Nguyen Tuan, Miller Kenneth D, Palmigiano Agostina
Center for Theoretical Neuroscience, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY.
Dept. of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY.
bioRxiv. 2025 May 2:2025.04.28.651100. doi: 10.1101/2025.04.28.651100.
How does the brain integrate sensory inputs with non-feature-tuned signals, such as those arising from behavioral state changes or neuromodulation? Here, we show that the dynamics of disordered E/I networks with structured, feature-dependent connectivity can be well characterized by an effective model describing interactions between the responses of cells who prefer the current sensory stimulus ("matched" cells) and the responses of cells firing at the baseline. This effective network exhibits strong feedback from the baseline onto the matched responses but weak reverse projections. Thus, an untuned stimulus not only directly drives matched cells, but also indirectly drives them via modulation of the baseline. We demonstrate through a linear response analysis that the baseline effect on the matched response is suppressive if the network is strongly coupled and feedback-inhibition dominated. In particular, in this regime, feature-dependent networks produce "rate reshuffling", wherein untuned optogenetic excitation yields large changes in the individual responses of matched cells without significantly changing their overall firing rate distribution, as the optogenetically-induced baseline response suppresses the matched response. Finally, if multiple sensory stimuli are presented, yielding sublinear response summation ("normalization"), the influence of the baseline on the matched responses is weakened. Thus, an untuned (., optogenetic) stimulus is less suppressive to multiple stimuli than to a single stimulus, making normalization effectively weaker in the presence of an untuned stimulus. Our framework provides the first theory of the interaction of untuned modulatory and tuned sensory inputs, reconciles prior experiments, and provides testable predictions about tuned-untuned interactions in cortical processing.
大脑如何将感觉输入与非特征调谐信号整合在一起，比如那些由行为状态变化或神经调节产生的信号？在这里，我们表明，具有结构化、特征依赖性连接的无序兴奋/抑制（E/I）网络的动力学可以通过一个有效模型得到很好的描述，该模型描述了偏好当前感觉刺激的细胞（“匹配”细胞）的反应与在基线水平放电的细胞的反应之间的相互作用。这个有效网络表现出从基线到匹配反应的强烈反馈，但反向投射较弱。因此，一个未调谐的刺激不仅直接驱动匹配细胞，还通过对基线的调制间接驱动它们。我们通过线性响应分析表明，如果网络是强耦合且以反馈抑制为主，那么基线对匹配反应的影响是抑制性的。特别是，在这种情况下，特征依赖性网络会产生“速率重排”，其中未调谐的光遗传学兴奋会在匹配细胞的个体反应中产生很大变化，而不会显著改变它们的整体放电率分布，因为光遗传学诱导的基线反应会抑制匹配反应。最后，如果呈现多个感觉刺激，产生亚线性反应总和（“归一化”），那么基线对匹配反应的影响就会减弱。因此，一个未调谐的（如光遗传学）刺激对多个刺激的抑制作用比对单个刺激的抑制作用小，使得在存在未调谐刺激的情况下归一化实际上更弱。我们的框架提供了第一个关于未调谐调制和调谐感觉输入相互作用的理论，调和了先前的实验，并提供了关于皮质处理中调谐 - 未调谐相互作用的可测试预测。
