학술논문

Ventral striatum uses a temporal difference rule for prediction during neuroprosthetic control
Document Type
Conference
Source
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Neural Engineering (NER), 2019 9th International IEEE/EMBS Conference on. :562-565 Mar, 2019
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Rats
Task analysis
Correlation
Predictive models
Logistics
Brain modeling
Language
ISSN
1948-3554
Abstract
During neuroprosthetic control, prediction of future outcome plays a key role, by informing about current performance, so the subject can correct and refine its actions. During performance, an error signal emerges representing the difference between what was predicted and what actually happened. Several studies have focused on understanding how error signals emerges. Yet, the predictions needed to compute these error signals are still unclear.It has been suggested that the ventral striatum, may be the responsible for encoding a prediction function. So far, there is no clear evidence that these predictions are present in the ventral striatum or which different roles dorsal and the ventral striatum may be playing during neuroprosthetic control. To that effect, we trained rats to control a brain-machine-interface (BMI) with motor cortex units while recording the activity in striatum.Our results show that neuronal activity of the ventral striatum could predict future outcome and that this prediction coincided with the state prediction of a temporal difference model of reinforcement learning. These results highlight the relevance of the ventral striatum during neuroprosthetic control and point towards the use of neuronal prediction information to improve brain-machine-interfaces.