Abeles, M., & Goldstein, M. H. (1977). Multispike train analysis. Proceedings of the IEEE, 65(5), 762–773.
Adamos, D. A., Kosmidis, E. K., & Theophilidis, G. (2008). Performance evaluation of PCA-based spike sorting algorithms. Computer Methods and Programs in Biomedicine, 91(3), 232–244.
Amid E, Warmuth MK. TriMap: Large-scale Dimensionality Reduction Using Triplets [Internet]. arXiv; 2022 [cited 2025 May 2]. Available from: http://arxiv.org/abs/1910.00204
Ardelean, E. R., Coporîie, A., Ichim, A. M., Dînșoreanu, M., & Mureșan, R. C. (2023). A study of autoencoders as a feature extraction technique for spike sorting. PLoS ONE, 18(3), e0282810.
Article CAS PubMed PubMed Central Google Scholar
Ardelean ER, Ichim AM, Dînşoreanu M, Mureşan RC. Improved space breakdown method – A robust clustering technique for spike sorting. Front Comput Neurosci [Internet]. 2023 [cited 2023 Feb 20];17. Available from: https://doi.org/10.3389/fncom.2023.1019637
Ardelean, A. I., Ardelean, E. R., Moca, V. V., Mureşan, R. C., & Dînşoreanu, M. Burst detection in neuronal activity. In: 2023 IEEE 19th International Conference on Intelligent Computer Communication and Processing (ICCP) [Internet]. 2023 [cited 2024 Jan 29]. p. 349–56. Available from: https://ieeexplore.ieee.org/document/10398703
Ardelean, E. R., Grosu, G. F., Terebeş, R., & Dînşoreanu, M. Exploiting the Self-Organizing Map for Spike Sorting. In: 2023 IEEE 19th International Conference on Intelligent Computer Communication and Processing (ICCP) [Internet]. 2023 [cited 2024 Jan 29]. p. 363–9. Available from: https://ieeexplore.ieee.org/document/10398692
Ardelean ER, Portase RL, Potolea R, Dînșoreanu M. A path-based distance computation for non-convexity with applications in clustering. Knowl Inf Syst [Internet]. 2024 Nov 1 [cited 2024 Nov 29]; Available from. https://doi.org/10.1007/s10115-024-02275-4
Bakkum D, Radivojevic M, Frey U, Franke F, Hierlemann A, Takahashi H. Parameters for burst detection. Front Comput Neurosci [Internet]. 2014 [cited 2022 Oct 27];7. Available from: https://www.frontiersin.org/articles/10.3389/fncom.2013.00193
Baldi, P., Autoencoders, Unsupervised Learning, and Deep Architectures. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning [Internet]. JMLR Workshop and Conference Proceedings; 2012 [cited 2021 Dec 22]. p. 37–49. Available from: https://proceedings.mlr.press/v27/baldi12a.html
Bear, M. F., Connors, B. W., & Paradiso, M. A. (2015). Neuroscience: Exploring the brain (4th ed.). Wolters Kluwer Health.
Belkin, M., & Niyogi, P. (2003). Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6), 1373–1396.
Berry, T., & Harlim, J. (2016). Variable bandwidth diffusion kernels. Appl Computat Harmon Analys, 40(1), 68–96.
Bhatia, K. K., Rao, A., Price, A. N., Wolz, R., Hajnal, J., & Rueckert, D. (2012). Hierarchical manifold learning. International Conference on Medical Image Computing and Computer-Assisted Intervention, 15(Pt 1), 512–519.
Borg I, Groenen PJF, editors. Constructing MDS Representations. In: Modern Multidimensional Scaling: Theory and Applications [Internet]. New York, NY: Springer; 2005 [cited 2025 May 2]. p. 19–35. Available from: https://doi.org/10.1007/0-387-28981-X_2
Buccino, A. P., Garcia, S., & Yger, P. (2022). Spike sorting: New trends and challenges of the era of high-density probes. Progress in Biomedical Engineering, 4(2), Article 022005.
Busch, E. L., Huang, J., Benz, A., Wallenstein, T., Lajoie, G., Wolf, G., et al. (2023). Multi-view manifold learning of human brain-state trajectories. Nature Computational Science, 3(3), 240–253.
