Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/27028
Title: Spatiotemporal dynamics in spiking recurrent neural networks using modified-full-FORCE on EEG signals
Authors: Ioannides, Georgios 
Kourouklides, Ioannis 
Astolfi, Alessandro 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Behavior;Brain;Cognition;Electroencephalography;Neural Networks;Neurons;Perception
Issue Date: 21-Feb-2022
Source: Scientific Reports, 2022, vol. 12, articl. no. 2896
Volume: 12
Journal: Scientific Reports 
Abstract: Methods on modelling the human brain as a Complex System have increased remarkably in the literature as researchers seek to understand the underlying foundations behind cognition, behaviour, and perception. Computational methods, especially Graph Theory-based methods, have recently contributed significantly in understanding the wiring connectivity of the brain, modelling it as a set of nodes connected by edges. Therefore, the brain's spatiotemporal dynamics can be holistically studied by considering a network, which consists of many neurons, represented by nodes. Various models have been proposed for modelling such neurons. A recently proposed method in training such networks, called full-Force, produces networks that perform tasks with fewer neurons and greater noise robustness than previous least-squares approaches (i.e. FORCE method). In this paper, the first direct applicability of a variant of the full-Force method to biologically-motivated Spiking RNNs (SRNNs) is demonstrated. The SRNN is a graph consisting of modules. Each module is modelled as a Small-World Network (SWN), which is a specific type of a biologically-plausible graph. So, the first direct applicability of a variant of the full-Force method to modular SWNs is demonstrated, evaluated through regression and information theoretic metrics. For the first time, the aforementioned method is applied to spiking neuron models and trained on various real-life Electroencephalography (EEG) signals. To the best of the authors' knowledge, all the contributions of this paper are novel. Results show that trained SRNNs match EEG signals almost perfectly, while network dynamics can mimic the target dynamics. This demonstrates that the holistic setup of the network model and the neuron model which are both more biologically plausible than previous work, can be tuned into real biological signal dynamics.
URI: https://ktisis.cut.ac.cy/handle/10488/27028
ISSN: 2045-2322
DOI: 10.1038/s41598-022-06573-1
Rights: © The Author(s). Open Access Tis article is licensed under a Creative Commons Attribution 4.0 International License
Type: Article
Affiliation : Imperial College London 
Cyprus University of Technology 
Appears in Collections:Άρθρα/Articles

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This item is licensed under a Creative Commons License Creative Commons