6 research outputs found
Towards a learning-theoretic analysis of spike-timing dependent plasticity
This paper suggests a learning-theoretic perspective on how synaptic
plasticity benefits global brain functioning. We introduce a model, the
selectron, that (i) arises as the fast time constant limit of leaky
integrate-and-fire neurons equipped with spiking timing dependent plasticity
(STDP) and (ii) is amenable to theoretical analysis. We show that the selectron
encodes reward estimates into spikes and that an error bound on spikes is
controlled by a spiking margin and the sum of synaptic weights. Moreover, the
efficacy of spikes (their usefulness to other reward maximizing selectrons)
also depends on total synaptic strength. Finally, based on our analysis, we
propose a regularized version of STDP, and show the regularization improves the
robustness of neuronal learning when faced with multiple stimuli.Comment: To appear in Adv. Neural Inf. Proc. System
Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks
Error backpropagation is an extremely effective algorithm for assigning
credit in artificial neural networks. However, weight updates under Backprop
depend on lengthy recursive computations and require separate output and error
messages -- features not shared by biological neurons, that are perhaps
unnecessary. In this paper, we revisit Backprop and the credit assignment
problem. We first decompose Backprop into a collection of interacting learning
algorithms; provide regret bounds on the performance of these sub-algorithms;
and factorize Backprop's error signals. Using these results, we derive a new
credit assignment algorithm for nonparametric regression, Kickback, that is
significantly simpler than Backprop. Finally, we provide a sufficient condition
for Kickback to follow error gradients, and show that Kickback matches
Backprop's performance on real-world regression benchmarks.Comment: 7 pages. To appear, AAAI-1