From Worms to Mice: Homeostasis Maybe All You Need

TL;DR

This paper proposes that a simple XOR neural motif, involving excitatory and inhibitory connections and guided solely by homeostasis, could underpin neural plasticity across diverse organisms, inspired by machine learning principles.

Contribution

It introduces a biologically plausible XOR motif as a fundamental plasticity mechanism and demonstrates its prevalence in connectomes across species, linking neural architecture to learning.

Findings

XOR motif is prevalent from C. elegans to mouse V1.

The motif supports neural patterns like 'winner takes all'.

Homeostasis may be the core principle driving neural plasticity.

Abstract

In this brief and speculative commentary, we explore ideas inspired by neural networks in machine learning, proposing that a simple neural XOR motif, involving both excitatory and inhibitory connections, may provide the basis for a relevant mode of plasticity in neural circuits of living organisms, with homeostasis as the sole guiding principle. This XOR motif simply signals the discrepancy between incoming signals and reference signals, thereby providing a basis for a loss function in learning neural circuits, and at the same time regulating homeostasis by halting the propagation of these incoming signals. The core motif uses a 4:1 ratio of excitatory to inhibitory neurons, and supports broader neural patterns such as the well-known 'winner takes all' (WTA) mechanism. We examined the prevalence of the XOR motif in the published connectomes of various organisms with increasing complexity, and found that it ranges from tens (in C. elegans) to millions (in several Drosophila neuropils) and more than tens of millions (in mouse V1 visual cortex). If validated, our hypothesis identifies two of the three key components in analogy to machine learning models: the architecture and the loss function. And we propose that a relevant type of biological neural plasticity is simply driven by a basic control or regulatory system, which has persisted and adapted despite the increasing complexity of organisms throughout evolution.