Neural structure of a sensory decoder for motor control.
Neural structure of a sensory decoder for motor control.
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DOI：
10.1038/s41467-022-29457-4
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发表时间：
2022-04-05
影响因子：
16.6
通讯作者：
Lisberger SG
中科院分区：
文献类型：
--
作者：
Egger SW;Lisberger SG
感觉输入到运动输出的转换通常被认为是对神经表征进行操作的解码器。我们通过模仿解码器设计中的神经回路来寻求对感觉解码的机械理解。一个简单实验的结果塑造了我们的方法。为了平滑追踪眼球运动而改变目标的大小会改变诱发行为的方差和均值之间的关系，这与“信号依赖噪声”的机制相矛盾，也与传统的解码方法相矛盾。理论分析使我们提出了一个电路的追求，包括多个平行的途径和多个来源的变化。具有仿生统计的行为和神经反应来自生物激励的电路模型，该模型在通路中具有噪声，该通路致力于灵活地调节视觉运动传输的强度。我们的研究结果证明了重新想象解码的力量，即通过神经系统的并行通路进行处理。行为变异被认为是由感觉表征或最终运动命令中的噪音引起的。在这项研究中，作者调查的眼睛运动的变化和模型的变化，造成嘈杂的感觉运动的转换发生在中颞视觉区。
The transformation of sensory input to motor output is often conceived as a decoder operating on neural representations. We seek a mechanistic understanding of sensory decoding by mimicking neural circuitry in the decoder’s design. The results of a simple experiment shape our approach. Changing the size of a target for smooth pursuit eye movements changes the relationship between the variance and mean of the evoked behavior in a way that contradicts the regime of “signal-dependent noise” and defies traditional decoding approaches. A theoretical analysis leads us to propose a circuit for pursuit that includes multiple parallel pathways and multiple sources of variation. Behavioral and neural responses with biomimetic statistics emerge from a biologically-motivated circuit model with noise in the pathway that is dedicated to flexibly adjusting the strength of visual-motor transmission. Our results demonstrate the power of re-imagining decoding as processing through the parallel pathways of neural systems. Behavioral variation is thought to result from noise in sensory representations or final motor commands. In this study, the authors investigate variability in eye movements and model that variability as resulting from noisy sensorimotor transformations occurring in the middle temporal visual area.
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影响因子：
16.2
作者：
Churchland AK;Kiani R;Chaudhuri R;Wang XJ;Pouget A;Shadlen MN
通讯作者：
Shadlen MN
影响因子：
1.9
作者：
BRITTEN, KH;SHADLEN, MN;MOVSHON, JA
通讯作者：
MOVSHON, JA
影响因子：
16.2
作者：
Beck JM;Ma WJ;Kiani R;Hanks T;Churchland AK;Roitman J;Shadlen MN;Latham PE;Pouget A
通讯作者：
Pouget A
影响因子：
16.2
作者：
Drugowitsch, Jan;Wyart, Valentin;Koechlin, Etienne
通讯作者：
Koechlin, Etienne
影响因子：
2.5
作者：
Born, RT
通讯作者：
Born, RT