Chueh Shuo-Yen, Chen Yuanxin, Subramanian Narayan, Goolsby Benjamin, Navarro Phillip, Oweiss Karim
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States of America.
Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America.
J Neural Eng. 2025 May 23;22(3):036020. doi: 10.1088/1741-2552/add37b.
Brain computer interfaces (BCIs) require substantial cognitive flexibility to optimize control performance. Remarkably, learning this control is rapid, suggesting it might be mediated by neuroplasticity mechanisms operating on very short time scales. Here, we propose a meta plasticity model of BCI learning and skill consolidation at the single cell and population levels comprised of three elements: (a) behavioral time scale synaptic plasticity (BTSP), (b) intrinsic plasticity (IP) and (c) synaptic scaling (SS) operating at time scales from seconds to minutes to hours and days. Notably, the model is able to explain-a frequent and widespread phenomenon that adversely affects BCI control and continued use.We developed an all-optical approach to characterize IP, BTSP and SS with single cell resolution in awake mice using fluorescent two photon (2P) GCaMP7s imaging and optogenetic stimulation of the soma targeted ChRmine. We further trained mice on a one-dimensional BCI control task and systematically characterized within session (seconds to minutes) learning as well as across sessions (days and weeks) with different neural ensembles.On the time scale of seconds, substantial BTSP could be induced and was followed by significant IP over minutes. Over the time scale of days and weeks, these changes could predict BCI control proficiency, suggesting that BTSP and IP might be complemented by SS to stabilize and consolidate BCI control.Our results provide early experimental support for a meta plasticity model of continual BCI learning and skill consolidation. The model predictions may be used to design and calibrate neural decoders with complete autonomy while considering the temporal and spatial scales of plasticity mechanisms. With the power of modern-day machine learning and artificial Intelligence, fully autonomous neural decoding and adaptation in BCIs might be achieved with minimal to no human intervention.
脑机接口（BCIs）需要强大的认知灵活性来优化控制性能。值得注意的是，学习这种控制非常迅速，这表明它可能是由在非常短的时间尺度上运作的神经可塑性机制介导的。在这里，我们提出了一种在单细胞和群体水平上进行脑机接口学习和技能巩固的元可塑性模型，该模型由三个要素组成：（a）行为时间尺度突触可塑性（BTSP），（b）内在可塑性（IP）和（c）突触缩放（SS），它们在从秒到分钟再到小时和天的时间尺度上运作。值得注意的是，该模型能够解释一种经常出现且广泛存在的现象，这种现象会对脑机接口控制和持续使用产生不利影响。我们开发了一种全光学方法，使用荧光双光子（2P）GCaMP7s成像和对靶向体细胞的ChRmine进行光遗传学刺激，在清醒小鼠中以单细胞分辨率表征IP、BTSP和SS。我们进一步训练小鼠完成一维脑机接口控制任务，并系统地表征了会话内（秒到分钟）的学习以及不同神经群体在多个会话（天和周）中的学习情况。在秒的时间尺度上，可以诱导出大量的BTSP，随后在几分钟内出现显著的IP。在数天和数周的时间尺度上，这些变化可以预测脑机接口控制的熟练程度，这表明BTSP和IP可能由SS补充，以稳定和巩固脑机接口控制。我们的结果为持续的脑机接口学习和技能巩固的元可塑性模型提供了早期实验支持。该模型预测可用于在考虑可塑性机制的时间和空间尺度的同时，完全自主地设计和校准神经解码器。借助现代机器学习和人工智能的力量，脑机接口中的完全自主神经解码和适应可能在极少或无需人工干预的情况下实现。
