Computational neurogenetic modeling: integration of spiking neural networks, gene networks, and signal processing techniques

The paper presents a theory and a new generic computational model of a biologically plausible artificial neural network (ANN) that can mimic certain brain neuronal ensembles, the dynamics of which is influenced by the dynamics of internal gene regulatory networks (GRN). We call these models "computational neurogenetic models" (CNGM) and this new area of research computational neurogenetics. We are aiming at developing a novel computational modeling paradigm and also at bringing original insights into how genes and their interactions influence the function of brain neural networks in normal and diseased states. Both brain activity and an ANN model can be analyzed using same signal processing techniques and then compared. In the proposed model, FFT and spectral characteristics of the ANN behavior are analyzed and compared with the brain EEG signal. The model will include a large set of biologically plausible parameters and functions related to genes/proteins, spiking neuronal activities, etc., which define the GRN and the corresponding ANN. These parameters will be optimized, based for instance on targeted EEG data, through using evolutionary algorithms. The paper also offers a list of open questions in the field of CNGM. It outlines directions for further research.