Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex

Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN (Version 3). ArXiv. https://doi.org/10.48550/ARXIV.1701.07875

Article  Google Scholar 

Ascoli, G. A., & Krichmar, J. L. (2000). L-neuron: A modeling tool for the efficient generation and parsimonious description of dendritic morphology. Neurocomputing, 32–33, 1003–1011. https://doi.org/10.1016/S0925-2312(00)00272-1

Article  Google Scholar 

Berlin, S., & Isacoff, E. Y. (2017). Synapses in the spotlight with synthetic optogenetics. EMBO Reports, 18(5), 677–692. https://doi.org/10.15252/embr.201744010

Article  CAS  PubMed  PubMed Central  Google Scholar 

Borji, A. (2021). Pros and cons of GAN evaluation measures: New developments (Version 3). ArXiv. https://doi.org/10.48550/ARXIV.2103.09396

Article  Google Scholar 

Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., & Erhan, D. (2016). Domain separation networks (Version 1). ArXiv. https://doi.org/10.48550/ARXIV.1608.06019

Article  Google Scholar 

Branco, P., Torgo, L., & Ribeiro, R. P. (2017). A survey of predictive modeling on imbalanced domains. ACM Computing Surveys, 49(2), 1–50. https://doi.org/10.1145/2907070

Article  Google Scholar 

Bulut, C., & Arslan, E. (2024). Comparison of the impact of dimensionality reduction and data splitting on classification performance in credit risk assessment. Artificial Intelligence Review. https://doi.org/10.1007/s10462-024-10904-1

Article  Google Scholar 

Capogrosso, M., Milekovic, T., Borton, D., Wagner, F., Moraud, E. M., Mignardot, J.-B., Buse, N., Gandar, J., Barraud, Q., Xing, D., Rey, E., Duis, S., Jianzhong, Y., Ko, W. K. D., Li, Q., Detemple, P., Denison, T., Micera, S., Bezard, E., & Courtine, G. (2016). A brain–spine interface alleviating gait deficits after spinal cord injury in primates. Nature, 539(7628), Article 7628. https://doi.org/10.1038/nature20118

Article  Google Scholar 

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority Over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953

Article  Google Scholar 

Courtine, G., Gerasimenko, Y., Van Den Brand, R., Yew, A., Musienko, P., Zhong, H., Song, B., Ao, Y., Ichiyama, R. M., Lavrov, I., Roy, R. R., Sofroniew, M. V., & Edgerton, V. R. (2009). Transformation of nonfunctional spinal circuits into functional states after the loss of brain input. Nature Neuroscience, 12(10), Article 10. https://doi.org/10.1038/nn.2401

Article  CAS  Google Scholar 

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 Computational Biology, 6(8), e1000877. https://doi.org/10.1371/journal.pcbi.1000877

Article  CAS  PubMed  PubMed Central  Google Scholar 

Esteban, C., Hyland, S. L., & Rätsch, G. (2017). Real-valued (Medical) time series generation with recurrent conditional GANs. arXiv. https://doi.org/10.48550/arXiv.1706.02633

Article  Google Scholar 

Fuzik, J., Zeisel, A., Máté, Z., Calvigioni, D., Yanagawa, Y., Szabó, G., Linnarsson, S., & Harkany, T. (2016). Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nature Biotechnology, 34(2), 175–183. https://doi.org/10.1038/nbt.3443

Article  CAS  PubMed  Google Scholar 

Gehlenborg, N., O’Donoghue, S. I., Baliga, N. S., Goesmann, A., Hibbs, M. A., Kitano, H., Kohlbacher, O., Neuweger, H., Schneider, R., Tenenbaum, D., & Gavin, A.-C. (2010). Visualization of omics data for systems biology. Nature Methods, 7(S3), S56–S68. https://doi.org/10.1038/nmeth.1436

Article  CAS  PubMed  Google Scholar 

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Networks (No. arXiv: 1406.2661). arXiv. https://doi.org/10.48550/arXiv.1406.2661

