Model data for: Task success in trained spiking neural network models coincides with emergence of cross-stimulus-modulated inhibition

Zip files of a sample experiment in each major experimental category are listed below. base.zip: baseline model (single readout unit), dual-training (task and rate) rate.zip: baseline model, rate-only training remove_ee.zip: baseline model without recurrent e-->e connectivity, dual-training Each npz file contains data for 10 epochs (100 batch updates of 30 trials each) beginning with file "1-10.npz" and ending with file "991-1000.npz". For example, the first 10 epochs can be loaded using data = np.load('1-10.npz'). Data variables can be accessed using data['variable_name']. The data variable names are: true_y and pred_y (true and predicted output): shaped [100 batches, 30 trials-per-batch, 4080 timesteps]. spikes: shaped [100 batches, 30 trials-per-batch, 4080 timesteps, 300 neurons] the first 240 neurons are excitatory. step_task_loss and step_rate_loss: shaped [100 batches]. epoch_loss: shaped [10 epochs]. tv0.postweights are the weights for the input layer after each batch update, and tv0.gradients are the gradients for the input layer for each batch update. In similar fashion, tv1 is the main recurrent layer and tv2 is the output layer. You can access postweights and gradients for both. tv0 variables are shaped [100 batches, 16 inputs, 300 neurons]. tv1 variables are shaped [100 batches, 300 neurons, 300 neurons], and tv2 variables are shaped [100 batches, 300 neurons, 1 output]. Weights and gradients are always saved after each batch update. The initial weights are saved in separate files named "input_preweights.npy", "main_preweights.npy", and "output_preweights.npy". We only provide sample experiments for three categories here due to data storage limitations (50GB on Zenodo; the total experimental data exceeds 6TB); additional data can be made available upon request. For example, sample experiments in additional categories (baseline task-only training, 1hot output encoding, and 1hot without recurrent e-->e connectivity) are readily available. Full datasets for each experimental category can also be arranged.