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Winner-Take-All Computation in Spiking Neural Networks
We provide efficient constructions of WTA circuits in our stochastic spiking neural network model, as well as lower bounds in terms of the number of auxiliary neurons required to drive convergence to WTA in a given number of steps.
Counting to Ten with Two Fingers: Compressed Counting with Spiking Neurons
We first consider a deterministic implementation of a neural timer and show that $\Theta(\log t)$ (deterministic) threshold gates are both sufficient and necessary.
Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks
Randomization allows us to solve this task with a very compact network, using $O \left (\frac{\sqrt{n}\log n}{\epsilon}\right)$ auxiliary neurons, which is sublinear in the input size.
Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks
In this paper, we focus on the important `winner-take-all' (WTA) problem, which is analogous to a neural leader election unit: a network consisting of $n$ input neurons and $n$ corresponding output neurons must converge to a state in which a single output corresponding to a firing input (the `winner') fires, while all other outputs remain silent.
