Computing with Canonical Microcircuits

TL;DR

This paper introduces a biologically inspired neural architecture based on canonical microcircuits, demonstrating high accuracy and efficiency on image recognition tasks while providing interpretable dynamical behaviors.

Contribution

The authors develop a neural ODE-based microcircuit model that achieves competitive performance with fewer parameters and offers insights into emergent behaviors similar to biological systems.

Findings

Single microcircuit node achieves 97.8% accuracy on MNIST

Hierarchical microcircuits improve performance on complex benchmarks

Model uses fewer parameters than traditional deep learning architectures

Abstract

The human brain represents the only known example of general intelligence that naturally aligns with human values. On a mere 20-watt power budget, the brain achieves robust learning and adaptive decision-making in ways that continue to elude advanced AI systems. Inspired by the brain, we present a computational architecture based on canonical microcircuits (CMCs) - stereotyped patterns of neurons found ubiquitously throughout the cortex. We implement these circuits as neural ODEs comprising spiny stellate, inhibitory, and pyramidal neurons, forming an 8-dimensional dynamical system with biologically plausible recurrent connections. Our experiments show that even a single CMC node achieves 97.8 percent accuracy on MNIST, while hierarchical configurations - with learnable inter-regional connectivity and recurrent connections - yield improved performance on more complex image benchmarks. Notably, our approach achieves competitive results using substantially fewer parameters than conventional deep learning models. Phase space analysis revealed distinct dynamical trajectories for different input classes, highlighting interpretable, emergent behaviors observed in biological systems. These findings suggest that neuromorphic computing approaches can improve both efficiency and interpretability in artificial neural networks, offering new directions for parameter-efficient architectures grounded in the computational principles of the human brain.