Objective. Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed a model-free, adaptive, closed-loop stimulation (ACLS) system that learns to use multi-site electrical stimulation to control the pattern of activity of a population of neurons. Approach. The ACLS system combined multi-electrode electrophysiological recordings with multi-site electrical stimulation to simultaneously record the activity of a population of 5–15 multiunit neurons and deliver spatially-patterned electrical stimulation across 4–16 sites. Using a closed-loop learning system, ACLS iteratively updated the pattern of stimulation to reduce the difference between the observed neural response and a specific target pattern of firing rates in the recorded multiunits. Main results. In silico and in vivo experiments showed ACLS learns to produce specific patterns of neural activity (in ∼15 min) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus. Significance. Our results show an ACLS system that can learn, in real-time, to generate specific patterns of neural activity. This work provides a framework for using model-free closed-loop learning to control neural activity.

客观的。神经活动的刺激是一种重要的科学和临床工具，用于检验假设并治疗神经退行性疾病和神经精神疾病。然而，当前的刺激方法不能灵活地控制神经元群体的活动模式。为了解决这个问题，我们开发了一个无模型、自适应、闭环刺激 (ACLS) 系统，该系统学习使用多点电刺激来控制一群神经元的活动模式。方法。ACLS 系统将多电极电生理记录与多部位电刺激相结合，同时记录 5-15 个多单元神经元的活动，并在 4-16 个部位提供空间模式的电刺激。使用闭环学习系统，ACLS 迭代更新刺激模式，以减少观察到的神经反应与记录的多单元中特定目标发射率模式之间的差异。主要结果。 在计算机和体内 实验表明，ACLS 学会产生特定的神经活动模式（在 15 分钟内），并且对神经反应中的噪声和漂移具有鲁棒性。在清醒小鼠的视觉皮层中，ACLS 学习了电刺激模式，这些模式产生的反应类似于视觉刺激引起的自然反应。与视觉刺激的重复如何导致神经反应的适应类似，对电刺激的反应在相关视觉刺激之前进行适应。意义。我们的结果显示了一个 ACLS 系统可以实时学习，以生成特定的神经活动模式。这项工作提供了一个使用无模型闭环学习来控制神经活动的框架。