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About this paper
Atsushi Masumori, Hiroki Sato, Takashi Ikegami
Masumori, Sato, and Ikegami explore the functional role of internal fluctuations in Growing Neural Cellular Automata (GNCA) during self-maintenance and self-repair. Their analysis reveals these fluctuations are structured, intrinsic dynamical components that contribute to organism-wide recovery from damage, supported by an adaptive shift in information processing from a synergy-dominant regime during normal operation to a redundancy-enhanced regime during repair.
Key insights
- Internal fluctuations in trained GNCA are structured, intrinsic dynamical components, not merely noise, and are functionally essential for self-maintenance and recovery from damage.
- Self-repair in GNCA represents a return to an attracting recurrent state in a high-dimensional state space, establishing a dynamical systems framework for robustness.
- The system employs adaptive computational strategies, shifting from a synergy-dominant information processing regime during normal operation to a redundancy-enhanced regime during active recovery.
Summary by alphaXiv.
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