Memory Networks: Towards Fully Biologically Plausible Learning

TL;DR

This paper introduces the Memory Network, a biologically inspired model that learns efficiently in a single pass without backpropagation, aiming to bridge the gap between biological plausibility and computational effectiveness in AI.

Contribution

The Memory Network model avoids backpropagation and convolutions, providing a new approach for rapid, biologically plausible learning in neural networks.

Findings

Achieves efficient learning on MNIST dataset

Demonstrates biological plausibility in learning process

Requires further development for complex datasets like CIFAR10

Abstract

The field of artificial intelligence faces significant challenges in achieving both biological plausibility and computational efficiency, particularly in visual learning tasks. Current artificial neural networks, such as convolutional neural networks, rely on techniques like backpropagation and weight sharing, which do not align with the brain's natural information processing methods. To address these issues, we propose the Memory Network, a model inspired by biological principles that avoids backpropagation and convolutions, and operates in a single pass. This approach enables rapid and efficient learning, mimicking the brain's ability to adapt quickly with minimal exposure to data. Our experiments demonstrate that the Memory Network achieves efficient and biologically plausible learning, showing strong performance on simpler datasets like MNIST. However, further refinement is needed for the model to handle more complex datasets such as CIFAR10, highlighting the need to develop new algorithms and techniques that closely align with biological processes while maintaining computational efficiency.