Using economic value signals from primate prefrontal cortex in neuro-engineering applications

TL;DR

This paper demonstrates that neural signals related to economic value from primate prefrontal cortex can be decoded using deep learning to predict choices, advancing brain-machine interfaces with abstract cognitive signals.

Contribution

The study introduces a novel approach to decoding economic value signals from primate prefrontal cortex for neuro-engineering applications, including data augmentation and neural forecasting.

Findings

Deep learning decoders accurately predict monkey choices.

Synthesized data improves out-of-sample prediction performance.

Neural forecasting enables earlier prediction of decisions.

Abstract

Neural signals related to movement can be measured from intracranial recordings and used in brain-machine interface devices (BMI) to restore physical function in impaired patients. In this study, we explore the use of more abstract neural signals related to economic value in a BMI context. Using data collected from the orbitofrontal cortex in non-human primates, we develop deep learning-based neural decoders that can predict the monkey's choice in a value-based decision-making task. Out-of-sample performance was improved by augmenting the training set with synthesized data, showing the feasibility of using limited training data. We further demonstrate that we can predict the monkey's choice sooner using a neural forecasting module that is equipped with task-related information. These findings support the feasibility of user preference-informed neuroengineering devices that leverage abstract cognitive signals.