Implementation of hierarchically coupled tiny network of neurons to discriminate odors - Sunitha, R (Expert systems with applications, 2025)
It goes without saying that the brain is the most intricate system that humans have ever encountered, and it continues to be at the centre of all fundamental research in the twenty-first century. One of the most important research endeavors of modern science is the simulation of the human brain and brain functioning. The subject of investigation of the article is to implement a tiny world of interactive Hodgkin and Huxley (HH) neurons, connected in a hierarchical fashion with feedback and feedforward connections mimicking the olfactory system and comprehending its dynamics. The developed biologically plausible network is designed to learn and strengthen synaptic connections through Hebbian learning using Spike Timing Dependent Plasticity (STDP) algorithm. In this study an optimal HH microscopic model has been incorporated into Freeman’s topology, with the Freeman KO set replaced with a three compartmental HH model of an Olfactory Receptor Neuron (ORN). This small world neuronal network could generate Electroencephalogram (EEG) signals with varied amounts of chaos and was validated using fractal dimension and Lyapunov exponent computed on the available public EEG database as well as on the simulated Freeman K set- generated EEG signals. Further, this small world hierarchical neuronal model is subjected to artificial and natural odorant responses collected from an array of sensors. The time series thus generated is compared with the database acquired from human subjects using the experimental Brain Computer Interface (BCI) setup developed under various odorant conditions.