认知任务中异质神经反应的潜在电路推断，这一成果由普林斯顿大学Tatiana A. Engel研究组经过不懈努力而取得。该项研究成果发表在2025年2月10日出版的《自然—神经科学》上。

该课题组人员开发了潜在回路模型，这是一种降维方法，其中任务变量通过低维循环连接相互作用以产生行为输出。该研究组将潜在电路推理应用于训练用于执行上下文依赖决策任务的递归神经网络，并发现上下文表征抑制无关感觉反应的抑制机制。研究组通过确认由潜在电路模型预测的模式连接扰动的行为效应来验证这一机制。

该研究组发现，在执行相同任务的猴子的前额叶皮层中，也存在类似的对无关感觉反应的抑制。课题组表明，结合任务变量之间的情感相互作用对于从神经反应数据中识别行为相关计算至关重要。

据介绍，较高的皮质区域携带广泛的感觉、认知和运动信号，这些信号混合在单个神经元对多个任务变量的异质反应中。依赖于神经活动和任务变量之间的相关性的降维方法不知道如何从连接中产生异质反应来驱动行为。

附：英文原文

Title: Latent circuit inference from heterogeneous neural responses during cognitive tasks

Author: Langdon, Christopher, Engel, Tatiana A.

Issue&Volume: 2025-02-10

Abstract: Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by confirming the behavioral effects of patterned connectivity perturbations predicted by the latent circuit model. We find similar suppression of irrelevant sensory responses in the prefrontal cortex of monkeys performing the same task. We show that incorporating causal interactions among task variables is critical for identifying behaviorally relevant computations from neural response data.

DOI: 10.1038/s41593-025-01869-7

Source: https://www.nature.com/articles/s41593-025-01869-7