Neural decoding on imbalanced calcium imaging data with a network of support vector machines

References

• Chen R, Lin D. Decoding brain states based on microcircuits. 2018 IEEE international conference on cyborg and bionic systems (cbs); 2018 Oct. p. 397–400.
   Google Scholar
• Bhattacharyya SS, Deprettere E, Leupers R, et al. Handbook of signal processing systems. 3rd ed. Springer; 2019.
   Google Scholar
• Lee Y, Madayambath SC, Liu Y, et al. Online learning in neural decoding using incremental linear discriminant analysis. 2017 IEEE international conference on cyborg and bionic systems (cbs); 2017 Oct. p. 173–177.
   Google Scholar
• Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen. 1936;7(2):179–188.
   Google Scholar
• Shaoning Pang, Ozawa S, Kasabov N. Incremental linear discriminant analysis for classification of data streams. IEEE Trans Syst Man Cybern Part B (Cybern). 2005 Oct;35(5):905–914.
   PubMed Web of Science ®Google Scholar
• Elizondo D. The linear separability problem: some testing methods. IEEE Trans Neural Netw. 2006 March;17(2):330–344.
   PubMed Web of Science ®Google Scholar
• Kotsiantis S, Kanellopoulos D, Pintelas P, et al. Handling imbalanced datasets: A review. GESTS Int Trans Computer Sci Eng. 2006;30(1):25–36.
   Google Scholar
• Barbera G, Liang B, Zhang L, et al. Spatially compact neural clusters in the dorsal striatum encode locomotion relevant information. Neuron. 2016;92(1):202–213.
   PubMed Web of Science ®Google Scholar
• Mani I, Zhang I. kNN approach to unbalanced data distributions: a case study involving information extraction. Proceedings of workshop on learning from imbalanced datasets; Vol. 126, 2003.
   Google Scholar
• Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey. ACM Comput Surv (CSUR). 2009 July;41(3):15:1–15:58.
   Web of Science ®Google Scholar
• Chen R, Herskovits E. Bayesian predictive modeling based on multidimensional connectivity profiling. Neuroradiol J. 2015;28(1):4–11.
   Web of Science ®Google Scholar
• Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–297.
   Web of Science ®Google Scholar
• Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998;2(2):121–167.
   Web of Science ®Google Scholar
• Wu H, Shen C, Sane N, et al. A model-based schedule representation for heterogeneous mapping of dataflow graphs. Proceedings of the international heterogeneity in computing workshop; Anchorage, Alaska: 2011 May. p. 66–77.
   Google Scholar
• Liu Y, Loh HT, Sun A. Imbalanced text classification: A term weighting approach. Expert Syst Appl. 2009;36(1):690–701.
   Web of Science ®Google Scholar
• Huang C, Li Y, Change Loy C, et al. Learning deep representation for imbalanced classification. The ieee conference on computer vision and pattern recognition (cvpr); 2016 June.
   Google Scholar
• Piri S, Delen D, Liu T. A synthetic informative minority over-sampling (SIMO) algorithm leveraging support vector machine to enhance learning from imbalanced datasets. Decis Support Syst. 2018;106:15–29.
   Web of Science ®Google Scholar
• Lee K, Lee Y, Lin DT, et al. Real-time calcium imaging based neural decoding with a support vector machine. Proceedings of the IEEE biomedical circuits and systems conference; 2019 October.
   Google Scholar
• Schölkopf B, Platt JC, Shawe-Taylor J, et al. Estimating the support of a high-dimensional distribution. Neural Comput. 2001;13(7):1443–1471.
   PubMed Web of Science ®Google Scholar
• Lee EA, Parks TM. Dataflow process networks. Proc IEEE. 1995;83(5):773–801.
   Web of Science ®Google Scholar
• Wasikowski M, Chen X. Combating the small sample class imbalance problem using feature selection. IEEE Trans Knowl Data Eng. 2010 Oct;22(10):1388–1400.
   Web of Science ®Google Scholar
• Sasaki Y. The truth of the F-measure. University of Manchester; 2007 October. Tech Rep.
   Google Scholar
• Manevitz LM, Yousef M. One-class SVMs for document classification. J Mach Learn Res. 2001;2(Dec):139–154.
   Google Scholar
• Raskutti B, Kowalczyk A. Extreme re-balancing for SVMs: A case study. ACM SIGKDD Explorations Newsletter. 2004 June;6(1):60–69.
   Google Scholar
• Lin S, Liu Y, Lee K, et al. The DSPCAD framework for modeling and synthesis of signal processing systems. In: Ha S, Teich J, editors. Handbook of hardware/software codesign. Springer; 2017. p. 1–35.
   Google Scholar
• Lee EA, Messerschmitt DG. Synchronous dataflow. Proc IEEE. 1987 September;75(9):1235–1245.
   Web of Science ®Google Scholar
• Plishker W, Sane N, Kiemb MFunctional DIF for rapid prototyping. Proceedings of the international symposium on rapid system prototyping; Monterey (CA); 2008 June. p. 17–23.
   Google Scholar