Review on Olfactory and Visual Neural Pathways in Drosophila
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摘要: 果蝇嗅觉和视觉神经系统对于自然环境中嗅觉和视觉刺激具有高度的灵敏性，高灵敏的嗅视单模态感知决策和跨模态协同决策机制为仿生应用提供一定的启示作用。该文首先以果蝇嗅觉和视觉神经系统为基础，从嗅觉和视觉信号的捕获、加工、决策3个部分概述了果蝇嗅觉和视觉神经单模态感知决策生理机制与计算模型的研究现状，同时对果蝇嗅觉和视觉神经跨模态协同决策生理机制与计算模型进行阐述；然后对果蝇嗅觉和视觉的单模态感知和跨模态协同的典型仿生应用进行归纳；最后总结果蝇嗅视神经通路生理机制与计算建模当前面临的难题并展望未来发展趋势，为未来相关研究工作奠定了基础。Abstract: The olfactory and visual neural systems in Drosophila are highly sensitive to the olfactory and visual stimuli in the natural environment. The highly sensitive single-modal perception and cross-modal collaboration decision-making mechanisms of the olfactory and visual neural systems provide certain inspiration for bionic applications. Firstly, based on the olfactory and visual neural systems in Drosophila, the current research status of the physiological mechanisms and computational models of single-modal perception decision-making of the olfactory and visual neurons is summarized. The summary is divided into three parts: capturing, processing, and decision-making of the olfactory and visual signals. Meanwhile, the physiological mechanisms and computational models of cross-modal collaboration decision-making of the olfactory and visual neurons in Drosophila are further expounded. Then, the typical bionic applications of single-modal perception and cross-modal collaboration in Drosophila are summarized. Finally, the current challenges of the physiological mechanisms and computational models of the olfactory and visual neural pathways in Drosophila are summarized and the future development trends are outlooked for, which lays a foundation for future research work.
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表 1 典型的果蝇嗅觉神经通路计算模型与简要描述
文献 示意图 简要描述 [22] 图2(a) 根据果蝇蘑菇体中凯尼恩细胞(KC)的稀疏性与蘑菇体输出神经元(MBON)的联想学习机制，Kennedy提出了果蝇嗅觉神经系统的动态计算模型验证嗅觉感受神经元(ORN)感知的自然气味表征学习分类，该模型适合于凯尼恩细胞(KC)对气味反应不相关，为了达到不相关性，在凯尼恩细胞层加入稳态阈值调谐机制，同时投射神经元(PN)存在局部神经元(LN)抑制机制和凯尼恩细胞(KC)存在前对侧神经元(APL)抑制作用。通过对110种自然气味进行测试，验证该计算模型的有效性与蘑菇体输出神经元(MBON)联想学习的泛化特性。区别在于凯尼恩细胞(KC)到蘑菇体输出神经元(MBON)之间通过数据库训练估计连接权重，利用固化的连接权重估计嗅觉信号的具体类型。 [23] 图2(b) 根据果蝇蘑菇体中凯尼恩细胞(KC)和蘑菇体输出神经元(MBON)之间的突触连接可塑性，Springer等人提出了基于果蝇蘑菇体的前馈电路模型验证食欲和厌恶条件反射以及记忆消退。蘑菇体输出神经元之间的侧抑制性与从蘑菇体输出神经元到多巴胺能神经元的兴奋反馈对于奖赏预测和消退机制至关重要。MVP2/M6神经元的激活介导接近行为，MV2/V2神经元的激活介导回避行为，对嗅觉信号的行为偏好(食欲或厌恶)是通过MVP2/M6和MV2/V2神经元之间的不平衡来计算。区别在于凯尼恩细胞(KC)与4个蘑菇体输出神经元(MBON)完全连接，并将四个蘑菇体输出神经元(MBON)平均分成两组，两两相互抑制输出，代表靠近和远离两种不同的行为。 [24] 图2(c) 根据果蝇中央脑能够有效地进行嗅觉信号类型的识别和气味浓度水平的估计，Mosqueiro等人提出基于Hebbian学习和输出抑制相互竞争机制的果蝇嗅觉信号感知模式识别通路模型，其中触角小叶神经层中嗅觉感受神经元(ORN)负责嗅觉信号采集，触角小叶神经层中投射神经元(PN)负责特征提取，蘑菇体中凯尼恩细胞(KC)负责模式识别。在决策过程中，果蝇嗅觉神经系统至少需要3条重要信息才能进行决策，即气味类型、气味浓度与气味源距离，其中气味类型用于了解果蝇是否对气味感兴趣，气味浓度用于了解有多少气味源可供捕获，气味源距离用于评估搜索来源的努力是否具有成本效益。区别在于凯尼恩细胞(KC)和蘑菇体输出神经元(MBON)之间使用Hebbian学习和蘑菇体输出神经元(MBON)之间相互竞争机制进行识别。 
