訊號傳遞的方向對神經網路是重要的。因此，在分析神經網路時，方向性是需納入的資訊之一，而訊號的傳遞方向可藉由判斷神經元的極性獲得。目前的辨識方式是以生化實驗為主，但若要處理大尺度的神經網路，實驗可能過度耗時。

為了解決這個問題，我們提出一個從型態骨架辨認神經元極性的演算法 (ASNeuPI)。在ASNeuPI中，我們首先將單一神經元從型態上拆成幾個子結構，並計算每個子結構的形態特徵。接著利用機器學習(Machine Learning)的方法找出提供最高正確率的特徵組合作為極性分類使用。

我們利用有連結果蠅protocerebral bridge(PCB)或medulla(MED)腦區的神經細胞來測試這套方法，資料來源為國立清華大學腦科學研究中心。平均起來，一個神經細胞中有85%以上的端點(terminal point)其極性能被正確的辨認。在所有測試的形態特徵中，距細胞本體(soma)的遠近對判斷神經極性最為有用。我們的結果顯示ASNeuPI具可行性，並有發展成一套利用型態骨架辨認神經元極性的半自動化流程之潛力。

The direction of signal transmission is crucial for neural networks. Therefore, the direction of signal flow, which could be provided by identifying neuronal polarity, should be included when we analyze neural networks. However, neuronal polarity is usually identified by biochemical method which is time consuming and might not be an appropriate way to deal with large-scale neural networks.

To solve this problem, we proposed the algorithm for skeleton-based neuronal polarity identification (ASNeuPI). In ASNeuPI, we first morphologically divide a neuron into several substructures, and then extract their morphological features. By applying methods in machine learning, we got an optimal axis providing highest accuracy to serve as the discriminant feature for polarity classification.

We tested this method on neurons innervating protocerebral bridge (PCB) or medulla (MED) in Drosophila. The data were obtained from Brain Research Center, National Tsing Hua University. On average, the polarity of above 85% terminal points in a neuron could be correctly identified. Among all the morphological features tested, the distance to soma is the most useful one. Our results show that ASNeuPI is workable and has the potential to provide a computer-based semi-automatic procedure to predict neuronal polarity from skeleton data.

Alpaydin, E., 2009. Introduction to Machine Learning, second ed. The MIT Press.
Chiang, A.-S., Lin, C.-Y., Chuang, C.-C., Chang, H.-M., Hsieh, C.-H., Yeh, C.-W., Shih, C.-T., Wu, J.-J., Wang, G.-T., Chen, Y.-C., Wu, C.-C., Chen, G.-Y., Ching, Y.-T., Lee, P.-C., Lin, C.-Y., Lin, H.-H., Wu, C.-C., Hsu, H.-W., Huang, Y.-A., Chen, J.-Y., Chiang, H.-J., Lu, C.-F., Ni, R.-F., Yeh, C.-Y., Hwang, J.-K., 2011. Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution. Current Biology 21, 1–11.
Cuntz, H., Forstner, F., Borst, A., Häusser, M., 2010. One Rule to Grow Them All: A General Theory of Neuronal Branching and Its Practical Application. PLoS Comput Biol 6, e1000877.
Cuntz, H., Forstner, F., Haag, J., Borst, A., 2008. The Morphological Identity of Insect Dendrites. PLoS Comput Biol 4, e1000251.
Hanesch, U., Fischbach, K.-F., Heisenberg, M., 1989. Neuronal architecture of the central complex in <i>Drosophila melanogaster</i> Cell and Tissue Research 257, 343–366.
Jang, J.-S.R., 2012. Machine Learning Toolbox (MLT) [WWW Document]. Machine Learning Toolbox. URL http://mirlab.org/jang/matlab/toolbox/machineLearning
Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E., 1998. Convergence Properties of the Nelder–Mead Simplex Method in Low Dimensions. SIAM J. on Optimization 9, 112–147.
Luczak, A., 2010. Measuring neuronal branching patterns using model-based approach. Front. Comput. Neurosci 4, 10.
Mishkin, M., Ungerleider, L.G., 1982. Contribution of striate inputs to the visuospatial functions of parieto-preoccipital cortex in monkeys. Behavioural Brain Research 6, 57–77.
Rolls, M.M., 2011. Neuronal polarity in Drosophila: Sorting out axons and dendrites. Developmental Neurobiology 71, 419–429.
Scorcioni, R., Polavaram, S., Ascoli, G.A., 2008. L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nature Protocols 3, 866–876.
Squire, L.R., Berg, D., Bloom, F., Lac, S. du, Ghosh, A. (Eds.), 2008. Fundamental Neuroscience, Third Edition, 3rd ed. Academic Press.
Wang, T., Liao, D., 2011. Neuronal morphology classification based on SVM, in: Computer Science and Service System (CSSS), 2011 International Conference On. pp. 3344 –3347.
Wicklein, M., Strausfeld, N.J., 2000. Organization and significance of neurons that detect change of visual depth in the hawk moth Manduca sexta. The Journal of Comparative Neurology 424, 356–376.
Witten, I.H., Frank, E., Hall, M.A., 2011. Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, 3rd ed. Morgan Kaufmann.