Simulates firing rates of Hodgkin-Huxley, Leaky Integrate and Fire (LIF), and Izhikevich models of neurons based on simulated input current.

This project requires Python to run, and was developed using Python 3.6.1.

1. Clone this repository, and navigate to the correct directory: neuron-simulator.
2. Run the following command: pip install -r requirements.txt
3. Run the following command: python simulator.py
4. Follow instructions as prompted

The code to simulate the LIF model can be found in lif.py. All of the simulators make use of plotter.py to plot the results.

The algorithm used for this model follows the formulas provided in the project description. Arbitrary values were used for the following:

Since a neuron is essentially an RC circuit, I decays over time following an RC circuit's pattern

The code to simulate the Izhikevich model can be found in izhikevich.py. All of the simulators make use of plotter.py to plot the results.

The algorithm used for this model follows the formulas provided in the project description. No arbitrary values were used, except for initial values of u and v. Initially, u = 0.0 and v = -65.0.

The user is able to control values of a, b, c, d, and I. However, suggested values are:

The code to simulate the Hodgkin-Huxley model can be found in hodgkin_huxley.py. All of the simulators make use of plotter.py to plot the results.

The formulas provided in the original project description were used to simulate the neuron. The only arbitrary value is that of the injected current, which was found via research and testing different numbers. For 0.0ms < t < 1.0ms, I = 150, and for 10.0ms < t < 11.0ms, I = 50. For all other values of t, I = 0.