用单模型同时预测神经活动和解码行为，效率更高。
Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale
- 用Mamba模型预测下一时刻的神经元放电率，一步完成
- 解码准确率达75.7%（比随机高2.3倍），100多试次即可校准
- 适合需要低延迟实时解码的脑机接口研究
闭环脑机接口通常需要预测未来神经群体活动并读取动物行为状态。一个仅在Neuropixels尺度下下一时刻放电计数上训练的轻量级Mamba预测器，可在一次前向传播中同时实现两项任务。通过在模型预测率上添加简单的会话级线性头，其行为解码性能优于直接使用原始放电计数的线性分类器，在相同时间上下文中表现更优。在包含39个会话、约27,000个神经元和1,994个保留测试试验的Steinmetz视觉辨别基准上测试：三个训练种子下，模型预测的鼠类选择正确率为75.7±0.2%（约2.3倍于随机水平），刺激侧分辨为66.1±0.6%（约两倍于随机）。相比基于500毫秒上下文的原始放电计数线性解码器，该方法在响应与刺激侧解码上分别提升4-6个百分点。约100–150个试次的会话起始校准块即可使读出结果接近最优性能，整个流程可控制在50毫秒内，适配典型有线慢性Neuropixels记录的桌面级GPU计算资源。
原文摘要 · Abstract (English)
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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