- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Automatic Translation feature is available on CiNii Labs
- Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
Self-reorganization of neuronal activation patterns in the cortex under brain-machine interface and neural operant conditioning
Search this article
Description
In this review, we describe recent experimental observations and model simulations in the research subject of brain-machine interface (BMI). Studies of BMIs have applied decoding models to extract functional characteristics of the recorded neurons, and some of these have more focused on adaptation based on neural operant conditioning. Under a closed loop feedback with the environment through BMIs, neuronal activities are forced to interact directly with the environment. These studies have shown that the neuron ensembles self-reorganized their activity patterns and completed a transition to adaptive state within a short time scale. Based on these observations, we discuss how the brain could identify the target neurons directly interacting with the environment and determine in which direction the activities of those neurons should be changed for adaptation. For adaptation over a short time scale, the changes of neuron ensemble activities seem to be restricted by the intrinsic correlation structure of the neuronal network (intrinsic manifold). On the other hand, for adaptation over a long time scale, modifications to the synaptic connections enable the neuronal network to generate a novel activation pattern required by BMI (extension of the intrinsic manifold). Understanding of the intrinsic constraints in adaptive changes of neuronal activities will provide the basic principles of learning mechanisms in the brain and methodological clues for better performance in engineering and clinical applications of BMI.
Journal
-
- Neuroscience Research
-
Neuroscience Research 156 279-292, 2020-07
Elsevier BV
