An Algorithmic Model of Decision Making in the Human Brain.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Cognitive Science

Background:

• Decision-making and problem-solving are complex neuroscience topics, especially with sequential events.
• Empirical modeling is key to understanding intricate brain functions.
• Model Predictive Control (MPC) is a robust industrial tool applicable to brain function.

Purpose of the Study:

• To propose and develop an MPC algorithm as a functional component of the brain.
• To enhance the performance of the decision-making process using computational modeling.
• To model and simulate the interaction between key brain regions involved in decision-making.

Main Methods:

• A hybrid methodology combining nonlinear control systems (MPC) and Object-Oriented Modeling (OOM).
• Developing an algorithm based on the cognitive functions of the prefrontal cortex (PFC) and hippocampus.
• Simulating neural circuit modulation between cortex and sub-cortex during decision-making.

Main Results:

• The methodology enabled the design of an algorithm reflecting PFC and hippocampus cognitive roles.
• The developed algorithm modulates neural circuits between cortical and sub-cortical regions.
• Demonstrated MPC's capability in modeling brain decision-making mechanisms.

Conclusions:

• Decision-making involves prefrontal cortex (working memory) and hippocampus (long-term memory) communication.
• MPC was used to model the mechanisms of other essential brain regions in decision-making.
• MPC effectively controls data flow in a closed-loop system between the prefrontal cortex and hippocampus to refine actions.