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Computer Science > Machine Learning

arXiv:2103.04689 (cs)
[Submitted on 8 Mar 2021]

Title:Predictive Coding Can Do Exact Backpropagation on Any Neural
Network

Authors:Tommaso Salvatori, Yuhang Song, Thomas Lukasiewicz, Rafal
Bogacz, Zhenghua Xu
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    Abstract: Intersecting neuroscience and deep learning has brought
    benefits and developments to both fields for several decades,
    which help to both understand how learning works in the brain,
    and to achieve the state-of-the-art performances in different AI
    benchmarks. Backpropagation (BP) is the most widely adopted
    method for the training of artificial neural networks, which,
    however, is often criticized for its biological implausibility
    (e.g., lack of local update rules for the parameters). Therefore,
    biologically plausible learning methods (e.g., inference learning
    (IL)) that rely on predictive coding (a framework for describing
    information processing in the brain) are increasingly studied.
    Recent works prove that IL can approximate BP up to a certain
    margin on multilayer perceptrons (MLPs), and asymptotically on
    any other complex model, and that zero-divergence inference
    learning (Z-IL), a variant of IL, is able to exactly implement BP
    on MLPs. However, the recent literature shows also that there is
    no biologically plausible method yet that can exactly replicate
    the weight update of BP on complex models. To fill this gap, in
    this paper, we generalize (IL and) Z-IL by directly defining them
    on computational graphs. To our knowledge, this is the first
    biologically plausible algorithm that is shown to be equivalent
    to BP in the way of updating parameters on any neural network,
    and it is thus a great breakthrough for the interdisciplinary
    research of neuroscience and deep learning.

Comments: 15 pages, 9 figures
Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2103.04689 [cs.LG]
          (or arXiv:2103.04689v1 [cs.LG] for this version)

Submission history

From: Tommaso Salvatori [view email]
[v1] Mon, 8 Mar 2021 11:52:51 UTC (1,026 KB)
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