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We developed Convis, a Python simulation toolbox for large scale neural populations which offers arbitrary receptive fields by 3D convolutions executed on a graphics card. The resulting software proves to be flexible and easily extensible in Python, while building on the PyTorch library (The Pytorch Project, 2017), which was previously used successfully in deep learning applications, for just. . The ST-Conv block contains two temporal convolutions (TemporalConv) with kernel size k. Hence for an input sequence of length m, the output sequence will be length m-2 (k-1). Parameters. in_channels ( int) – Number of input features. hidden_channels ( int) – Number of hidden units output by graph convolution block. The LSTM cell equations were written based on Pytorch documentation because you will probably use the existing layer in your project. In the original paper, c t − 1 \textbf{c}_{t-1} c t − 1 is included in the Equation (1) and (2), but you can omit it. For consistency reasons with the Pytorch docs, I will not include these computations in the code.
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