Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale
A single AI model reads both brain activity and animal decisions from neural recordings
Researchers trained a single AI model to forecast neural activity one step ahead and discovered it could simultaneously decode what a mouse was about to do—predicting its choice 75.7% of the time and which visual stimulus it saw 66.1% of the time. This dual capability emerged from learning to predict raw spike counts alone, without explicit behavioral training, and worked reliably after just 100–150 calibration trials at the start of each recording session.
Brain-computer interfaces need both prediction and readout, usually requiring separate models and extra computational overhead. This approach cuts that complexity in half while running fast enough for real-time closed-loop experiments on standard lab computers, making it practical for researchers developing neural prosthetics or studying decision-making in animal models.