Abstract:
:We show that Langevin Markov chain Monte Carlo inference in an energy-based model with latent variables has the property that the early steps of inference, starting from a stationary point, correspond to propagating error gradients into internal layers, similar to backpropagation. The backpropagated error is with respect to output units that have received an outside driving force pushing them away from the stationary point. Backpropagated error gradients correspond to temporal derivatives with respect to the activation of hidden units. These lead to a weight update proportional to the product of the presynaptic firing rate and the temporal rate of change of the postsynaptic firing rate. Simulations and a theoretical argument suggest that this rate-based update rule is consistent with those associated with spike-timing-dependent plasticity. The ideas presented in this article could be an element of a theory for explaining how brains perform credit assignment in deep hierarchies as efficiently as backpropagation does, with neural computation corresponding to both approximate inference in continuous-valued latent variables and error backpropagation, at the same time.
journal_name
Neural Computjournal_title
Neural computationauthors
Bengio Y,Mesnard T,Fischer A,Zhang S,Wu Ydoi
10.1162/NECO_a_00934subject
Has Abstractpub_date
2017-03-01 00:00:00pages
555-577issue
3eissn
0899-7667issn
1530-888Xjournal_volume
29pub_type
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