provide reasons why the backprojections should not be very much weaker than the forward connections between two cortical areas. The major advantage of the analytical approach is that it discloses relations between all the parameters of the model. This task cannot be fulfilled purely by numerical simulations.

Keywords: Attention, biased competition model, Cerebral Cortex, Mathematical analysis, top-down connections, neural networks

Received: 08 May 2019; Accepted: 04 Jul 2019.

Edited by:

Paul Miller, Brandeis University, United States

Reviewed by:

Guy Elston, Centre for Cognitive and Systems Neuroscience, Division of Education, Arts and Social Sciences, University of South Australia, Australia
Adam Ponzi, Okinawa Institute of Science and Technology Graduate University, Japan  

Copyright: © 2019 Turova and Rolls. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) 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.

* Correspondence: Prof. Edmund Rolls, Oxford Centre for Computational Neuroscience, and Department of Computer Science, Oxford, CV4 7AL, United Kingdom,