Article PubMed PubMed Central Google Scholar
Buzsáki G. Rhythms of the Brain [Internet]. New York: Oxford University Press; 2006 [cited 2021 Dec 8]. 464 p. Available from: https://oxford.universitypressscholarship.com/10.1093/acprof:oso/9780195301069.001.0001/acprof-9780195301069
Caliński, T., & Ja, H. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3, 1–27. https://doi.org/10.1080/03610927408827101
Caro-Martín, C. R., Delgado-García, J. M., Gruart, A., & Sánchez-Campusano, R. (2018). Spike sorting based on shape, phase, and distribution features, and K-TOPS clustering with validity and error indices. Science and Reports, 8(1), 17796.
Carter M, Shieh J. Chapter 4 - Electrophysiology. In: Carter M, Shieh J, editors. Guide to Research Techniques in Neuroscience (Second Edition) [Internet]. San Diego: Academic Press; 2015 [cited 2022 Aug 2]. p. 89–115. Available from: https://www.sciencedirect.com/science/article/pii/B9780128005118000046
Chung, J. E., Magland, J. F., Barnett, A. H., Tolosa, V. M., Tooker, A. C., Lee, K. Y., et al. (2017). A fully automated approach to spike sorting. Neuron, 95(6), 1381-1394.e6.
Article CAS PubMed PubMed Central Google Scholar
Davies, D. L., & Bouldin, D. W. (2009). A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2, 224–227.
Dimensionality reduction: a comparative review | BibSonomy [Internet]. [cited 2022 Aug 11]. Available from: https://www.bibsonomy.org/bibtex/2ed03568f0e9bca9cdaf6b25304e55940/peter.ralph
Dipalo, M., Amin, H., Lovato, L., Moia, F., Caprettini, V., Messina, G., et al. (2017). Intracellular and extracellular recording of spontaneous action potentials in mammalian neurons and cardiac cells with 3D plasmonic nanoelectrodes. Nano Letters. https://doi.org/10.1021/acs.nanolett.7b01523
Article PubMed PubMed Central Google Scholar
Donoho, D. L., & Grimes, C. (2003). Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Sciences, 100(10), 5591–5596.
Dwork C, Kumar R, Naor M, Sivakumar D. Rank aggregation methods for the Web. In: Proceedings of the 10th international conference on World Wide Web [Internet]. New York, NY, USA: Association for Computing Machinery; 2001 [cited 2022 Dec 6]. p. 613–22. (WWW ’01). Available from: https://doi.org/10.1145/371920.372165
Ebbesen, C. L., Reifenstein, E. T., Tang, Q., Burgalossi, A., Ray, S., Schreiber, S., et al. (2016). Cell type-specific differences in spike timing and spike shape in the rat parasubiculum and superficial medial entorhinal cortex. Cell Reports, 16(4), 1005–1015.
Article CAS PubMed Google Scholar
Eom, J., Park, I. Y., Kim, S., Jang, H., Park, S., Huh, Y., et al. (2021). Deep-learned spike representations and sorting via an ensemble of auto-encoders. Neural Networks, 1(134), 131–142.
Estivill-Castro, V. (2002). Why so many clustering algorithms: A position paper. SIGKDD Explor Newsl., 4(1), 65–75.
Fowlkes, E. B., & Mallows, C. L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78(383), 553–569.
Georgiadis V, Petrantonakis PC. SpikeSift: A Computationally Efficient and Drift-Resilient Spike Sorting Algorithm [Internet]. arXiv; 2025 [cited 2025 Aug 6]. Available from: http://arxiv.org/abs/2504.01604
Glaser EM, Marks WB. ON-LINE SEPARATION OF INTERLEAVED NEURONAL PULSE SEQUENCES. In: Enslein K, editor. Data Acquisition and Processing in Biology and Medicine [Internet]. Pergamon; 1968 [cited 2022 Aug 11]. p. 137–56. Available from: https://www.sciencedirect.com/science/article/pii/B9780080035437500124
Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information System, 17(2), 107–145.
Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193–218.
Hulata, E., Segev, R., & Ben-Jacob, E. (2002). A method for spike sorting and detection based on wavelet packets and Shannon’s mutual information. Journal of Neuroscience Methods, 117(1), 1–12.
