Structure as Computation: Developmental Generation of Minimal Neural Circuits

TL;DR

This paper models cortical development from stem cells to neural circuits, showing how developmental rules create structures that enable rapid learning on tasks like MNIST and CIFAR-10.

Contribution

It introduces a developmental simulation of neural circuit formation that results in structures capable of fast, high-accuracy learning without architectural changes.

Findings

Developmental process generates a small, densely connected neural core.

The resulting circuit achieves over 90% accuracy on MNIST after one epoch.

The same circuit attains 40.53% on CIFAR-10 after one epoch.

Abstract

This work simulates the developmental process of cortical neurogenesis, initiating from a single stem cell and governed by gene regulatory rules derived from mouse single-cell transcriptomic data. The developmental process spontaneously generates a heterogeneous population of 5,000 cells, yet yields only 85 mature neurons - merely 1.7% of the total population. These 85 neurons form a densely interconnected core of 200,400 synapses, corresponding to an average degree of 4,715 per neuron. At iteration zero, this minimal circuit performs at chance level on MNIST. However, after a single epoch of standard training, accuracy surges to over 90% - a gain exceeding 80 percentage points - with typical runs falling in the 89-94% range depending on developmental stochasticity. The identical circuit, without any architectural modification or data augmentation, achieves 40.53% on CIFAR-10 after one epoch. These findings demonstrate that developmental rules sculpt a domain-general topological substrate exceptionally amenable to rapid learning, suggesting that biological developmental processes inherently encode powerful structural priors for efficient computation.