Kanashiro Tatjana, Ocker Gabriel Koch, Cohen Marlene R, Doiron Brent
Program for Neural Computation, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, United States.
Department of Mathematics, University of Pittsburgh, Pittsburgh, United States.
Elife. 2017 Jun 7;6:e23978. doi: 10.7554/eLife.23978.
The circuit mechanisms behind shared neural variability (noise correlation) and its dependence on neural state are poorly understood. Visual attention is well-suited to constrain cortical models of response variability because attention both increases firing rates and their stimulus sensitivity, as well as decreases noise correlations. We provide a novel analysis of population recordings in rhesus primate visual area V4 showing that a single biophysical mechanism may underlie these diverse neural correlates of attention. We explore model cortical networks where top-down mediated increases in excitability, distributed across excitatory and inhibitory targets, capture the key neuronal correlates of attention. Our models predict that top-down signals primarily affect inhibitory neurons, whereas excitatory neurons are more sensitive to stimulus specific bottom-up inputs. Accounting for trial variability in models of state dependent modulation of neuronal activity is a critical step in building a mechanistic theory of neuronal cognition.
共享神经变异性（噪声相关性）背后的电路机制及其对神经状态的依赖性目前仍知之甚少。视觉注意力非常适合用于约束反应变异性的皮层模型，因为注意力既能提高 firing 率及其对刺激的敏感性，还能降低噪声相关性。我们对恒河猴视觉区域 V4 的群体记录进行了一项新颖的分析，结果表明，单一的生物物理机制可能是这些不同的注意力神经关联的基础。我们探索了模型皮层网络，其中自上而下介导的兴奋性增加分布在兴奋性和抑制性目标上，捕捉到了注意力的关键神经元关联。我们的模型预测，自上而下的信号主要影响抑制性神经元，而兴奋性神经元对刺激特异性的自下而上输入更为敏感。在构建神经元认知的机制理论时，考虑神经元活动状态依赖性调制模型中的试验变异性是关键的一步。