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表 2 表征视网膜神经层生理机制的四种数学模型
文献 数学模型 [25] $ {{P}}\left( {x,y,t} \right) = L\left( {x,y,t} \right)\; - L\left( {x,y,t - 1} \right) $ [26] $ {{{P}}_{\mathrm{G}}}\left( {x,y,t} \right) = \iint {L\left( {{{u}},{{v}},t} \right){{{G}}_\sigma }\left( {x - {{u}},y - {{v}}} \right)}{\rm{d}}{{u}}{\rm{d}}{{v}} $ $ {G_\sigma }\left( {x,y} \right){\text{ = }}\dfrac{1}{{2{\pi}{\sigma ^2}}}\exp \left( { - \dfrac{{{x^2} + {y^2}}}{{2{\sigma ^2}}}} \right) $ [27] $ {\rm{Li}}\left( {x,y,t} \right){\text{ = }}\dfrac{{{{\left( {{P_{\mathrm{G}}}\left( {x,y,t} \right)} \right)}^n}}}{{{{\left( {{P_{\mathrm{G}}}\left( {x,y,t} \right)} \right)}^n} + {{\left( {{P_{\mathrm{C}}}\left( {x,y,t} \right)} \right)}^n}}} $ $ \dfrac{{{\rm{d}}{{{P}}_{\text{C}}}\left( {x,y,t} \right)}}{{{\rm{d}}{{t}}}} = \dfrac{1}{\tau }\left( {{{{P}}_{\text{G}}}\left( {x,y,t} \right) - {{{P}}_{\text{C}}}\left( {x,y,t} \right)} \right) $ [28] $ \begin{gathered} {{P}}\left( {x,y,t} \right) = L\left( {x,y,t} \right)\; - L\left( {x,y,t - 1} \right)\; \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\quad+\sum\limits_{i{\text{ = }}1}^{{n_p}} {\left( {{\lambda _{{i}}} \cdot P\left( {x,y,t - {{i}}} \right)} \right)} \\ \end{gathered} $ $ {\lambda _{{i}}}\;{\text{ = }}\;{\left( {1 + {{\text{e}}^{{i}}}} \right)^{ - 1}} $
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表 3 果蝇视觉神经决策通路计算模型相关研究机构与简要描述
研究机构 示意图 简要描述 英国林肯大学计算机
科学学院图4(a)–图4(d) 岳士岗团队专注于果蝇视觉、视觉行为偏好等的研究，提出了基于LPTC,LGMD1,LGMD2和STMD神经元的计算模型，区别在于基于LPTC和STMD神经元的计算模型直接将输入视觉信号分为ON和OFF两路视觉通路，然后传递到相应的运动神经元进行信号处理，而基于LGMD1和LGMD2计算模型首先将输入视觉信号分为兴奋性(Excitability)和抑制性(Inhibition)两路视觉通路，然后基于E和I视觉通路再分成ON和OFF两路视觉通路，LGMD1和LGMD2计算模型的区别在于对ON视觉通路的选择性，相关计算模型主要应用在无人机与机器人中。 英国纽卡斯尔大学行为
与进化中心图4(e) Rind团队专注于果蝇中央脑、认知行为等的研究，提出了基于LGMD神经元的计算模型，区别在于兴奋性(Excitability)和抑制性(Inhibition)两路视觉通路未再次分成ON和OFF两路视觉通路，而是直接传递到LGMD神经元进行信号处理，相关计算模型主要应用在无人和辅助驾驶中。 中国科学院自动化研究所与生物物理研究所 图4(f) 中国科学院自动化研究所的曾毅团队与生物物理研究所郭爱克团队专注于类脑认知计算模型等的研究，提出使用脉冲神经网络构建果蝇的中央脑神经层计算模型，区别在于将感知线性决策与价值非线性决策两部分集成到一起，高度表征了中央脑神经层的神经生理机制，相关计算模型主要应用在无人机中。
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