Sanzeni Alessandro, Palmigiano Agostina, Nguyen Tuan H, Luo Junxiang, Nassi Jonathan J, Reynolds John H, Histed Mark H, Miller Kenneth D, Brunel Nicolas
Department of Computing Sciences, Bocconi University, 20100 Milan, Italy; Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neurobiology, Duke University, Durham, NC 27710, USA.
Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
Neuron. 2023 Dec 20;111(24):4102-4115.e9. doi: 10.1016/j.neuron.2023.09.018. Epub 2023 Oct 20.
The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low-/high-rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling.
通过光遗传学手段干扰神经回路的能力为研究控制回路功能的机制打开了一扇前所未有的窗口。我们分析并从理论上模拟了小鼠和猴初级视觉皮层（V1）中神经元对视觉和光遗传学输入的反应。在这两个物种中，对兴奋性神经元的光遗传学刺激强烈调节了单个神经元的活动，但对整个群体的放电率分布影响微弱或没有影响。因此，光遗传学输入重新排列了整个网络的放电率。小鼠和猴反应的关键统计数据处于连续状态，小鼠/猴分别占据低/高放电率区域。我们表明，只要耦合强度（递归耦合和外部输入的组合）足以使强大的抑制性反馈抵消平均光遗传学输入，神经元重新排列现象就会普遍出现在随机连接的兴奋性/抑制性网络中。一个更现实的模型，在结构化网络中区分经过调谐的视觉输入与未经调谐的光遗传学输入，降低了解释重新排列所需的耦合强度。
