Computational methods for learning representations of neural data and decoding behavioral, cognitive, or experimental variables.
Approaches can include supervised learning, representation learning, dimensionality reduction, temporal models, and multimodal analysis.
The methods can support studies of neural representations, brain-behavior relationships, and computational cognitive science.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.