Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale

John R. Minnick; Jesus Gonzalez-Ferrer; Kamran Hussain; Jinghui Geng; Ash Robbins; Mohammed A. Mostajo-Radji; David Haussler; Jason Eshraghian

q-bio.NCMay 13, 2026

Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale

Authors: John R. Minnick, Jesus Gonzalez-Ferrer, Kamran Hussain, Jinghui Geng, Ash Robbins, Mohammed A. Mostajo-Radji, David Haussler, Jason Eshraghian, +1 more

Organizations: Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA · UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA · Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA · Department of Applied Mathematics, University of California, Santa Cruz, CA, USA · Department of Computer Science and Engineering, University of California, Santa Cruz, CA, USA

Abstract

Closed-loop brain-computer interfaces often require both a forecast of upcoming neural population activity and a readout of the animal's behavioral state. A single Mamba forecaster, trained only on next-step spike counts at Neuropixels scale, can deliver both in one forward pass. A lightweight per-session linear head reading the model's predicted rates decodes behavior better than the same linear classifier reading the raw spike counts, under matched temporal context. We test on the Steinmetz visual-discrimination benchmark, which spans 39 sessions, roughly 27,000 neurons, and 1,994 held-out trials. Across three training seeds, Mamba's predicted rates decode mouse choice at 75.7 $\pm$ 0.2% trial vote, roughly 2.3 times chance level, and stimulus side at 66.1 $\pm$ 0.6%, about twice chance. Compared to a matched 500 ms-context linear decoder on the raw spike counts, Mamba wins at trial vote by 4-6 pp on response and 4-6 pp on stimulus side. A session-start calibration block of about 100-150 trials brings the readout within 1-2 pp of asymptote, and the full pipeline fits inside the 50 ms bin budget on workstation-class GPUs typical of tethered chronic Neuropixels recordings.

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Aug 5, 2026 · cs.LGJ/K move · Enter open · S save

NeuroPB: Scaling Neural Decoding with Pretrained Behavioral Representations

Luyao Jin, Yonghao Song, Huan Zhao, Vincent C. K. Cheung+1

Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong · Department of Biomedical Engineering, School of Medicine, Tsinghua University · School of Biomedical Sciences, The Chinese University of Hong Kong

Decoding continuous motor trajectories from neural activity is essential for developing practical brain-computer interfaces (BCIs). However, current neural decoders are constrained by the limited scale and heterogeneity of neural recordings. In contrast, behavioral data can be collected more readily and at substantially larger scale from humans, animals, simulations, and robotic systems. Here, we introduce NeuroPB, a framework that scales neural decoding by transferring knowledge from pretrained behavioral representations. NeuroPB first pretrains a motor encoder on large-scale motor behavior data and then aligns neural activity with the resulting behavioral representation space using a limited set of paired neural-behavioral recordings. A neural encoder and lightweight motor decoder are subsequently optimized to reconstruct continuous movement from the aligned neural representations. Across multiple macaque motor datasets, behavioral pretraining improves trajectory decoding, including an 11%

R^2

increase on center-out and 8% on random-target compared with training the motor encoder from scratch. Notably, pretraining on robotic trajectories achieves performance comparable to pretraining on macaque trajectories, demonstrating that transferable kinematic structure is shared across biological and artificial models. Moreover, decoding performance improves as the scale and diversity of robotic pretraining data increase, when the amount of neural data is fixed. Pretraining also enhances generalization across recording sessions, subjects, and motor tasks, with only 10% calibration needed to match training from scratch. Overall, these results establish behavioral pretraining as a scalable source for neural decoding and provide a promising route toward high-performance and calibration-efficient BCIs under limited neural data.

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Abstract

Explore similar work

NeuroPB: Scaling Neural Decoding with Pretrained Behavioral Representations

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