Implementing Goal-Directed Foraging Decisions of a Simpler Nervous System in Simulation

Area of Science:

• Neuroscience
• Computational Biology
• Animal Behavior

Background:

• Economic decisions involve evaluating actions based on motivation and memory.
• The predatory sea slug *Pleurobranchaea* exhibits complex foraging decisions.
• Understanding the neural basis of decision-making is crucial.

Purpose of the Study:

• To express and test neuronal circuit relations for approach-avoidance choice in *Pleurobranchaea*.
• To develop a computational model simulating foraging decisions.
• To investigate the role of appetitive state in integrating sensory information, motivation, and memory.

Main Methods:

• Development of the foraging simulation 'Cyberslug'.
• Modeling neuronal circuit relations for decision-making.
• Integration of sensation, motivation (satiation/hunger), and memory into an 'appetitive state'.
• Simulation of prey choice regulation through learning and satiation.

Main Results:

• The Cyberslug simulation realistically reproduces *Pleurobranchaea*'s foraging decisions.
• Appetitive state acts as a switch for approach-avoidance responses based on sensation.
• Sensory stimuli are processed for incentive value and prey location.
• The model satisfies optimal foraging criteria.

Conclusions:

• A simple, affectively controlled homeostat with learning abilities underlies economic foraging decisions in *Pleurobranchaea*.
• The model provides a framework for studying neural and behavioral complexity in decision-making.
• This research offers insights into the evolution of decision-making processes in nervous systems.