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Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs.

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Predictive coding can now approximate backpropagation (backprop) for any computation graph using only local learning rules. This breakthrough enables machine learning models like convolutional neural networks and recurrent neural networks to be implemented using biologically plausible, local brain plasticity mechanisms.

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Artificial intelligence

Background:

  • Backpropagation (backprop) is a key algorithm for training machine learning models via end-to-end differentiation.
  • Predictive coding offers a biologically plausible alternative using local, Hebbian updates, previously shown to approximate backprop in multilayer perceptrons (MLPs).
  • The broader power of backprop stems from automatic differentiation, applicable to any differentiable computation graph.

Purpose of the Study:

  • To demonstrate that predictive coding can achieve exact backpropagation gradients on arbitrary computation graphs.
  • To develop a method for translating standard machine learning architectures into predictive coding equivalents.
  • To explore the potential for implementing machine learning algorithms in neural circuitry and neuromorphic architectures.

Main Methods:

  • Developed a predictive coding framework capable of approximating backpropagation gradients on general computation graphs.
  • Translated core machine learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, into their predictive coding counterparts.
  • Employed local and Hebbian learning rules for all updates.

Main Results:

  • Predictive coding was shown to converge asymptotically and rapidly to exact backprop gradients on arbitrary computation graphs.
  • Constructed predictive coding versions of CNNs, RNNs, and LSTMs, handling complex structures like branching and multiplicative interactions.
  • These predictive coding models achieved performance equivalent to backprop models on challenging machine learning benchmarks.

Conclusions:

  • Predictive coding can replicate the gradient computation of backpropagation for any differentiable program using only local learning rules.
  • This approach facilitates the implementation of standard machine learning algorithms within biologically plausible neural systems.
  • The findings support the development of fully distributed neuromorphic architectures and offer insights into cortical computation.