Journal Article

FZJ-2023-03209

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A refined information processing capacity metric allows an in-depth analysis of memory and nonlinearity trade-offs in neurocomputational systems

Schulte to Brinke, T. (Corresponding author)FZJ*RWTH* ; Dick, M.FZJ*RWTH* ; Duarte, R.Extern* ; Morrison, A.FZJ*RWTH*

2023
Macmillan Publishers Limited, part of Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41598-023-37604-0  doi:10.34734/FZJ-2023-03209

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.

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Contributing Institute(s):

1. Computational and Systems Neuroscience (INM-6)
2. Theoretical Neuroscience (IAS-6)
3. Jara-Institut Brain structure-function relationships (INM-10)

Research Program(s):

1. 5232 - Computational Principles (POF4-523) (POF4-523)
2. ACA - Advanced Computing Architectures (SO-092) (SO-092)
3. SDS005 - Towards an integrated data science of complex natural systems (PF-JARA-SDS005) (PF-JARA-SDS005)
4. DFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487) (491111487)

Appears in the scientific report 2023

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Database coverage:
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