Flexible Electrode Arrays Track the Same Brain Cells for Months as Their Codes Keep Shifting

The brain is a moving target. Even when an animal looks at exactly the same scene day after day, the individual neurons that respond to that scene do not hold still in their behavior: their firing patterns gradually reorganize, a phenomenon neuroscientists call representational drift. For anyone hoping to build a brain–computer interface that keeps working over months or years, this slow-motion shuffling of neural codes is a fundamental problem, because a decoder calibrated on Monday may be quietly wrong by Friday. A new study published in Nature Biomedical Engineering by Hao Shen, Siyuan Zhao, Arnau Marin-Llobet and colleagues in Jia Liu’s group at Harvard University shows how both halves of that problem can be attacked at once, using tissue-like flexible electrode arrays to follow the same neurons for up to five months and a geometric transformation to keep a decoder locked onto the drifting code.

The first half of the challenge is hardware. Conventional neural probes are stiff devices made of silicon or metal, and the brain is soft, so every heartbeat, breath and head movement creates relative motion between the implant and the surrounding tissue. Over weeks, that mechanical mismatch triggers an immune response: glial cells wall off the foreign object, signal quality degrades, and neurons that were once cleanly recorded drift out of reach or disappear entirely. The Harvard team built around this by using flexible mesh-like electrode arrays whose mechanical properties are far closer to those of brain tissue itself. Because the probes move with the brain rather than fighting against it, they provoke less of a scarring response and stay in stable physical registration with the neurons around them.

That stability paid off dramatically. Implanting the arrays in the mouse visual cortex and hippocampus, the researchers recorded single-unit action potentials from the same identifiable neurons across sessions spanning five months. Spike sorting and waveform analysis confirmed that the cells tracked over time were genuinely the same neurons: within-neuron waveform correlations stayed high, estimated displacements of the neurons’ positions remained small, and firing-rate statistics were consistent across the recording period. The team compared mice that watched visual movies frequently with mice that saw the same stimuli only occasionally, and found no significant differences in tracking quality between the groups, ruling out the possibility that repeated stimulation itself was distorting the recordings.

With a stable window onto the same cells, the researchers could then measure representational drift directly and rigorously. They presented mice with repeated showings of the same movies and quantified how the population vectors of neural activity changed between recording sessions separated by days or weeks. The drift was real and persistent: correlations between population vectors declined as the time interval between sessions grew, in both the visual cortex and hippocampal CA1. Importantly, the team used deep-learning-based eye and pupil tracking to show that changes in arousal or attention could not account for the drift, and statistical comparisons showed that drift rates did not slow down over the course of the months-long experiment.

The study also probed what drives the drift. When the researchers parametrically scrambled the movies, degrading their higher-order structure while preserving basic statistics, they found that single-neuron tuning correlations and drift rates changed systematically with the level of scrambling, indicating that the statistics of the visual stimulus itself shape how representations wander. They also ran a conditioned task in which a scene in the movie predicted a water reward, and found that reward engagement altered some aspects of drift, such as tuning-curve correlations, while leaving others, including population-vector correlations and latent dynamics, statistically unchanged. Drift was also stronger in hippocampal CA1 than in primary visual cortex, consistent with the hippocampus’s role in rapidly remapping experience.

The second half of the study is where the work becomes most consequential for neurotechnology. Rather than trying to stop the drift, the team modeled it. Using Gaussian-process factor analysis, they extracted low-dimensional latent dynamics, the smooth trajectories that neural populations trace through an abstract state space as they process a stimulus. They found that while the raw latent trajectories from different sessions did not overlap, they were geometrically similar: the same movie drove the population through roughly the same shaped path, just rotated or otherwise transformed in the state space. A classic orthogonal Procrustes alignment, a geometric transformation that finds the best rotation mapping one set of coordinates onto another, was enough to bring the latent dynamics from different months into close correspondence.

Once the latent spaces were aligned, decoding became stable. The researchers trained a decoder on the transformed latent dynamics from one session and found that it maintained high performance across long time spans, accurately predicting which time window of a movie the animal was watching even months later. Decoding from the transformed latent dynamics significantly outperformed decoding from either the raw high-dimensional single-neuron activity or the unaligned latent dynamics, for both support-vector-machine and LSTM decoders, in both frequently and infrequently stimulated mice. In effect, the geometric transformation acts as a translation layer that converts each day’s neural code back into a common reference frame, so the decoder never needs to be retrained.

The team pushed the idea further with a proof-of-concept general model that spanned both time and animals, showing that aligned latent dynamics could support decoding that generalizes across different mice, and even demonstrated frame reconstruction of the visual stimulus using a neural-data-guided diffusion model. They also validated the approach with nonlinear alternatives, including autoencoder-based extraction of latent dynamics and encoder-decoder architectures that align sessions without explicit Procrustes fitting. Simulation experiments underscored why the flexible hardware matters: when the researchers artificially injected probe instability, such as 20 to 30 percent neuron dropout and tuning changes mimicking a drifting stiff probe, the quality of the latent-dynamics alignment degraded significantly. Stable recording of the same neurons is not a luxury but a prerequisite for the alignment mathematics to work.

The implications reach well beyond the mouse visual cortex. Brain–computer interfaces for paralyzed patients have long battled the same twin enemies: probe migration and immune scarring on the hardware side, and slow drift in the neural code on the algorithmic side. Previous work had shown that aligning low-dimensional neural activity spaces can stabilize a brain–computer interface over days, but doing so required a stable substrate of tracked neurons, which rigid implants struggle to provide. By combining tissue-matched flexible electronics with months-long single-neuron tracking and geometric realignment of population dynamics, this study demonstrates a complete framework in which both the sensor and the decoder adapt to a changing brain rather than pretending the brain stands still. If the approach translates to clinical implants, it could mean neural interfaces that stay calibrated for years, turning representational drift from a showstopper into just another coordinate transformation.

Subject of Research: Long-term tracking and decoding of representational drift in the mouse visual cortex using flexible electrode arrays

Article Title: Realignment of representational drift in mouse visual cortex via flexible electrode arrays

Article References: Shen, H., Zhao, S., Marin-Llobet, A., Qin, S., Jiang, S., Tang, X., Lee, M., Zhang, X., Lee, J., Chen, J., & Liu, J. (2026). Realignment of representational drift in mouse visual cortex via flexible electrode arrays. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-026-01780-x

Image Credits: AI Generated

DOI: 10.1038/s41551-026-01780-x

Keywords: representational drift, flexible electrode arrays, brain-computer interface, visual cortex, hippocampus, neural decoding, latent dynamics, manifold alignment, single-neuron tracking, mouse neuroscience, neural probes, Nature Biomedical Engineering