Updating the standard neuron model in artificial neural networks

2026-05-19

AI Summary

Standard artificial neural networks rely on simplified point-neuron models that fail to capture the complex computational properties of biological neurons. This work proposes, for the first time, integrating biologically plausible dynamical models—derived from cutting-edge neuroscience and closely aligned with cortical neuron physiology—directly into deep network architectures as drop-in replacements for conventional units, without increasing parameter count. Theoretical analysis and empirical experiments demonstrate that this approach substantially enhances model expressivity, accelerates learning, and improves robustness, while simultaneously reducing overfitting and decreasing reliance on large training datasets.

Abstract

From their inception in the 1950s, artificial neural networks (ANNs) started using the so-called point neuron model then prevalent in neuroscience, hoping that this analogy would allow for a better emulation of brain function. Over the years the neuroscience literature has shown that the point neuron model is too simplistic to properly represent many fundamental neural processes; however, the standard neuron model in ANNs still remains the same. Here we substitute it by a very recent model of cortical cells and demonstrate through theoretical analyses and experimental results how, simply by using a more realistic neural unit element without augmenting the number of parameters, the resulting ANNs offer a number of important advantages that include increases in expressivity, robustness and learning speed, and a reduction in memorization and the amount of training data needed.

Problem

• point neuron model
• artificial neural networks
• cortical cells
• neural modeling
• neuroscience

Innovation

• point neuron model
• cortical neuron model
• neural network expressivity
• learning efficiency
• biologically plausible neurons