Hyvärinen, A. (2013). Independent component analysis: Recent advances. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1984), 20110534.
Jia, X., Siegle, J. H., Bennett, C., Gale, S. D., Denman, D. J., Koch, C., et al. (2019). High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification. Journal of Neurophysiology, 121(5), 1831–1847.
Joshi, M. V., Kumar, V., & Agarwal, R. C. (2001). Evaluating boosting algorithms to classify rare classes: comparison and improvements. In Proceedings 2001 IEEE international conference on data mining. Presented at the proceedings 2001 IEEE international conference on data mining (pp. 257–264). https://doi.org/10.1109/ICDM.2001.989527
Jun, J. J., Steinmetz, N. A., Siegle, J. H., Denman, D. J., Bauza, M., Barbarits, B., et al. (2017). Fully integrated silicon probes for high-density recording of neural activity. Nature, 551(7679), 232–236.
Article CAS PubMed PubMed Central Google Scholar
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.
Lazarenko, D., & Bonald, T. (2021). Pairwise adjusted mutual information. https://doi.org/10.48550/arXiv.2103.12641
Lefebvre, B., Yger, P., & Marre, O. (2016). Recent progress in multi-electrode spike sorting methods. Journal of Physiology-Paris, 110(4), 327–335.
Lewicki, M. S. (1998). A review of methods for spike sorting: The detection and classification of neural action potentials. Netw Bristol Engl., 9(4), R53-78.
Litke, A. M., Bezayiff, N., Chichilnisky, E. J., Cunningham, W., Dabrowski, W., Grillo, A. A., et al. (2004). What does the eye tell the brain?: Development of a system for the large-scale recording of retinal output activity. IEEE Transactions on Nuclear Science, 51(4), 1434–1440.
Lopes, M. V., Aguiar, E., Ewaldo, S., Eder, S., & Barros, A. K. (2013). ICA feature extraction for spike sorting of single-channel records. In 2013 ISSNIP biosignals and biorobotics conference: Biosignals and robotics for better and safer living (BRC). Presented at the 2013 ISSNIP biosignals and biorobotics conference: Biosignals and robotics for better and safer living (BRC) (pp. 1–5). https://doi.org/10.1109/BRC.2013.6487468
Lopez Pinaya, W. H., Vieira, S., Garcia-Dias, R., & Mechelli, A. (2020). Chapter 11 - Autoencoders. In A. Mechelli & S. Vieira (Eds.), Machine learning (pp. 193–208). Academic Press. https://doi.org/10.1016/B978-0-12-815739-8.00011-0
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1: Statistics (pp. 281–298). University of California Press.
Magland, J., Jun, J. J., Lovero, E., Morley, A. J., Hurwitz, C. L., Buccino, A. P., Garcia, S., & Barnett, A. H. (2020). SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters. eLife, 9, e55167. https://doi.org/10.7554/eLife.55167
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval (Illustrated, p. 506). Cambridge University Press.
Marcílio-Jr, W. E., Eler, D. M., Paulovich, F. V., & Martins, R. M. (2025). HUMAP: Hierarchical uniform manifold approximation and projection. IEEE Transactions on Visualization and Computer Graphics, 31(9), 5741–5753.
Marques-Smith A, Neto JP, Lopes G, Nogueira J, Calcaterra L, Frazão J, et al. Simultaneous patch-clamp and dense CMOS probe extracellular recordings from the same cortical neuron in anaesthetized rats. [Internet]. CRCNS; 2018 [cited 2025 May 19]. p. 370080. Available from: CRCNS.org
Marques-Smith A, Neto JP, Lopes G, Nogueira J, Calcaterra L, Frazão J, et al. Recording from the same neuron with high-density CMOS probes and patch-clamp: a ground-truth dataset and an experiment in collaboration [Internet]. bioRxiv; 2020 [cited 2025 May 19]. p. 370080. Available from: https://www.biorxiv.org/content/10.1101/370080v2
McInnes L, Healy J, Melville J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction [Internet]. arXiv; 2020 [cited 2025 May 2]. Available from: http://arxiv.org/abs/1802.03426
Meilă M, Zhang H. Manifold learning: what, how, and why [Internet]. arXiv; 2023 [cited 2025 May 4]. Available from: http://arxiv.org/abs/2311.03757
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