Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons

• URL: http://arxiv.org/abs/2110.14549v1
• Date: Wed, 27 Oct 2021 16:15:55 GMT
• Title: Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
• Authors: Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai A. Petrovici
• Abstract summary: We introduce Latent Equilibrium, a new framework for inference and learning in networks of slow components. We derive disentangled neuron and synapse dynamics from a prospective energy function. We show how our principle can be applied to detailed models of cortical microcircuitry.
• Score: 0.7340017786387767
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The response time of physical computational elements is finite, and neurons are no exception. In hierarchical models of cortical networks each layer thus introduces a response lag. This inherent property of physical dynamical systems results in delayed processing of stimuli and causes a timing mismatch between network output and instructive signals, thus afflicting not only inference, but also learning. We introduce Latent Equilibrium, a new framework for inference and learning in networks of slow components which avoids these issues by harnessing the ability of biological neurons to phase-advance their output with respect to their membrane potential. This principle enables quasi-instantaneous inference independent of network depth and avoids the need for phased plasticity or computationally expensive network relaxation phases. We jointly derive disentangled neuron and synapse dynamics from a prospective energy function that depends on a network's generalized position and momentum. The resulting model can be interpreted as a biologically plausible approximation of error backpropagation in deep cortical networks with continuous-time, leaky neuronal dynamics and continuously active, local plasticity. We demonstrate successful learning of standard benchmark datasets, achieving competitive performance using both fully-connected and convolutional architectures, and show how our principle can be applied to detailed models of cortical microcircuitry. Furthermore, we study the robustness of our model to spatio-temporal substrate imperfections to demonstrate its feasibility for physical realization, be it in vivo or in silico.

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