Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures

TL;DR

This paper evaluates biologically inspired deep learning algorithms on large-scale datasets, revealing their limitations compared to backpropagation and highlighting the need for new architectures and methods for scalability.

Contribution

It provides a rigorous assessment of biologically motivated algorithms like target propagation and feedback alignment on complex datasets, establishing baseline performance and identifying scalability challenges.

Findings

Biologically motivated algorithms perform well on MNIST.

Performance drops significantly on CIFAR-10 and ImageNet.

Locally connected architectures pose additional challenges.

Abstract

The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.