Introduction: Identifying the potential firing patterns following by different brain regions under normal and abnormal conditions increases our understanding of what is happening in the level of neural interactions in the brain. On the other hand, it is important to be capable of modeling the potential neural activities, in order to build precise artificial neural networks. The Izhikevich model is one of the simple biologically plausible models that is capable of capturing the most known firing patterns of neurons. This property makes the model efficient in simulating  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, is performed by extracellular recordings of male Wistar rats and spike sorting is done using Plexon offline sorter. Further data analyses are done through NeuroExplorer and MATLAB software. In order to optimize the Izhikevich model parameters, the genetic algorithm is used. Results: In the present study, the possible firing patterns of the real single neurons of the HIP and BLA are identified. Additionally, improvement of the Izhikevich model is achieved. As a result , the real neuronal spiking pattern of these regions neurons , and the corresponding cases of the Izhikevich neuron spiking pattern are adjusted with great accuracy. Conclusion: This study is 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 is achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns  and eliminating unrealistic ones , as the results of this study .
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 .
R
Introduction:
Introduction-
D
by
R
what is happening in
events at
D
On the other hand, it is important to be capable of modeling the potential neural activities, in order to build precise artificial neural networks.
R
simple
simplest
R
that is
, i.e.
R
known
recognized
A
the
R
with
to
A
the
R
Methods:
Methods-
R
is performed by
was performed by the
R
Wistar rats
rats,
R
is done using
was conducted by
R
data analyses are done
analyses were performed
R
software. In order to
. To
R
the genetic algorithm is used. Results:
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-
R
are
were
R
improvement of the Izhikevich model is achieved. As a result
an improved Izhikevich model was achieved. Accordingly
D
,
R
are
were
R
Conclusion: This study is
Conclusion- This study was
R
large scale neural networks
large-scale neural network
R
is
was
A
,
D
, as the results of this study