  Introduction- Identifying the potential firing patterns following different
brain regions under normal and abnormal conditions increases our understanding
of events at the level of neural interactions in the brain. The Izhikevich
model is one of the simplest biologically plausible models, i.e. capable of
capturing the most recognized firing patterns of neurons. This property makes
the model efficient in simulating the large-scale networks of neurons.
Improving the Izhikevich model for adapting to the neuronal activity of the rat
brain with great accuracy would make the model effective for future neural
network implementations. Methods- Data sampling from two brain regions, the HIP
and BLA, was performed by the extracellular recordings of male rats, and spike
sorting was conducted by Plexon offline sorter. Further analyses were performed
through NeuroExplorer and MATLAB. To optimize the Izhikevich model parameters,
a genetic algorithm was used. The process of comparison in each iteration leads
to the survival of better populations until achieving the optimum solution.
Results- In the present study, the possible firing patterns of the real single
neurons of the HIP and BLA were identified. Additionally, an improved
Izhikevich model was achieved. Accordingly, the real neuronal spiking pattern
of these regions neurons and the corresponding cases of the Izhikevich neuron
spiking pattern were adjusted with great accuracy. Conclusion- This study was
conducted to elevate our knowledge of neural interactions in different
structures of the brain and accelerate the quality of future large-scale neural
network simulations, as well as reducing the modeling complexity. This aim was
achievable by performing the improved Izhikevich model, and inserting only the
plausible firing patterns, and eliminating unrealistic ones.
