Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis

2025/05/29 by Xingyu Liu, Yubin Li, Liu, Xingyu +3
Engineering · Physics and Astronomy · #Spacecraft Design and Technology #Space Exploration and Technology #Space Science and Extraterrestrial Life

paper · pdf · doi:10.48550/arxiv.2506.11062

Abstract

A central idea in understanding brains and building artificial intelligence is that structure determines function. Yet, how the brain's complex structure arises from a limited set of genetic instructions remains a key question. The ultra high-dimensional detail of neural connections vastly exceeds the information storage capacity of genes, suggesting a compact, low-dimensional blueprint must guide brain development. Our motivation is to uncover this blueprint. We introduce a generative model, to learn this underlying representation from detailed connectivity maps of mouse cortical microcircuits. Our model successfully captures the essential structural information of these circuits in a compressed latent space. We found that specific, interpretable directions within this space directly relate to understandable network properties. Building on this, we demonstrate a novel method to controllably generate new, synthetic microcircuits with desired structural features by navigating this latent space. This work offers a new way to investigate the design principles of neural circuits and explore how structure gives rise to function, potentially informing the development of more advanced artificial neural networks.

Citations

• Discrete-state Continuous-time Diffusion for Graph Generation
• Neuronal Auditory Machine Intelligence (NEURO-AMI) In Perspective
• Traveling Waves Encode the Recent Past and Enhance Sequence Learning
• Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal Brain Functional Connectome Embedding
• NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
• Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
• Neural Architecture Search: Insights from 1000 Papers
• Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation
• Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
• FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
• Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
• RoFormer: Enhanced Transformer with Rotary Position Embedding
• Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
• Efficient Graph Generation with Graph Recurrent Attention Networks
• Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
• GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
• Graph Attention Networks
• A Unified Approach to Interpreting Model Predictions
• Dynamic models of large-scale brain activity
• NEURONAL CIRCUITS OF THE NEOCORTEX
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• LOCAL CIRCUITS IN PRIMARY VISUAL CORTEX OF THE MACAQUE MONKEY

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