Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning

TL;DR

This paper introduces NeurPIR, a contrastive learning framework that captures intrinsic neuron representations from activity data, enabling accurate neuron type and location prediction across domains.

Contribution

It proposes a novel contrastive learning method for extracting neuron-invariant representations from multi-segment activity data, validated on simulated and real neuronal datasets.

Findings

Accurately identified neuron types in simulated data.

Predicted neuron types and locations in real datasets.

Robust performance on out-of-domain data.

Abstract

The Platonic Representation Hypothesis suggests a universal, modality-independent reality representation behind different data modalities. Inspired by this, we view each neuron as a system and detect its multi-segment activity data under various peripheral conditions. We assume there's a time-invariant representation for the same neuron, reflecting its intrinsic properties like molecular profiles, location, and morphology. The goal of obtaining these intrinsic neuronal representations has two criteria: (I) segments from the same neuron should have more similar representations than those from different neurons; (II) the representations must generalize well to out-of-domain data. To meet these, we propose the NeurPIR (Neuron Platonic Intrinsic Representation) framework. It uses contrastive learning, with segments from the same neuron as positive pairs and those from different neurons as negative pairs. In implementation, we use VICReg, which focuses on positive pairs and separates dissimilar samples via regularization. We tested our method on Izhikevich model-simulated neuronal population dynamics data. The results accurately identified neuron types based on preset hyperparameters. We also applied it to two real-world neuron dynamics datasets with neuron type annotations from spatial transcriptomics and neuron locations. Our model's learned representations accurately predicted neuron types and locations and were robust on out-of-domain data (from unseen animals). This shows the potential of our approach for understanding neuronal systems and future neuroscience research.