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Course Work 1: Fitting a Neural Model to Data

Due end week 6 (February 17th, 2023)

This course work is worth 40% of the overall module mark.

The course work is to develop a neural model in PyNN and Nest that approximates the behaviour of known neural data. In this case, a rat neuron has a time varying input. The model should take the input, and spike when the actual biological neural spikes. A 1000 word report should discuss the models used and their performance.

The data comes from Jolivet, R., Kobayashi, R., Rauch, A., Naud, R., Shinomoto, S., & Gerstner, W. (2008). A benchmark test for a quantitative assessment of simple neuron models. Journal of neuroscience methods, 169(2), 417-424. It describes rat neurons that have an electrode embedded. The electrode is provided with a time varying input, and the voltage of the neuron is measured. I've used that data to infer spikes (when a neuron goes above 0 voltage, and then returns below 0).

Aligning spikes is not a simple process. Describe how you align actual spikes with the simulated spikes from your model.

Here is the data , the spikes , a sample program in txt , and a sample program in py.

Marking scheme:

10Running Code
40Quality of Results
20Discussion of Model
20Discussion of Results

Please submit the code and the report to the moodle page.