Ageing, computation and the evolution of neural regeneration processes

Area of Science:

• Computational neuroscience
• Evolutionary biology
• Artificial intelligence

Background:

• Metazoans rely on neural networks for environmental interaction and response.
• Neural network performance must remain reliable throughout an organism's lifespan, despite cellular aging and connection failures.

Purpose of the Study:

• To investigate how neural computations degrade over an organism's lifespan.
• To determine the reliability limits of neural networks in digital organisms.
• To explore the evolutionary pressures shaping neural system maintenance strategies.

Main Methods:

• A multi-objective optimization approach was used to model digital organisms with neural networks.
• Simultaneous minimization of neural connection costs, regeneration costs, and computational error was investigated.
• The impact of organism lifespan and external damage rates as evolutionary pressures was analyzed.

Main Results:

• Pareto optimal trade-offs emerged, revealing diverse solutions from small, high-regeneration networks to large, redundant, slow-regeneration circuits.
• Organism lifespan and external damage rates influenced the exploration of design solutions and imposed tighter constraints.
• High damage rates could force organisms into unique neural maintenance strategies.

Conclusions:

• Neural network design in organisms involves a complex interplay between cost, reliability, and regeneration.
• Evolutionary pressures like lifespan and damage significantly shape the strategies for maintaining neural system integrity.
• Understanding these trade-offs provides insights into the robustness and evolution of biological computation.