Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception.
Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception.
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DOI：
10.1126/science.aba2357
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发表时间：
2020-06-19
期刊：
影响因子：
--
通讯作者：
Rinberg D
中科院分区：
文献类型：
--
作者：
Chong E;Moroni M;Wilson C;Shoham S;Panzeri S;Rinberg D
神经活动如何产生知觉？寻找空间或时间活动特征（如神经元身份或潜伏期）的组合，是感知的结果仍然具有挑战性。我们训练小鼠识别由参数定义的光遗传激活模式构建的合成气味，然后测量时空维度上广泛和受控扰动期间的感知变化。我们将识别建模为模式与学习模板的匹配。最好的预测识别的模板是空间识别单元的序列，按相对于彼此的lavian排序（具有最小的嗅闻效应）。在模板中，个别单位的贡献是相加的，更大的贡献来自更早激活的单位。我们的综合方法揭示了嗅觉代码的基本逻辑，并提供了一个通用的框架来测试感觉活动和感知之间的联系。(A)我们训练老鼠识别合成气味模式：人工刺激嗅球的神经活动。模式在空间（右上）和时间（右下）中定义。(B)通过对训练模式的系统修改来测量感知反应。(C)模式活动的模板匹配模型（左）解释了感知反应（右）。在大尺度和精细尺度上监测大脑活动的进展揭示了大脑如何响应和代表外部世界的巨大复杂性。虽然大脑活动模式中的许多特征（哪些脑细胞在何时放电）被发现与外部感官世界的变化相关，但尚不清楚哪些活动特征对感知是重要的，以及它们是如何结合起来产生感知的。一些研究表明，这些活动中的许多相关变化可能是多余的，甚至是副作用。为了解决大脑活动如何产生感知，我们直接和系统地操纵小鼠嗅觉系统中的神经活动，同时测量感知反应。小鼠嗅觉是一个很有吸引力的模型系统，因为相关的大脑回路已经被仔细绘制出来，并且可以直接操作。我们使用了基因工程小鼠，在这些小鼠中，脑细胞可以简单地通过照射它们来激活-这种技术被称为光遗传学。光遗传学使我们能够以精确和参数化的方式直接产生和操纵大脑活动。我们首先训练小鼠识别嗅觉系统中光驱动的活动模式，或“合成气味”。随后，我们测量了当我们系统地操纵学习到的活动模式时，识别是如何变化的。一些操作导致更大的变化，在识别比其他人，和变化的程度反映了每个操纵的功能感知的重要性。通过同时对多个特征进行额外的操作，我们可以精确地量化各个特征是如何组合起来产生感知的。小鼠的感知反应不仅取决于哪组细胞被激活，还取决于它们的激活时间，即，类似于旋律中定时音符的时间序列。关键的是，感知上最相关的激活时间是相对于序列中的其他细胞而不是大脑或身体节律来定义的（例如，动物嗅闻），如先前从观察性研究中假设的。此外，序列中较早激活的细胞对行为反应有较大的影响;修改序列中较晚激活的细胞影响较小。为了解释所有的结果，我们制定了一个简单的计算模型的基础上模板匹配，新的活动序列进行比较与学习序列或模板。该模型权衡了每个序列中的相对时间，并考虑了早期激活细胞的重要性。基于我们的模型，新序列和学习模板之间的不匹配程度预测了随着神经活动在许多不同操作中的变化，识别应该下降的程度。我们开发了一个实验和理论框架，以映射精确和系统操纵的大脑活动模式到行为反应的广阔空间。使用这个框架，我们发现了嗅觉系统对神经活动产生感知的关键计算，并导出了与感知直接相关的嗅觉处理的系统模型。我们的框架形成了一个强大的，通用的方法来因果测试大脑活动和感知或行为之间的联系。考虑到用于在各个大脑区域细尺度上操纵大脑活动的先进工具的持续发展，这个框架尤其重要。▪
How does neural activity generate perception? Finding the combinations of spatial or temporal activity features (such as neuron identity or latency) that are consequential for perception remains challenging. We trained mice to recognize synthetic odors constructed from parametrically defined patterns of optogenetic activation, then measured perceptual changes during extensive and controlled perturbations across spatiotemporal dimensions. We modeled recognition as the matching of patterns to learned templates. The templates that best predicted recognition were sequences of spatially identified units, ordered by latencies relative to each other (with minimal effects of sniff). Within templates, individual units contributed additively, with larger contributions from earlier-activated units. Our synthetic approach reveals the fundamental logic of the olfactory code and provides a general framework for testing links between sensory activity and perception. (A) We trained mice to recognize synthetic odor patterns: artificially stimulated neural activity in the olfactory bulb. Patterns were defined in space (top right) and time (bottom right). (B) Perceptual responses were measured across systematic modifications of trained patterns. (C) Template-matching model of pattern activity (left) accounts for perceptual responses (right). Advances in monitoring brain activity at large and fine scales have revealed tremendous complexity in how the brain responds to, and represents, the external world. Although many features in brain activity patterns (which brain cells fire and when) are found to correlate with changes in the external sensory world, it is not yet known which activity features are consequential for perception and how they are combined to generate