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Really Impressive. I wonder if you could use this to visualize a neural net that's solving actual problems.

• sabalaba 12y ago
  This doesn't look like a neural network that's solving any actual problems beyond visualizing firing patterns. Most ANNs don't have any concept of time built into the model. When you're in feed-forward mode, the underlying computation is simply a bunch of dot products. However, you might be able to make some cool visualizations if your ANN was a Spiking Neural Network. [1] https://en.wikipedia.org/wiki/Spiking_neural_network https://en.wikipedia.org/wiki/Spiking_neural_network
  • nightski 12y ago
    Well it's thresholded so you could visualize the "activated" neurons in a manner such as this. Would it be useful though? Maybe. Probably not as the fact that a neuron fires isn't nearly as useful as why it fired (what it represents). But I like that you mention time. It always seemed odd that NN's today completely ignore that aspect. Sure whether a neuron fires is binary. But the accumulation of spikes is not binary and highly temporal. yet we completely ignore this aspect.
    • wuliwong 12y ago
      I'm confused by you guys saying NN's today completely ignore time? If I have a network of Hodgkin-Huxley neurons and I integrate the equations, I'm integrating over time. I'm not sure how that is ignoring it? Disclaimer: it has been about 4 years since I've written any code to integrate H-H neurons, so I might be forgetting something really obvious. :) But I can't imagine EVERYONE is ignoring time when they simulate networks of neurons.
      • delluminatus 12y ago
        They are talking about NNs used in machine learning, which are atemporal, unlike H-H or other models of human cognition. In a machine learning neural network there is no integration; they are basically just nonlinear data transformations that can (usually...) be trained.