example dataset to fit PLDS model, see Haimerl et al. 2023, previously published in Ruff & Cohen 2016

Sensory-guided behavior requires reliable encoding of stimulus information in neural populations, and flexible, task-specific readout. The former has been studied extensively, but the latter remains poorly understood. We introduce a theory for adaptive sensory processing based on functionally-targeted stochastic modulation. We show that responses of neurons in area V1 of monkeys performing a visual discrimination task exhibit low-dimensional, rapidly fluctuating gain modulation, which is stronger in task-informative neurons and can be used to decode from neural activity after few training trials, consistent with observed behavior. In a simulated hierarchical neural network model, such labels are learned quickly and can be used to adapt downstream readout, even after several intervening processing stages. Consistently, we find the modulatory signal estimated in V1 is also present in the activity of simultaneously recorded MT units, and is again strongest in task-informative neurons. These results support the idea that co-modulation facilitates task-adaptive hierarchical information routing.

感官引导行为（sensory-guided behavior）需要在神经集群（neural populations）中实现刺激信息（stimulus information）的可靠编码，同时具备灵活的任务特异性读出（readout）能力。前者已得到广泛研究，但后者的相关机制仍有待深入阐释。我们提出了一套基于功能靶向随机调制（functionally-targeted stochastic modulation）的自适应感官处理理论。研究表明，执行视觉辨别任务（visual discrimination task）的猴子V1区神经元的响应呈现低维、快速波动的增益调制（gain modulation）特征，该调制效应在任务信息神经元（task-informative neurons）中更为显著，且可在少量训练试次（few training trials）后用于从神经活动中解码，这与已观测到的行为表现相符。在模拟的分层神经网络模型（simulated hierarchical neural network model）中，此类调制模式可被快速学习，并能用于适配下游读出（downstream readout）操作，即便经过多个中间处理阶段（intervening processing stages）后依然有效。进一步分析显示，在V1区估算得到的调制信号，同样存在于同步记录的MT区神经元（MT units）的活动中，且在任务信息神经元中仍表现出最强的调制强度。上述结果支持了“共同调制（co-modulation）可促进任务自适应的分层信息路由（task-adaptive hierarchical information routing）”这一观点。