A minimal model of cognition based on oscillatory and current-based reinforcement processes

Area of Science:

• Computational Neuroscience
• Theoretical Biology
• Systems Biology

Background:

• Modeling complex brains is challenging due to intricate neural networks.
• Basal cognition offers a simplified framework, representing organisms without central nervous systems like fungi, slime molds, and bacteria.
• Existing models of basal cognition do not explicitly incorporate particle dynamics.

Purpose of the Study:

• To propose a novel mathematical model for basal cognition.
• To demonstrate efficient resource coupling in organisms lacking central nervous systems.
• To investigate the role of particle oscillations and flow in biological systems.

Main Methods:

• Developed a mathematical model incorporating oscillatory and current-based reinforcement processes.
• Explicitly modeled oscillations in particle numbers (nutrients, signals) and their flow.
• Analyzed the efficiency of resource coupling under varying environmental oscillation conditions.

Main Results:

• The model demonstrates efficient resource coupling when environmental oscillations are sufficiently out of phase.
• Amplitude differences in oscillations promote efficient solutions.
• The model exhibits robustness to frequency differences in environmental oscillations.

Conclusions:

• The proposed model effectively mimics the resource coupling mechanisms of organisms with basal cognition.
• Explicit modeling of particle oscillations and flow is a key innovation for understanding basal cognition.
• Oscillations may play a crucial role in self-organized problem-solving in organisms like slime molds.