Feeding event based dataset to nengo for training

General Discussion
1

I have been trying to train a single-layer network on N-MNIST.

Got a code snippet from the Tonic dataset library:

dataset = tonic.datasets.NMNIST(save_to='./data', train=False)
dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, num_workers=4, pin_memory=True)
events, target = next(iter(dataloader))
print(events.shape)
print(target.shape)

I get output as:

torch.Size([1, 4517, 4])
torch.Size([1])

So essentially for a single image, I got 4517 events each with 4 fields: x (pixel), y(pixel), time_stamp(in us), polarity.

Did anyone try training any kind of network with such a dataset?