A neuro-computational model of economic decisions

Area of Science:

• Neuroscience
• Computational Neuroscience
• Neuroeconomics

Background:

• Economic decisions involve the orbitofrontal cortex (OFC), with identified neurons encoding offer value, chosen value, and chosen good identity.
• A key question is how these neuronal populations interact to produce economic decisions.

Purpose of the Study:

• To adapt a biophysically realistic neural network for perceptual decisions to model economic decision-making in the OFC.
• To investigate the emergent properties of a neural circuit composed of offer value, chosen value, and interneurons within the OFC.

Main Methods:

• Adaptation of a previously developed biophysically realistic neural network for perceptual decisions.
• Mapping input/output nodes to specific neuronal populations (offer value, chosen value cells) in the OFC.
• Analyzing network activity, particularly interneuron behavior, in the context of economic choices.

Main Results:

• The adapted model successfully simulated economic decision-making, performing well despite the broader domain.
• Interneuron activity in the model closely matched the activity of chosen value cells observed empirically.
• The model reproduced neuronal origins of choice variability and generated testable predictions about neuronal properties and connectivity.

Conclusions:

• The findings present a biologically plausible neural model for economic decisions, demonstrating emergence from OFC cell activity.
• Chosen value cells are suggested to play a direct role in the decision process.
• The model serves as a platform for integrating neuroscience findings with economic theory.