A refined information processing capacity metric allows an in-depth analysis of memory and nonlinearity trade-offs in neurocomputational systems
Scientific Reports, vol. 13, pp. 10517
Abstract
Since dynamical systems are an integral part of many scientific domains and can be inherently computational, analyses that reveal in detail the functions they compute can provide the basis for far-reaching advances in various disciplines. One metric that enables such analysis is the information processing capacity. This method not only provides us with information about the complexity of a system's computations in an interpretable form, but also indicates its different processing modes with different requirements on memory and nonlinearity. In this paper, we provide a guideline for adapting the application of this metric to continuous-time systems in general and spiking neural networks in particular. We investigate ways to operate the networks deterministically to prevent the negative effects of randomness on their capacity. Finally, we present a method to remove the restriction to linearly encoded input signals. This allows the separate analysis of components within complex systems, such as areas within large brain models, without the need to adapt their naturally occurring inputs.
Authors 4
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Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University
Affiliation as printed
Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany. t.schulte.to.brinke@fz-juelich.de
Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, 52425, Jülich, Germany. t.schulte.to.brinke@fz-juelich.de
Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, Jülich, Germany
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Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University
Affiliation as printed
Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, 52425, Jülich, Germany
Peter Grünberg Institut (PGI-1) and Institute for Advanced Simulation (IAS-1), Jülich Research Centre, 52425, Jülich, Germany
Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, Jülich, Germany
Peter Grünberg Institut (PGI-1) and Institute for Advanced Simulation (IAS-1), Jülich Research Centre, Jülich, Germany
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Radboud University Nijmegen · Donders Institute for Brain, Cognition and Behaviour
Affiliation as printed
Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
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Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University
Affiliation as printed
Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, 52425, Jülich, Germany
Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, Jülich, Germany
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