Delahunt Charles B, Riffell Jeffrey A, Kutz J Nathan
Department of Electrical Engineering, University of Washington, Seattle, WA, United States.
Computational Neuroscience Center, University of Washington, Seattle, WA, United States.
Front Comput Neurosci. 2018 Dec 19;12:102. doi: 10.3389/fncom.2018.00102. eCollection 2018.
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 synaptic weight updates in 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 firing-rate model of the 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 neural firing rates in our simulations match the statistical features of 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.
昆虫嗅觉系统，包括触角叶（AL）、蘑菇体（MB）和辅助结构，是一个相对简单的能够学习的神经系统。其结构特征在生物神经系统中广泛存在，通过一系列网络处理嗅觉刺激，其中从一个阶段到下一个阶段会发生维度的大幅变化，并且稀疏性和随机性在编码中起着关键作用。学习部分是由章鱼胺对触角叶的神经调节奖励机制实现的，其活性增加通过赫布可塑性诱导蘑菇体中的突触权重更新。蘑菇体中的强制稀疏性将赫布生长集中在对学习到的气味表示最重要的神经元上。基于当前的生物物理知识，我们构建了一个蛾类嗅觉系统的端到端计算发放率模型，该模型包括章鱼胺刺激下触角叶和蘑菇体的相互作用。我们的模型能够稳健地学习新气味，并且我们模拟中的神经发放率与发放率数据的统计特征相匹配。从生物学角度来看，该模型为研究神经调节剂（如章鱼胺）在学习中的作用提供了一个有价值的工具，并深入了解了学习过程中稀疏性、赫布生长和刺激之间的关键相互作用。我们的模拟还为目前特征尚不明确的嗅觉系统结构细节提供了预测。从机器学习角度来看，该模型产生了受生物启发的机制，这些机制可能有助于构建能够从极少样本中快速学习的神经网络。这些机制包括高噪声层、作为噪声滤波器的稀疏层，以及一种基于章鱼胺刺激、稀疏层和赫布生长来训练网络的生物学上合理的优化方法。
