Computational Neuroscience Projects

This repository contains a collection of Python implementations focusing on neural signal processing, receptive field modeling, and network dynamics. These projects utilize computational methods to simulate and analyze biological neural systems.

📂 Project Modules

1. Neural Response Analysis

Focus: Stochastic Modeling & Spike Train Analysis

• Implemented Poisson processes to generate synthetic spike trains.
• Analyzed variability in neural firing using Fano Factor calculations across 1,000 trials.
• Computed Spike-Triggered Averages (STA) to characterize the neural response to stimuli.

2. Visual Receptive Field Modeling

Focus: System Identification & Linear Kernel Estimation

• Modeled the firing rate of Simple and Complex cells in the primary visual cortex.
• Implemented Reverse Correlation techniques to estimate spatial receptive fields.
• Simulated responses to Gaussian white noise and sinusoidal gratings to determine spatial frequency selectivity.

3. Excitatory-Inhibitory Network Dynamics

Focus: Dynamical Systems & Oscillation Modeling

• Modeled membrane potential oscillations arising from Excitatory-Inhibitory (E-I) population interactions.
• Simluated low-pass filtering properties of the neural membrane.
• Analyzed the stability and oscillation frequency of the system under varying time constants ($\tau$).

🛠️ Tech Stack

• Language: Python
• Libraries: NumPy, SciPy, Matplotlib
• Key Concepts: Linear Systems Theory, Differential Equations, Statistical Signal Processing