Fig. 2 |. Manifold-based stabilization intuition and design.

a, Neural recording instabilities, such as a change in a neuron being recorded by a particular electrode (for example, electrode 3 (El. 3); red), result in changes in the recorded neural population activity across days. b, With a fixed BCI decoder alone, neural instabilities can result in the day 1 decoder (blue/green arrows, shown as a one-dimensional vector for simplicity) becoming inconsistent with neural activity recorded on day 2 (right; black dots). This leads to poor BCI performance. Cursor velocities are obtained by projecting the neural activity (black dots) onto the decoder axis. c, Combining stabilization with a fixed decoder allows the BCI performance to remain high across days. To extract a stable representation of neural population activity across days, low-dimensional neural manifolds are first fit to the neural population activity recorded on each day. The coordinate system for the neural manifold on day 2 is aligned so that neurons that are stable across days (electrodes 1 and 2) are defined in a consistent manner with respect to the coordinate systems for both days (denoted by the axes labelled p₁ and p₂). The thick black lines depict the final aligned coordinate system. Alignment ensures that unstable neural population activity is mapped to the same stable low-dimensional manifold across days, providing a stabilized latent state that can be used for decoding across days. FR, firing rate.