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

Front. Comput. Neurosci. | doi: 10.3389/fncom.2019.00079

Passing the message: representation transfer in modular balanced networks

  • 1Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation(IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1 / INM-10), Julich Research Centre, Germany
  • 2Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Germany
  • 3Department of Data-driven Analysis of Biological Networks, Campus Institute for Dynamics of Biological Networks, University of Göttingen, Germany
  • 4MEG Unit, Brain Imaging Center, Goethe University Frankfurt, Germany
  • 5Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Germany

Neurobiological systems rely on hierarchical and modular architectures to carry out intricate computations using minimal resources. A prerequisite for such systems to operate adequately is the capability to reliably and efficiently transfer information across multiple modules. Here, we study the features enabling a robust transfer of stimulus representations in modular networks of spiking neurons, tuned to operate in a balanced regime. To capitalize on the complex, transient dynamics that such networks exhibit during active processing, we apply reservoir computing principles and probe the systems' computational efficacy with specific tasks. Focusing on the comparison of random feed-forward connectivity and biologically inspired topographic maps, we find that, in a sequential set-up, structured projections between the modules are strictly necessary for information to propagate accurately to deeper modules. Such mappings not only improve computational performance and efficiency, they also reduce response variability, increase robustness against interference effects, and boost memory capacity. We further investigate how information from two separate input streams is integrated and demonstrate that it is more advantageous to perform non-linear computations on the input locally, within a given module, and subsequently transfer the result downstream, rather than transferring intermediate information and performing the computation downstream. Depending on how information is integrated early on in the system, the networks achieve similar task-performance using different strategies, indicating that the dimensionality of the neural responses does not necessarily correlate with nonlinear integration, as predicted by previous studies. These findings highlight a key role of topographic maps in supporting fast, robust and accurate neural communication over longer distances. Given the prevalence of such structural feature, particularly in the sensory systems, elucidating their functional purpose remains an important challenge towards which this work provides relevant, new insights. At the same time, these results shed new light on important requirements for designing functional hierarchical spiking networks.

Keywords: modularity, information transfer, spiking neural networks, topographic maps, Reservoir computing (RC)

Received: 19 Aug 2019; Accepted: 29 Oct 2019.

Copyright: © 2019 Zajzon, Mahmoudian, Morrison and Duarte. 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: Mx. Barna Zajzon, Julich Research Centre, Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation(IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1 / INM-10), Jülich, Germany,