SIMPL: Scalable and hassle-free optimisation of neural representations from behaviour
Tom George, Pierre Glaser, Kimberly Stachenfeld, Caswell Barry, Claudia Clopath · COSYNE 2025
High-dimensional neural activity in the brain is known to encode low-dimensional, time-evolving, behaviour-related latent variables. A fundamental goal of neural data analysis consists of identifying such variables and their mapping to neural activity. The canonical approach is to assume the latent variables are behaviour (e.g. the measured location of the animal) and visualise the subsequent tuning curves. However, significant mismatches between behaviour and the encoded variables may still exist---the agent may be thinking of another location, or be uncertain of its own---distorting the tuni
ePoster · Neuroscience