percepts. Some studies have shown that many of these correlated changes in activity may be redundant or even epiphenomenal. To address how brain activity generates perception, we directly and systematically manipulated neural activity in the mouse olfactory system while measuring perceptual responses. Mouse olfaction is an attractive model system because the relevant brain circuitry has already been carefully mapped out and is accessible for direct manipulation. We used genetically engineered mice in which brain cells can be activated simply by shining light on them— a technique known as optogenetics. Optogenetics allowed us to directly generate and manipulate brain activity in a precise and parametric manner. We first trained mice to recognize light-driven activity patterns in the olfactory system, or “synthetic odors.” Subsequently, we measured how recognition changed as we systematically manipulated learned activity patterns. Some manipulations led to larger changes in recognition than others, and the degree of change reflected the importance of each manipulated feature to perception. By the additional manipulation of multiple features simultaneously, we could precisely quantify how individual features combined to produce perception. The perceptual responses of mice not only depended on which groups of cells were activated, but also on their activation latencies, i.e., temporal sequences akin to timed notes in a melody. Critically, the most perceptually relevant activation latencies were defined relative to other cells in a sequence and not to brain or body rhythms (e.g., animal sniffing) as previously hypothesized from observational studies. Moreover, earlier-activated cells in the sequence had a larger effect on behavioral responses; modifying later cells in the sequence had small effects. To account for all results, we formulated a simple computational model based on template matching, in which new activity sequences are compared with learned sequences or templates. The model weighs relative timing within each sequence and also accounts for the greater importance of earlier-activated cells. Based on our model, the degree of mismatch between the new sequence and learned template predicts the extent to which recognition should degrade as neural activity changes across many different manipulations. We developed an experimental and theoretical framework to map a broad space of precisely and systematically manipulated brain activity patterns to behavioral responses. Using this framework, we uncovered key computations made by the olfactory system on neural activity to generate percepts and derived a systematic model of olfactory processing directly relevant for perception. Our framework forms a powerful, general approach for causally testing the links between brain activity and perception or behavior. This framework is especially pertinent given the continued development of advanced tools for manipulating brain activity at fine scales across various brain regions. ▪
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影响因子：
16.6
作者：
Arneodo EM;Penikis KB;Rabinowitz N;Licata A;Cichy A;Zhang J;Bozza T;Rinberg D
通讯作者：
Rinberg D
影响因子：
25
作者：
Haddad, Rafi;Lanjuin, Anne;Madisen, Linda;Zeng, Hongkui;Murthy, Venkatesh N.;Uchida, Naoshige
通讯作者：
Uchida, Naoshige
影响因子：
64.8
作者：
HOPFIELD, JJ
通讯作者：
HOPFIELD, JJ
影响因子：
16.2
作者：
Franks, KM;Isaacson, JS
通讯作者：
Isaacson, JS
影响因子：
5.5
作者：
HUBEL, DH;WIESEL, TN
通讯作者：
WIESEL, TN