Neural Cellular Automata
Introduction
Neural Cellular Automata (NCA) represent an innovative intersection between traditional cellular automata and neural networks. They are capable of learning complex behaviors through simple, local interactions between cells, mimicking biological growth and regeneration processes.
The Cellular Automata Perspective
From the perspective of cellular automata, NCAs build on the concept of simple, rule-based systems where each cell updates its state based on the states of its neighbors. This approach enables the emergence of complex patterns from basic rules.
The Neural Network Perspective
When viewed through the lens of neural networks, NCAs leverage the power of deep learning to optimize the update rules. This allows the system to adapt, learn, and generalize behaviors across various environments, making NCAs versatile tools for modeling dynamic systems.
The Research
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