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Bridging across functional models: The OFC as a value-making neural network.

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This study introduces a novel neural network model for the orbitofrontal cortex (OFC) that generates object values from attributes. The model explains OFC activity patterns and predicts behavioral consequences of OFC damage.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • The orbitofrontal cortex (OFC) has diverse proposed functions, including signaling action outcome value and task-specific trial positioning.
  • Classical and recent theories of OFC function present conflicting explanations for observed neural activity.

Purpose of the Study:

  • To propose a unifying neural network architecture for the OFC.
  • To explain observed univariate value signals and multivariate feature decoding in OFC activity.
  • To account for OFC activity during value judgment and the behavioral effects of OFC damage.

Main Methods:

  • Developed a neural network model where values are outputs generated from object attributes.
  • Incorporated recurrent feedback connections to model simultaneous coding of attended and previously considered object values.
  • Simulated model behavior, including virtual lesions, to compare with empirical observations.

Main Results:

  • The model explains univariate value signals and multivariate feature decoding in OFC tasks.
  • Simulations reconcile population-level positive correlations with subjective value and diverse single-unit correlations.
  • Virtual OFC lesions in the model produced systematically intransitive preferences, mirroring patient data.

Conclusions:

  • The proposed neural network architecture offers a unified framework for understanding OFC function.
  • The model successfully bridges findings from human neuroimaging and monkey electrophysiology.
  • The model provides specific predictions for OFC activity and the behavioral impact of OFC damage.