学习的生物机制：烟草天蛾嗅觉学习的计算模型及其在神经网络中的应用
Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets
摘要：
昆虫的嗅觉系统包括触角叶（AL）、蘑菇体（MB）和辅助结构，是一个相对简单的具有学习能力的神经系统。它的结构特征在生物神经系统中广泛存在，通过级联网络处理嗅觉刺激，在级联网络中，从一个阶段到另一个阶段发生大的维度变化，稀疏性和随机性在编码中起着关键作用。学习部分是通过章鱼胺刺激AL的神经调节性奖励机制实现的，其增加的活性通过Hebbian可塑性诱导MB的重新布线。MB中的强制稀疏性将Hebbian生长集中在对学习气味的表示最重要的神经元上。基于目前的生物物理知识，我们已经构建了一个端到端的计算模型的天蛾嗅觉系统，其中包括在章鱼胺刺激下的AL和MB的相互作用。我们的模型能够鲁棒地学习新的气味，我们对整合和激发神经元的模拟与体内放电率数据的统计特征相匹配。从生物学的角度来看，该模型提供了一个有价值的工具，用于检查神经调节剂，如章鱼胺，在学习中的作用，并提供了深入了解稀疏，赫布增长和刺激之间的关键相互作用在学习。我们的模拟还告知嗅觉系统的结构细节的预测，目前还没有得到很好的表征。从机器学习的角度来看，该模型产生了生物启发的机制，这些机制在构建神经网络以从非常少的样本中快速学习时可能有用。这些机制包括高噪声层、作为噪声过滤器的稀疏层以及基于章鱼胺刺激、稀疏层和Hebbian增长来训练网络的生物学上合理的优化方法。
英文摘要：
The insect olfactory system, which includes the antennal lobe (AL), mushroom body (MB), and ancillary structures, is a relatively simple neural system capable of learning. Its structural features, which are widespread in biological neural systems, process olfactory stimuli through a cascade of networks where large dimension shifts occur from stage to stage and where sparsity and randomness play a critical role in coding. Learning is partly enabled by a neuromodulatory reward mechanism of octopamine stimulation of the AL, whose increased activity induces rewiring of the MB through Hebbian plasticity. Enforced sparsity in the MB focuses Hebbian growth on neurons that are the most important for the representation of the learned odor. Based upon current biophysical knowledge, we have constructed an end-to-end computational model of the Manduca sexta moth olfactory system which includes the interaction of the AL and MB under octopamine stimulation. Our model is able to robustly learn new odors, and our simulations of integrate-and-fire neurons match the statistical features of in-vivo firing rate data. From a biological perspective, the model provides a valuable tool for examining the role of neuromodulators, like octopamine, in learning, and gives insight into critical interactions between sparsity, Hebbian growth, and stimulation during learning. Our simulations also inform predictions about structural details of the olfactory system that are not currently well-characterized. From a machine learning perspective, the model yields bio-inspired mechanisms that are potentially useful in constructing neural nets for rapid learning from very few samples. These mechanisms include high-noise layers, sparse layers as noise filters, and a biologically-plausible optimization method to train the network based on octopamine stimulation, sparse layers, and Hebbian growth.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
