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首页> 外文期刊>PLoS Computational Biology >Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities
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Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities

机译:没有蘑菇体的嗅觉学习:蜜蜂外侧触角叶道的尖刺神经网络模型揭示了其在各种复杂性的气味记忆任务中的能力

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摘要

The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn. We show that inhibitory spike-timing dependent plasticity (modelling non-associative plasticity by exposure to different stimuli) in the synapses from local neurons to projection neurons decorrelates the projection neurons’ outputs. The strength of the decorrelations is regulated by global inhibitory feedback within antennal lobes to the projection neurons. By additionally modelling octopaminergic modification of synaptic plasticity among local neurons in the antennal lobes and projection neurons to LHN connections, the model can discriminate and generalize olfactory stimuli. Although positive patterning can be accounted for by the l-ALT model, negative patterning requires further processing and mushroom body circuits. Thus, our model explains several–but not all–types of associative olfactory learning and generalization by a few neural layers of odour processing in the l-ALT. As an outcome of the combination between non-associative and associative learning, the modelling approach allows us to link changes in structural organization of honeybees' antennal lobes with their behavioural performances over the course of their life.
机译:蜜蜂嗅觉系统是用于了解学习和记忆功能机制的公认模型。嗅觉刺激首先在触角叶中进行处理,然后通过称为内侧触角和外侧触角(m-ALT和l-ALT)的双重途径转移到蘑菇体和外侧角。最近的研究报道,蜜蜂可以通过将气味与奖励信号相关联来进行元素学习,即使在m-ALT发生损伤或阻塞蘑菇体后也是如此。为了测试侧向通路(l-ALT)足以进行元素学习的假设,我们在触角的肾小球肾小球内局部计算了模型,其中投射神经元的轴突连接到侧角的决策神经元(LHN)。我们显示,从局部神经元到投射神经元的突触中,抑制性的与穗期定时相关的可塑性(通过暴露于不同的刺激来模拟非关联可塑性)可与投射神经元的输出相关。去相关的强度由触角到投影神经元的全局抑制反馈调节。通过另外建模触角叶中局部神经元和投射神经元与LHN连接之间的突触可塑性的章鱼胺能修饰,该模型可以区分和概括嗅觉刺激。尽管l-ALT模型可以说明正图案,但负图案需要进一步处理和蘑菇体电路。因此,我们的模型通过l-ALT中气味处理的一些神经层来解释几种(但不是全部)联想式嗅觉学习和泛化。作为非关联学习与关联学习相结合的结果,建模方法使我们能够将蜜蜂触角叶的结构组织变化与它们在一生中的行为表现联系起来。

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