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Front-end CNN

A CNN model for modelling the processings in retina and thalamus. Given input movies, it outputs spike trains that can be used as inputs to certain downstream tasks.

How to generate spikes

Input

The input to the pre-trained CNN model is the raw dot coherence change movies. These movies (in .tfrecord format) are in my folder in the server [email protected], at mufeng/tfrecord_data_processing/preprocessed. The directory in firing_rate.py to these movies has been changed to the absolute path, so you should be able to run the code directly.

To generate spikes, you can clone this repository to your folder, then navigate to the tf2gpu virtual env on the .225 server:

conda activate tf2gpu

Then run the command

CUDA_VISIBLE_DEVICES=1 python firing_rate.py 'ch_model4' 50

where 'ch_model4' is the pre-trained CNN model, and 50 here means the number of dot coh change movies. 'ch_model4' is the model trained with natural movies only, and you can also specify 'ch_model6', which is the model pre-trained by the mixture of natural and dot movies. Both models are in the folder models. The maximum number of dot coh change movies is 60.

There is a thrid optional argument for running this command, which is the number of natural movies you want to investigate. This is for exploration purpose only, and is not needed for the purpose of generating spikes given dot movies.

Ouput

By running the script, a folder CNN_outputs will be created in your working directory, which contains 3 files:

  1. spike_train.npz: this is a matrix of spike trains generated from the firing rates. Its size is [num_moviesx10x4080, num_neurons]. In the example above, num_movies would be 50, and each movie contains 10 trials (we have excluded grey screens) - that's what the 10 means. 4080 here means the length of a trial (or a grey screen) in milliseconds, and it results from repeating the firing rates at each of the compressed 60 timesteps 68 times (60*68=4080). Therefore the num_moviesx10x4080 is the total length of all the movies in milliseconds. The num_neurons here is fixed at 16. When training the SNN, we feed the spike trains into the SNN trial by trial, so you should reshape this matrix into [num_moviesx10, 4080, num_neurons] so each of your input instance would be a 4080(ms)x16(cells) matrix. This matrix is also saved as a scipy sparse matrix to save disk space, and to load and convert it to a numpy array or tensorflow tensor you can refer to here.

  2. coherences.npz: this is a matrix of coherence levels (0 for 15% and 1 for 100%), of size [num_movies, 40800]. Again, in the example above num_movies would be 50, and 40800 is the total length (in milliseconds) of a movie containing 10 trials. This matrix records the coherence level at each millisecond, in each movie. If you are training your SNN to predict the ms-by-ms coherence levels, you should load this as your target variable. (It is a scipy sparse matrix as well!)

  3. changes.npy: this is a matrix that records whether there is a coherence change in each trial, of size [num_movies, 10] (i.e. each movie contains 10 trials). If you want to train your SNN to detect change(1)/no change(0) in the trials, you should load this as your target variable.