brain-inspired-neural-cellular-automata

name	brain-inspired-neural-cellular-automata
description	Brain-inspired Neural Cellular Automata (BraiNCA) for morphogenesis and motor control. Uses biological neighborhood structures beyond regular grids.
version	1.0.0
author	Research Synthesis
license	MIT
metadata	{"hermes":{"tags":["neural-cellular-automata","morphogenesis","motor-control","brain-inspired","self-organization","developmental"],"source_paper":"BraiNCA: brain-inspired neural cellular automata and applications to morphogenesis and motor control (arXiv:2604.01932v1)"}}

BraiNCA: Brain-Inspired Neural Cellular Automata

Overview

BraiNCA extends Neural Cellular Automata beyond regular Moore neighborhoods to incorporate brain-inspired connectivity patterns. By using biologically-motivated neighborhood structures and update rules, BraiNCA achieves complex morphogenesis and motor control behaviors with self-organizing dynamics.

Core Concepts

Beyond Grid Neighborhoods

• Traditional NCAs use fixed Moore (8-neighbor) neighborhoods
• BraiNCA uses irregular, brain-inspired connectivity graphs
• Neighborhood structure adapts to task requirements

Self-Organization

• Local rules produce global patterns (emergent behavior)
• Morphogenesis: growing complex structures from simple rules
• Motor control: coordinated movement through local interactions

Biological Inspiration

• Cortical column organization
• Recurrent local processing with long-range connections
• Developmental plasticity during growth phase

Implementation Pattern

class BraiNCA(nn.Module):
    def __init__(self, n_cells, state_dim, neighborhood_graph):
        super().__init__()
        self.n_cells = n_cells
        self.state_dim = state_dim
        self.adj = neighborhood_graph
        self.update_rule = nn.Sequential(
            nn.Linear(state_dim * 2, 64), nn.ReLU(),
            nn.Linear(64, state_dim)
        )
    
    def step(self, states):
        new_states = []
        for i in range(self.n_cells):
            neighbors = self.get_neighbors(i, states)
            cell_state = states[i]
            neighbor_mean = neighbors.mean(dim=0)
            combined = torch.cat([cell_state, neighbor_mean])
            new_state = self.update_rule(combined)
            new_states.append(new_state)
        return torch.stack(new_states)
    
    def get_neighbors(self, cell_idx, states):
        neighbor_indices = torch.where(self.adj[cell_idx])[0]
        return states[neighbor_indices]

Applications

• Developmental robotics
• Self-organizing materials and structures
• Biological morphogenesis simulation
• Distributed motor control systems

Activation Keywords

• neural cellular automata, BraiNCA, morphogenesis NCA, self-organizing systems, brain-inspired cellular automata, 神经细胞自动机, 形态发生

References

• BraiNCA: brain-inspired neural cellular automata and applications to morphogenesis and motor control
• Authors: Léo Pio-Lopez, Benedikt Hartl, Michael Levin
• Published: 2026-04-02
• arXiv: https://arxiv.org/abs/2604.01932v1