Detection of patterns of simulated bioelectric activity and modeling of bioinspired neural networkswith genetic expression
Modular architecture is a hallmark of many brain circuits. Particularly, in the cerebral cortex it has been observed that reciprocal connections are often present between functionally interconnected areas that are hierarchically organized. Evolutionary development is another distinctive characteristic of living species, even the simplest viruses are capable to adapt to better ﬁt new environmental conditions. Having hierarchical architectures and evolutionary features in mind, we build unique and novel simulation framework, which allows us to model and to study evolving hierarchically organized circuits of modules of spiking neural networks. Each module is characterized by embedded neural development and expression of spike timing dependent plasticity. Cell death, synaptic plasticity and projection pruning, embedded in the neural model, drive the build-up of auto-associative links within each module, which generate an areal activity that reﬂect the changes in the corresponding functional connectivity within and between neuronal modules. Bio-electric activity of each module is recorded by means of virtual electrodes and these signals, called electrochipograms (EChG), are analyzed by time and frequency domain methods in order to ﬁnd general patterns of emerging behavior. Beside time and frequency domain analysis methods, a novel robust non-linear structural regression approach is proposed to provide researchers with more powerful tools specially adapted to the data typically used in the domain. We tested the eﬀect of an external stimulus at ﬁxed frequency fed to a sensory module, which pro jecting its activity to two hierarchically organized parallel pathways. We found that modeled circuits manifest behavior similar in certain aspects to that of real brains. We show evidence that all networks of modules are able to maintain long patterns of activity associated with the stimulus oﬀset. These ﬁndings bring new insights to the understanding of EEG-like signals, both real and virtual. The ﬁndings prove that the approach is successful and could be extended to model cognitive and behavioral processes in the brains.