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Nina Kudryashova

@ninelk.bsky.social

61 followers 68 following 10 posts

Computational neuroscientist interested in movement and prediction

Posts Replies Media

Nina Kudryashova @ninelk.bsky.social · 07/04/2025

Whoops, apparently, a transparent background does not work. Here is our model schematics

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Nina Kudryashova @ninelk.bsky.social · 07/04/2025

Our study provides new insights into the neural code for feedback-driven motor control, showing that movement corrections can be encoded in small, yet behaviorally-relevant neural variability. Check it out: www.biorxiv.org/content/10.1... #biorxiv

biorxiv.org

BAND: Behavior-Aligned Neural Dynamics is all you need to capture motor corrections

Neural activity in motor cortical areas is well-explained by latent neural population dynamics: the motor preparation phase sets the initial condition for the movement while the dynamics that unfold d...

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Nina Kudryashova @ninelk.bsky.social · 07/04/2025

Finally, our model’s sequence-to-sequence behavior decoder allowed us to explore the temporal relationships between neural latent factors and behavior. The movement corrections generated latents that lag behind behavior by 90ms, suggesting a feedback-driven motor control strategy.

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Nina Kudryashova @ninelk.bsky.social · 07/04/2025

Comparing models on a force-field perturbation dataset, we found that model dynamics captures the movement plan (like LFADS), while behavior supervision captures transient corrections (like CEBRA). The best results are achieved with the combination of both.

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Nina Kudryashova @ninelk.bsky.social · 07/04/2025

We immediately achieved excellent results on Neural Latents Benchmark (neurallatents.github.io), outperforming both unsupervised as well as (semi-)supervised models on hand velocity reconstruction

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Nina Kudryashova @ninelk.bsky.social · 07/04/2025

We added behavior supervision in our Behavior Aligned Neural Dynamics (BAND) model to ensure that the neural code for movement correction is included in the latent variables, since movement corrections cause a substantial change in behavioral output.

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Nina Kudryashova @ninelk.bsky.social · 07/04/2025

We hypothesize that, unlike movement plans, corrections to a perturbation only transiently deviate from planned trajectories. As a result, these corrections would be reflected in relatively few spikes (i.e. account for a small neural variability).

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Nina Kudryashova @ninelk.bsky.social · 07/04/2025

In data from [Perich et al. 2018], we found that monkeys showed hand velocity oscillations during center-out reaches in a force-field. These oscillations were decodable from M1 (not PMd), but unsupervised models like LFADS struggled to capture them in the latent space.

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Nina Kudryashova @ninelk.bsky.social · 07/04/2025

Preparatory activity is a central source of neural and behavioral variability in motor areas. This is a baseline assumption for a computation through dynamics framework, which informs the current most powerful models in the field.

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Nina Kudryashova @ninelk.bsky.social · 07/04/2025

Excited to share our new pre-print on bioRxiv, in which we reveal that feedback-driven motor corrections are encoded in small, previously missed neural signals.

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