Department of Engineering Science, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
Neural Comput. 2013 Jan;25(1):259-87. doi: 10.1162/NECO_a_00386. Epub 2012 Sep 28.
In this letter, we use the firing rates from an array of olfactory sensory neurons (OSNs) of the fruit fly, Drosophila melanogaster, to train an artificial neural network (ANN) to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron (MLP) was also used, providing a significant increase in prediction performance. The effect of the number of hidden layers and hidden neurons of the MLP was investigated and found to be effective in enhancing network performance with both a single and a double hidden layer investigated separately. A hybrid array of MLPs was investigated and compared against the single MLP architecture. The hybrid MLPs were found to classify all vectors of the validation set, presenting the highest degree of prediction accuracy. Adjustment of the number of hidden neurons was investigated, providing further performance gain. In addition, noise injection was investigated, proving successful for certain network designs. It was found that the best-performing MLP was that of the double-hidden-layer hybrid MLP network without the use of noise injection. Furthermore, the level of performance was examined when different numbers of OSNs used were varied from the maximum of 24 to only 5 OSNs. Finally, the ideal OSNs were identified that optimized network performance. The results obtained from this study provide strong evidence of the usefulness of ANNs in the field of olfaction for the future realization of a signal processing back end for an artificial olfactory biosensor.
在这封信中，我们使用果蝇的一组嗅觉感觉神经元（OSN）的发射率来训练人工神经网络（ANN），以区分不同化学类别的挥发性气味。对优化后的网络进行自举，为网络的预测值提供了准确的估计。最初，我们使用简单的线性预测器来评估数据的复杂性，发现它提供了低预测性能。我们还使用了一种非线性的 ANN，形式为单个多层感知器（MLP），这显著提高了预测性能。我们还研究了 MLP 中隐藏层和隐藏神经元数量的影响，发现无论是单个还是双隐藏层，这都可以有效地增强网络性能。我们研究了混合 MLP 阵列，并将其与单个 MLP 架构进行了比较。混合 MLP 被发现可以对验证集的所有向量进行分类，从而提供了最高的预测精度。我们还研究了隐藏神经元数量的调整，这进一步提高了性能。此外，我们还研究了噪声注入，结果证明对某些网络设计是成功的。我们发现，表现最好的 MLP 是双隐藏层混合 MLP 网络，而无需使用噪声注入。此外，我们还研究了当使用的 OSN 数量从最多 24 个变化到只有 5 个 OSN 时，性能水平如何变化。最后，我们确定了优化网络性能的理想 OSN。这项研究的结果为 ANNs 在嗅觉领域的实用性提供了有力的证据，这有助于未来实现人工嗅觉生物传感器的信号处理后端。
