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What can spiking neurons compute? Kendrick Kay (with Greg Corrado) October 5, 2009 The big picture • Given neuromorphic spiking hardware, what useful things can we compute? • Think about computation in terms of input/output • Literature on spiking neurons not large; hard to subject spiking neurons to theoretical analysis • Approach: Simulate neuromorphic spiking hardware (in MATLAB) and see what happens Example of a result • Oja’s rule applied to a perceptron leads to computation of the first principal component of the data (Oja 1982) Let’s get started: FNS • FNS: “Framework for Neural Simulation” – a modular code architecture (in MATLAB) that allows different flavors of synapses, membranes, and spikers to be interchanged and reused – includes a variety of neuronal models as special cases What kind of neural network to build? • Can we design a general-purpose neural network that can learn appropriate behaviors when embedded in a closed-loop system (input: sensory, output: action)? • Example: – learn to play Pong based on raw video input – or maybe more vision-type stuff Some initial results • Perceptron – Hmm... notice that input, output, network can each be binary or continuous (8 possible combinations) Open issues, things to do Things to tackle: • The world is analog, but spiking neurons propagate digital signals. • The world evolves over time, but artificial neural networks tend to deal with static data points. Is this going to work at all? • Multi-layer perceptrons already can compute any function. • What does spiking computation really offer? Surely not computational capacity, but efficiency?