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We investigate the effect of reciprocal connections in a network of modules of simulated spiking neurons.
A synchronous concurrent algorithm (SCA) is a parallel deterministic algorithm based on a network of modules and channels, computing and communicating data in parallel, and synchronised by a global clock with discrete time.
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Previous works mainly used local signatures to capture interactions between pairs of modules (Bandyopadhyay et al., 2008; Pu et al., 2008) or learn a network of disjoint modules that are coherent in terms of physical and genetic interactions (Ulitsky et al., 2008).
Recently a network of such modules, operating in the alpha range and with additive noise, has been used to investigate mechanisms of cross-frequency coupling between brain areas [68].
We generated a network of 10 modules with 10 nodes each.
Genes and proteins in a cell are often organized as a network of interacting modules (e.g. biological pathways) in order to carry out their biological functions.
We processed the microarray data with our R package and constructed a network of functional modules perturbed in common human diseases.
Thus, metabolism can be considered as a network of metabolic modules, which are spatially compartmentalized to optimize energetic communication between energy-producing and energy-consuming parts of the cell (1).
To understand how the viral proteome interplays with these functional protein modules, we first performed a topological deconvolution of the human protein interaction network into a network of inter-connected modules.
The robot structure that consists of 18 degrees of freedom, is controlled by a network of multiple interconnected modules.
Briefly, the algorithm starts from a network of m fully connected modules, each having M nodes.
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