Extended Data Fig. 4: PSID can be used to model neural activity for different neural signal types including LFP power activity or population spiking activity.
From: Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification
Modeling neural activity using PSID is demonstrated with example signals, extracted latent states, and decoded behavior for (a) LFP power activity (that is signal power in different frequency bands, which are shown with different colors, Methods) and (b) Population spiking activity (Methods). In both cases, regardless of neural signal type, after extracting the neural feature time-series, decoding consists of two steps: 1) applying Kalman filter to extract the latent states given the neural feature time-series, 2) computing a linear combination of the states to get the decoding of behavior. By learning the dynamic model parameters, PSID specifies the Kalman filter parameters as well as the linear combination. Joint name abbreviations are as in Supplementary Fig. 12.
