Improvement of the Izhikevich model based on the rat basolateral amygdala and hippocampus neurons, and recognition of their possible firing patterns

Abstract

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. Furthermore, it is important to be capable of modeling the potential neural activities to build precise artificial neural networks. The Izhikevich model is one of the simplest biologically-plausible models, i.e. capable of capturing 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 with the neuronal activity of 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 Wistar 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. In this algorithm, optimization tools, like crossover and mutation, provide the basis for generating model parameters populations. 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 networks 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.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…Complex spikes can also be met in the thalamus, and spike sorting is just a way to learn about its electrophysiological properties (Pastor and Vega-Zelaya, 2020 ). In the subthalamic nucleus, clustering of spike trains may help to understand the pathophysiology of movement disorders (Kaku et al, 2019 ; Sukiban et al, 2019 ), while the basolateral amygdala or the hippocampus can offer ideas about general neural interactions (Hojjatinia et al, 2020 ; Oghazian et al, 2020 ). Correlations calculated after vigorous spike sorting in multichannel data gave rise to a promising neural encoding capacity hypothesis (Isbister et al, 2021 ) and neural populational activity dynamics (Theilman et al, 2021 ).…”

A Computational Model Integrating Multiple Phenomena on Cued Fear Conditioning, Extinction, and Reinstatement

“…Concerning the second issue, testing different combinations of connectivity schemes would require, for each one, a parameter search and a following sensitivity analysis. In order to account for these issues is necessary an automatic methodology of parameter fitting, namely a genetic algorithm (Hojjatinia et al, 2020 ) or a brute-force search (Oliva et al, 2018 ). This different methodological approach could be the argument for a future work, allowing to analyze more complex behaviors, such as fear renewal (Ji and Maren, 2005 ; Zelikowsky et al, 2012 ) or freezing/flight behavior (Fadok et al, 2017 ).…”

Comparison of Different Spike Sorting Subtechniques Based on Rat Brain Basolateral Amygdala Neuronal Activity

“…Extracellular firing activity data was recorded from the BLA of male Wistar rat brain [22]. The rat was housed in Animal Care Facility maintained at 23 ± 1○C on a 12:12 h light/dark cycle.…”