Spontaneous activity in gustatory cortex

描述（由申请人提供）：PI是石溪大学神经生物学和行为学系的教员。他的研究重点是味觉皮层的自发和诱发神经活动。这位候选人最近从理论物理背景过渡到神经科学。该奖项将为候选人提供必要的五年保护期，在拟议的指导委员会的监督下接受系统和理论神经科学和电生理技术的培训。在学期结束时，候选人将实现其长期职业目标，即成功申请独立研究基金并成为理论神经科学领域的独立研究者。提出的研究计划的目的是阐明味觉皮层自发活动和诱发活动之间的关系，以及预期线索对其的调节。感觉皮质的自发神经活动，传统上被认为是一个嘈杂的基线，越来越引起人们的注意：它可以预测试验间的可变性；它包含了关于神经网络功能架构的信息；它的特征要么是可能的网络活动的“剧目”，要么是由发展形成的对感官刺激的期望的存储。然而，在味觉加工的背景下，它几乎没有受到关注。这一建议的目的是阐明味觉皮层自发活动的性质。这是一个理想的系统，因为对味觉刺激的反应具有丰富的时间动态，表现在味觉特定的活动模式序列上，在整个神经群中同步。该计划的第一个目标是采用最先进的机器学习技术来阐明与诱发活动相关的自发活动的特征。这将为自发活动的表征提供新的分析工具，并通过引入时间动力学的新特征，在单一框架内调和目前在文献中持有的不同观点。本研究的第二个目标是阐明预期线索在调节自发活动和诱发活动之间的关系中所起的作用，并阐明反应时间对自发活动的依赖性。我们将介绍一个生物学上合理的味觉皮层自发活动模型，该模型基于脉冲神经元网络。该模型将对电生理数据分析所揭示的自发活动特征产生机制理解。

DESCRIPTION (provided by applicant): The PI is a faculty member of the Department of Neurobiology and Behavior at Stony Brook. His research focuses on spontaneous and evoked neural activity in the gustatory cortex. The candidate recently transitioned to neuroscience from a theoretical physics background. This award will provide the necessary five-year protected period for the candidate to receive training in systems and theoretical neuroscience and electrophysiological techniques, under the supervision of the proposed mentorship committee. By the end of the termed period, the candidate will achieve his long term career goals of successfully applying for independent research funding and becoming an independent investigator in the field of theoretical neuroscience. The goal of the proposed research plan is elucidating the relation between spontaneous and evoked activity in the gustatory cortex, and its modulation by anticipatory cues. Spontaneous neural activity in sensory cortices, traditionally regarded as a noisy baseline, is increasingly drawing attention: it can predict trial to trial variability; it contains information on the functional architecture of neural networks; and it was characterized as either a "repertoire" of possible network activities or a storage of expectations of sensory stimuli, shaped by development. In the context of taste processing, however, it has received little to no attention. The aim of this proposal is to elucidate the properties of spontaneous activity in the gustatory cortex. This is an ideal system, due to the rich temporal dynamics of responses to taste stimuli, represented in terms of taste-specific sequences of patterns of activity, synchronized across the whole neural population. The first goal of the proposed plan is to employ state-of-the-art machine learning techniques to elucidate the features of spontaneous activity in relation to evoked activity. This will provide new analytical tools for the characterization of spontaneous activity and reconcile within a single framework the different views currently held in the literature, by introducing the new feature of temporal dynamics. As a second goal of the proposed project, the role played by anticipatory cues in modulating the relation between spontaneous and evoked activity will be elucidated, and the dependence of reaction times on spontaneous activity will be clarified. A biologically plausible model of spontaneous activity in the gustatory cortex will be introduced, based on networks of spiking neurons. This model will yield a mechanistic understanding of the feature of spontaneous activity as revealed by the analysis of electrophysiological data.