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.
Highlights
No matter how surprising this might sound, after more than a century of Neuroscience research, researchers still do not comprehend many of the fundamental principles by which the brain controls bodily movements, stores episodic memories and makes plans
In order to examine whether or not a network does satisfy the small-worldness property, three Graph Theoretic metrics are calculated across a range of intra-cluster rewiring probabilities, β0
It should be noted that Small-World Network (SWN) can exhibit short-term memory, which is the ability to hold information between different states in a Dynamical System
Summary
No matter how surprising this might sound, after more than a century of Neuroscience research, researchers still do not comprehend many of the fundamental principles by which the brain controls bodily movements, stores episodic memories and makes plans. At the network-level, Clopath et al.[27], proposed the use of an adjacency matrix (i.e. the matrix which portrays the connectivity and synaptic strengths between neurons) that is a sparse matrix with no additional constraints that are biologically motivated They used a simulated target signal of a simplistic sinusoidal waveform, which represents a single musical note and stands for bird sounds. More recently in28, Synapse Time Dependent Plasticity (STDP) has been used as a mean of converging synaptic strengths between connected neurons While this makes their approach more biologically-plausible than previous a ttempts[27], there is no formal metric being mentioned assessing this biological plausibility (e.g. through the Small-Worldness Index or the dynamical complexity that aims to measure the c onsciousness[29]). Since biological plausibility remains the key to constructing a CBM, there appear to be a lot of areas of improvement in order to propose a framework which is more realistic and more biologically plausible
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