Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo,...

Area of Science:

• Evolutionary Biology
• Theoretical Biology
• Systems Biology

Background:

• Evolutionary mechanisms (variation, selection, inheritance) are not static.
• Major evolutionary transitions involve changes in developmental, ecological, and reproductive organizations.
• Current theory struggles to explain how these organizations evolve and create complex adaptive systems.

Purpose of the Study:

• Reinterpret key questions in evolutionary biology.
• Synthesize a new theoretical approach using cognitive learning models.
• Propose

Main Methods:

• Applying connectionist models of memory and learning to evolution.
• Analyzing how incremental changes in relationships between components create complex behaviors.
• Investigating functional equivalences between learning and evolution.

Main Results:

• Connectionist models show how simple mechanisms can generate complex system-level behaviors and improve adaptation.
• "Evolutionary connectionism" offers a framework for understanding how natural selection on relationships shapes evolutionary systems.
• Evidence supports functional equivalences between learning and evolution.

Conclusions:

• Evolutionary connectionism can resolve conceptual problems in understanding major evolutionary transitions.
• This approach provides insights into the evolution of developmental, ecological, and reproductive organizations.
• It offers a new perspective on how evolution self-modifies its own parameters over time.