This repository contains projects and reports for a graduate-level computational neuroscience course at the University of Tehran.
The repository includes the following topics:
-
Leaky Integrate-and-Fire Neuron Models: Python implementation and analysis of the LIF neuron model and its improved versions including Exponential LIF and Adaptive Exponential LIF.
-
Neural Populations: Simulates and analyzes different connectivity schemes in spiking neural networks.
-
Neural Encoding: Implements Time-to-First-Spike encoding algorithm.
-
Learning: Models STDP and Reward-Modualted STDP learning rules and resulting weight changes in spiking neural networks.
The code for each topic is contained in its own subdirectory along with a report PDF describing the background and presenting results.
To run the code, install PymoNNtorch and execute the Python scripts. Parameters can be configured in code.
These projects were completed as part of a graduate course on computational neuroscience methods and models taught by Dr. Ganjtabesh at the University of Tehran.
Topics covered neural biophysics, synaptic plasticity, network connectivity, dynamics, and learning. The course combined theoretical foundations with practical modeling to gain insights into brain computation.