Allison J Murphy, Luke Shaw, J Michael Hasse, Robbe L T Goris, Farran Briggs
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We used optogenetic methods to selectively and reversibly enhance the activity of corticogeniculate neurons in anesthetized ferrets while recording responses of LGN neurons to drifting gratings and white noise stimuli. We found that optogenetic activation of corticogeniculate feedback systematically reduced LGN gain variability and increased information coding capacity among LGN neurons. Optogenetic activation of corticogeniculate neurons generated similar increases in information encoded in LGN responses to drifting gratings and white noise stimuli. 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引用次数: 6
摘要
尽管它们在解剖学上具有稳健性,但很难确定连接初级视觉皮层和丘脑外侧膝状核(LGN)的皮质环回路在反馈方向上的功能作用。越来越多的证据表明,皮质化反馈并不直接塑造LGN神经元的空间感受野特性,而是调节LGN反应的时间和精度以及LGN神经元的信息编码能力。我们提出,皮质酸反馈特异性地稳定LGN神经元的响应增益,从而增加其信息编码能力。受McClurkin et al.(1994)早期工作的启发,我们操纵促皮质化神经元的活动来验证这一假设。我们利用光遗传学的方法选择性地、可逆地增强麻醉雪貂皮质化神经元的活性,同时记录下LGN神经元对漂移光栅和白噪声刺激的反应。我们发现,光遗传激活的皮质化反馈系统地降低了LGN增益变异性,增加了LGN神经元之间的信息编码能力。光遗传激活皮质化神经元对漂移光栅和白噪声刺激的反应产生了类似的信息编码增加。总之,这些发现表明,促肾上腺皮质激素反馈对LGN反应精度和信息编码能力的影响可以通过减少增益变异性来介导。
Optogenetic activation of corticogeniculate feedback stabilizes response gain and increases information coding in LGN neurons.
In spite of their anatomical robustness, it has been difficult to establish the functional role of corticogeniculate circuits connecting primary visual cortex with the lateral geniculate nucleus of the thalamus (LGN) in the feedback direction. Growing evidence suggests that corticogeniculate feedback does not directly shape the spatial receptive field properties of LGN neurons, but rather regulates the timing and precision of LGN responses and the information coding capacity of LGN neurons. We propose that corticogeniculate feedback specifically stabilizes the response gain of LGN neurons, thereby increasing their information coding capacity. Inspired by early work by McClurkin et al. (1994), we manipulated the activity of corticogeniculate neurons to test this hypothesis. We used optogenetic methods to selectively and reversibly enhance the activity of corticogeniculate neurons in anesthetized ferrets while recording responses of LGN neurons to drifting gratings and white noise stimuli. We found that optogenetic activation of corticogeniculate feedback systematically reduced LGN gain variability and increased information coding capacity among LGN neurons. Optogenetic activation of corticogeniculate neurons generated similar increases in information encoded in LGN responses to drifting gratings and white noise stimuli. Together, these findings suggest that the influence of corticogeniculate feedback on LGN response precision and information coding capacity could be mediated through reductions in gain variability.
期刊介绍:
The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.