Color Processing in the Early Visual System of Drosophila

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Shared Song Detector Neurons in Drosophila Male and Female Brains Drive Sex-Specific Behaviors

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Functional architecture of neural circuits for leg proprioception in Drosophila

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Mechanisms of lactic acid gustatory attraction in Drosophila

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Neural network modeling of batch cell growth pattern

The capability of neural networks in modeling batch cell growth by providing initial conditions only is tested in this study. The neural network tested is of the back-propagation-type including a newly discovered saturation-type transfer function. The simulation and prediction results of this neural network modeling will be demonstrated. (c) 1993 John Wiley & Sons, Inc.     

Object-Displacement-Sensitive Visual Neurons Drive Freezing in Drosophila

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Dissimilarities in auditory tuning in midwife toads of the genus Alytes (Amphibia: Anura)

The auditory sensitivity in three species of the anuran genus Alytes (Alytidae) was examined to determine patterns of intra‐ and interspecific variation, relating these measurements to behavioural preferences measured in previous studies and to the adaptive and evolutionary significance of this sensory function. The audiograms obtained with multi‐unit recordings in the torus semicircularis of 13 Alytes cisternasii, 10 Alytes obstetricans, and eight Alytes dickhilleni show two regions of enhanced sensitivity, between approximately 100–500 and 1200–2400 Hz, with minimum thresholds at approximately 40 and 45 dB SPL, respectively. The mean and range of the high‐frequency region differed among species, although the sensitivity, measured as minimum thresholds, was similar. The region of high‐frequency sensitivity was centred at approximately the frequency of the advertisement call in A. cisternasii but, in A. obstetricans and A. dickhilleni, was centred at frequencies higher than the conspecific calls. These results contrast with preferences for lower frequencies exhibited by Alytes in female phonotactic and in male evoked vocal responses. Such loose relationships between signals and receivers suggest that the divergence of the sound communication system in Alytes has implied environmental and phylogenetic factors in addition to sexual selection processes.     

A Neural Basis for Categorizing Sensory Stimuli to Enhance Decision Accuracy

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A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans

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Tracing the development of learned song preferences in the female zebra finch brain with functional magnetic resonance imaging

In sexually dimorphic zebra finches (Taeniopygia guttata), only males learn to sing their father's song, whereas females learn to recognize the songs of their father or mate but cannot sing themselves. Memory of learned songs is behaviorally expressed in females by preferring familiar songs over unfamiliar ones. Auditory association regions such as the caudomedial mesopallium (CMM; or caudal mesopallium) have been shown to be key nodes in a network that supports preferences for learned songs in adult females. However, much less is known about how song preferences develop during the sensitive period of learning in juvenile female zebra finches. In this study, we used blood-oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to trace the development of a memory-based preference for the father's song in female zebra finches. Using BOLD fMRI, we found that only in adult female zebra finches with a preference for learned song over novel conspecific song, neural selectivity for the father's song was localized in the thalamus (dorsolateral nucleus of the medial thalamus; part of the anterior forebrain pathway, AFP) and in CMM. These brain regions also showed a selective response in juvenile female zebra finches, although activation was less prominent. These data reveal that neural responses in CMM, and perhaps also in the AFP, are shaped during development to support behavioral preferences for learned songs.     

预测性编码在视听觉神经活动中的典型表征

现代神经科学研究指出,大脑是外部世界的“预测器”,它能根据先验知识和当前信息对即将到来的感觉信息进行主动估计,从而完成与外部世界的高效交互。预测性编码是描述预期作用机制的主要理论模型,梳理其在解释视、听觉神经现象方面的研究进展,可为深入理解大脑工作模式提供新的理论基础。本文简述了预测性编码的内容;从常用范式、典型现象、面临争议等方面梳理预期与感觉输入相互作用的典型研究;从有预期无刺激的神经表征、预期相关神经振荡模式两方面简述预期独立于刺激的内源性神经表征;进而回顾了支持预测性编码中分级结构的神经生理证据及重要神经结构。最后,本文从深化理论研究、助力疾病诊疗、启发脑-机接口技术等方面对预测性编码相关研究的发展进行了展望。深入理解预测性编码在视、听觉神经活动中的计算模型及神经表征,有望为揭示大脑感知觉神经活动工作模式开辟新途径。     

