A Programmer's Guide to Cascaded Adaptive Combiners: Online Learning by Biologically Accurate Models of Multilayer Neuron Networks

2026-06-12

AI Summary

This work proposes a biologically plausible multilayer neuronal network model that moves beyond the simplified weighted-sum neuron paradigm of conventional artificial neural networks, which struggles to capture the true learning mechanisms of biological systems. The proposed architecture employs cascaded adaptive combiners to enable efficient online, streaming learning without relying on backpropagation. By integrating neuron-level computations that more closely mirror biological reality with practical machine learning principles, the model establishes a concise and scalable learning framework. Empirical evaluation on image classification tasks demonstrates competitive performance, confirming both the efficacy and practicality of the approach.

Abstract

Learning in biological multilayer neuronal networks offers insights that extend beyond the classical weighted-sum neuron model commonly used in artificial neural networks. This article presents an accessible guide to a mechanistic neuronal network model that more accurately captures aspects of biological computation while enabling a simple yet powerful mechanism for learning in multilayer neural networks. The proposed approach supports efficient online streamed learning and provides a practical alternative to backpropagation. We demonstrate its potential in an image classification task, achieving competitive classification performance. The approach's simplicity, biological grounding, and broad applicability highlight a promising path toward algorithms that unify mechanistic neuron models and machine learning.

Problem

• online learning
• biological neuron models
• multilayer neural networks
• alternative to backpropagation
• cascaded adaptive combiners

Innovation

• Cascaded Adaptive Combiners
• online learning
• biologically plausible neural networks
• alternative to backpropagation
• mechanistic neuron models