Brain plasticity plays a critical role in the coordination of brain functions, particularly considering that the hippocampus is one of the most important areas in the brain. This study aims to investigate and analyze the hippocampal pathways using three types of plasticity and two computational methods. In this research, the rules of Hebbian plasticity, homeostatic plasticity, and plasticity dependent on the synchrony of dendritic activity have been used. Python programming language and Nest library are also among the tools of this project.
The first method is using the Leaky Integrate-and-Fire (LIF) model. For this purpose, we have used biological neural networks in which neurons are considered as nodes, synapses as connections, and different types of plasticity as network properties. This method can simulate connectivity among neurons as well as their activity. The second method uses a directed random graph model to simulate only the connectivity of the circuit.
In both simulations, the number of neurons, the excitation and inhibition balance, and the connection probability have been chosen carefully to reflect the real properties of the circuit. Both models show agreement with the connectivity pattern of the hippocampus obtained from experimental studies. The strength of connections matches the experimental results of the hippocampus in the first model. In contrast, due to the lack of activity simulation in the second model, the strength of network synapses differs from experimental results as it is a mathematical model and aims to partially reflect the properties of biological neural networks.