EEG Signal Processing and Machine Learning. Saeid Sanei

examples of these models are as follows. The electronic neuron model developed by [62] is realized with integrated circuit technology. The circuit includes one neuron with eight synapses. The chip area of the integrated circuit is 4.5 × 5 mm². The array contains about 200 NPN and 100 PNP transistors, and about 200 of them are used. In 1991, Mahowald and Douglas [63] published an integrated circuit realization of electronic neuron model. The model was realized with complementary metal oxide semiconductor (CMOS) circuits using very large‐scale integrated (VLSI) technology. Their model accurately simulates the spikes of a neocortical neuron. The power dissipation of the circuit is 60 μW, and it occupies less than 0.1 mm². It is estimated that 100–200 such neurons could be fabricated on a 1 cm × 1 cm die.

3.6 Dynamic Modelling of Neuron Action Potential Threshold

      Neuron threshold is the transmembrane voltage level at which any further depolarization will activate a sufficient number of sodium channels to enter a self‐generative positive feedback phase [64]. This threshold is often considered constant. Any alteration to the threshold influences the neuron spike train temporal transformation. There are however evidences that the threshold is nonlinearly affected by the AP firing history [65–67]. In [64] a method for dynamically varying the threshold for intercellular activity has been proposed. The method is suitable for systems with spikes in both their inputs and outputs.

3.7 Summary

      Most of the models generated mathematically are very primitive representations of EEG generators. More complicated models will be necessary to represent the brain EPs and the abnormal EEGs recorded under various brain diseases and disorders. The model based on phase coupling explained here introduces the WCOs which can be used to model the interactions between the neurons. Hodgkin and Huxley model conversely provides a detailed and accurate model for generation of active potentials. Linear and nonlinear prediction filers can be used in modelling the neuro generators. Gaussian mixtures are capable in modelling the EEG particularly event‐related, evoked, and movement‐related potentials. Finally, circuit models have been introduced to combine the excitatory and inhibitory post‐synaptic potentials for generation of an EEG signal. The synaptic currents have also been modelled using electronic circuits. These circuits can be expanded to have very accurate model of either a single neuron or a membrane. They can also be used to model a large number of neurons for generation of particular brain waveforms. The electronic models have potential to be used in brain morphing technology.

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