Neural Circuit Dynamics for Sensory Detection
Neural Circuit Dynamics for Sensory Detection
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DOI：
10.1523/jneurosci.2185-19.2020
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发表时间：
2020-01
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通讯作者：
Sruti Mallik;Srinath Nizampatnam;Anirban Nandi;D. Saha;B. Raman;ShiNung Ching
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作者：
Sruti Mallik;Srinath Nizampatnam;Anirban Nandi;D. Saha;B. Raman;ShiNung Ching
我们考虑的问题是，感觉网络如何使在组合编码空间的感觉刺激的检测。我们对嗅觉系统特别感兴趣，最近的实验研究报告了与刺激开始和抵消相关的丰富而神秘的反应模式的存在。我们考虑的问题是，感觉网络如何使在组合编码空间的感觉刺激的检测。我们对嗅觉系统特别感兴趣，最近的实验研究报告了与刺激开始和抵消相关的丰富而神秘的反应模式的存在。本研究旨在确定这种反应模式的功能相关性（即，在自然环境中检测刺激的背景下，这种神经活动提供了什么好处）。我们通过规范的、基于优化的建模来研究这个问题。在这里，我们定义了刺激同一性的低维潜在表征的概念，它是通过感觉网络的作用产生的。我们的优化框架的目标是以节能的方式确保在这个潜在空间中对标称表示进行高保真跟踪。结果表明，从这一框架中出现的最佳基序与在雌雄蝗虫体内观察到的原型发病和抵消反应具有形态学上的相似性。此外，这一目标可以通过一个具有相互兴奋-抑制竞争动力学的网络来实现，类似于昆虫早期嗅觉系统中投射神经元和局部神经元之间的相互作用。导出的模型还对在混杂背景信息和感觉活动能量与由此产生的行为测量（如刺激检测的速度和准确性）之间的权衡之间维持稳健的潜在表征做出了一些预测。嗅觉编码研究的一个关键领域是理解从高维感官刺激到低维解码表征的转变。在这里，我们不仅研究了这种映射的降维，还研究了它的时间动态，特别关注了时间连续的刺激。通过基于优化的综合，我们研究了感官网络如何在没有事先假设离散试验结构的情况下跟踪表征。我们表明，这种跟踪可以通过规范的网络架构和动力学来实现，并且由此产生的响应类似于昆虫嗅觉系统中神经元的观察。因此，我们的研究结果提供了关于嗅觉回路活动在单个神经元和群体尺度上的功能作用的假设。
We consider the question of how sensory networks enable the detection of sensory stimuli in a combinatorial coding space. We are specifically interested in the olfactory system, wherein recent experimental studies have reported the existence of rich, enigmatic response patterns associated with stimulus onset and offset. We consider the question of how sensory networks enable the detection of sensory stimuli in a combinatorial coding space. We are specifically interested in the olfactory system, wherein recent experimental studies have reported the existence of rich, enigmatic response patterns associated with stimulus onset and offset. This study aims to identify the functional relevance of such response patterns (i.e., what benefits does such neural activity provide in the context of detecting stimuli in a natural environment). We study this problem through the lens of normative, optimization-based modeling. Here, we define the notion of a low-dimensional latent representation of stimulus identity, which is generated through action of the sensory network. The objective of our optimization framework is to ensure high-fidelity tracking of a nominal representation in this latent space in an energy-efficient manner. It turns out that the optimal motifs emerging from this framework possess morphologic similarity with prototypical onset and offset responses observed in vivo in locusts (Schistocerca americana) of either sex. Furthermore, this objective can be exactly achieved by a network with reciprocal excitatory–inhibitory competitive dynamics, similar to interactions between projection neurons and local neurons in the early olfactory system of insects. The derived model also makes several predictions regarding maintenance of robust latent representations in the presence of confounding background information and trade-offs between the energy of sensory activity and resultant behavioral measures such as speed and accuracy of stimulus detection. SIGNIFICANCE STATEMENT A key area of study in olfactory coding involves understanding the transformation from high-dimensional sensory stimulus to low-dimensional decoded representation. Here, we examine not only the dimensionality reduction of this mapping but also its temporal dynamics, with specific focus on stimuli that are temporally continuous. Through optimization-based synthesis, we examine how sensory networks can track representations without prior assumption of discrete trial structure. We show that such tracking can be achieved by canonical network architectures and dynamics, and that the resulting responses resemble observations from neurons in the insect olfactory system. Thus, our results provide hypotheses regarding the functional role of olfactory circuit activity at both single neuronal and population scales.