听觉选择性注意的可塑性及其神经机制研究

本文旨在研究音乐训练是否能增强基于音高和空间位置的听觉选择性注意力,以及音乐训练对听觉可塑性的神经机制.听觉感知实验中,受试者根据音高差异或空间位置差异,选择两个同时播放的数字之一.听觉认知实验在安静和噪声环境中播放频率分辨率不同的复合音,记录受试者听觉脑干频率跟随响应(frequency-following responses,FFRs).本文提出分析FFR的四种方法,即包络相关频率跟随响应(envelope-related frequency-following response,FFRENV)的短时锁相值、瞬时相位差极性图、相位差均值矢量以及时间细节结构相关频率跟随响应(temporal-fine-structure-related frequency-following response,FFRTFS)的幅度谱信噪比.实验结果表明,在完成基于音高的任务时,受过音乐训练的受试者准确率更高、反应时间更短.外界噪声不影响两组人群在基频(fundamental frequency,F0)的神经元锁相能力,但是显著降低了谐波处的神经元锁相能力.受过音乐训练的受试者的神经元在基频处的锁相能力和谐波处抗噪能力均增强,且其FFRTFS幅度谱信噪比与基于音高的行为学准确率呈正相关.因此,受过音乐训练的受试者其音高选择性注意感知能力的提高取决于认知神经能力的增强,经过音乐训练后,F0处FFRENV的锁相能力、谐波处FFRTFS的抗噪和持续锁相能力以及谐波处FFRTFS幅度谱信噪比均明显增强.音乐训练对听觉选择性注意具有显著的可塑性.     

行道树叶绿素变化的高光谱神经网络模型

采样分析了城市行道树（长春市主要街道）与对比区（净月潭国家森林公园）相应树种的叶绿素变化,并对由叶绿索变化引起的光谱反射率以ASD光谱仪进行了测试,并对二者之间的关系进行了单波段回归分析、V1植被指数与叶绿素含量的模型分析以及神经网络模型分析。结果表明,1）城市环境对行道树叶绿素有重要影响,但是对针叶树种响应较小,而阔叶树种响应较大;2）光谱反射率与测试树种叶绿素含量关系密切,在740～760nm附近确定性系数达0．72以上;3）PSSR植被指数与测试树种的叶绿素含量关系密切,幂函数回归的决定系数R^2为0．82左右。3）神经网络模型能够提高光谱反射率模型反演植被叶绿素含量的水平,模型的决定系数R^2高达0．97。表明高光谱遥感可以用来监测因城市环境引起的植被叶绿素变化。     

神经网络预警系统及其在害虫预测中的应用

利用神经网络的基本原理 ,结合气象因子以及江苏通州市田间褐飞虱发生程度的实测数据 ,建立了该地的神经网络长期预警系统。经实例验证 ,该方法的预测精度达 80 % ,为害虫的长期可预测性提供了一种新的方法     

Generating parallel representations of position and identity in the olfactory system

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Behavioral signatures of structured feature detection during courtship in Drosophila

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High-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples

White light phase-shifting interference microscopy (WL-PSIM) is a prominent technique for high-resolution quantitative phase imaging (QPI) of industrial and biological specimens. However, multiple interferograms with accurate phase-shifts are essentially required in WL-PSIM for measuring the accurate phase of the object. Here, we present single-shot phase-shifting interferometric techniques for accurate phase measurement using filtered white light (520±36 nm) phase-shifting interference microscopy (F-WL-PSIM) and deep neural network (DNN). The methods are incorporated by training the DNN to generate (a) four phase-shifted frames and (b) direct phase from a single interferogram. The training of network is performed on two different samples i.e., optical waveguide and MG63 osteosarcoma cells. Further, performance of F-WL-PSIM+DNN framework is validated by comparing the phase map extracted from network generated and experimentally recorded interferograms. The current approach can further strengthen QPI techniques for high-resolution phase recovery using a single frame for different biomedical applications.