Article  Google Scholar 

Gouwens, N. W., Sorensen, S. A., Berg, J., Lee, C., Jarsky, T., Ting, J., Sunkin, S. M., Feng, D., Anastassiou, C. A., Barkan, E., Bickley, K., Blesie, N., Braun, T., Brouner, K., Budzillo, A., Caldejon, S., Casper, T., Castelli, D., Chong, P., & Koch, C. (2019). Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nature Neuroscience, 22(7), 1182–1195. https://doi.org/10.1038/s41593-019-0417-0

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gouwens, N. W., Sorensen, S. A., Baftizadeh, F., Budzillo, A., Lee, B. R., Jarsky, T., Alfiler, L., Arkhipov, A., Baker, K., Barkan, E., Berry, K., Bertagnolli, D., Bickley, K., Bomben, J., Braun, T., Brouner, K., Casper, T., Crichton, K., Daigle, T. L., & Zeng, H. (2020). Toward an integrated classification of neuronal cell types: Morphoelectric and transcriptomic characterization of individual GABAergic cortical neurons. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.02.03.932244

Article  Google Scholar 

Halavi, M., Polavaram, S., Donohue, D. E., Hamilton, G., Hoyt, J., Smith, K. P., & Ascoli, G. A. (2008). NeuroMorpho.Org implementation of digital neuroscience: Dense coverage and integration with the NIF. Neuroinformatics, 6(3), 241. https://doi.org/10.1007/s12021-008-9030-1

Article  PubMed  PubMed Central  Google Scholar 

Hartmann, K. G., Schirrmeister, R. T., & Ball, T. (2018). EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv. https://doi.org/10.48550/arXiv.1806.01875

Article  Google Scholar 

Haynes, V. R., Zhou, Y., & Crook, S. M. (2024). Discovering optimal features for neuron-type identification from extracellular recordings. Frontiers in Neuroinformatics, 18, 1303993. https://doi.org/10.3389/fninf.2024.1303993

Article  PubMed  PubMed Central  Google Scholar 

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2006.11239

Article  Google Scholar 

Jia, X., Siegle, J. H., Bennett, C., Gale, S. D., Denman, D. J., Koch, C., & Olsen, S. R. (2019). High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification. Journal of Neurophysiology, 121(5), 1831–1847. https://doi.org/10.1152/jn.00680.2018

Article  PubMed  Google Scholar 

Kathe, C., Skinnider, M. A., Hutson, T. H., Regazzi, N., Gautier, M., Demesmaeker, R., Komi, S., Ceto, S., James, N. D., Cho, N., Baud, L., Galan, K., Matson, K. J. E., Rowald, A., Kim, K., Wang, R., Minassian, K., Prior, J. O., Asboth, L., & Courtine, G. (2022). The neurons that restore walking after paralysis. Nature, 611(7936), Article 7936. https://doi.org/10.1038/s41586-022-05385-7

Article  CAS  Google Scholar 

Kobyzev, I., Prince, S. J. D., & Brubaker, M. A. (2019). Normalizing Flows: An Introduction and Review of Current Methods. https://doi.org/10.48550/ARXIV.1908.09257

Koene, R. A., Tijms, B., van Hees, P., Postma, F., de Ridder, A., Ramakers, G. J. A., van Pelt, J., & van Ooyen, A. (2009). NETMORPH: A framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies. Neuroinformatics, 7(3), 195–210. https://doi.org/10.1007/s12021-009-9052-3

Article  PubMed  Google Scholar 

Lee, E. K., Balasubramanian, H., Tsolias, A., Anakwe, S. U., Medalla, M., Shenoy, K. V., & Chandrasekaran, C. (2021). Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex. eLife, 10, e67490. https://doi.org/10.7554/eLife.67490

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lucic, M., Kurach, K., Michalski, M., Gelly, S., & Bousquet, O. (2017). Are GANs Created Equal? A Large-Scale Study (Version 4). arXiv. https://doi.org/10.48550/ARXIV.1711.10337

Article  Google Scholar 

Luo, L. (2021). Architectures of neuronal circuits. Science, 373(6559), eabg7285. https://doi.org/10.1126/science.abg7285

Article  CAS  PubMed  PubMed Central  Google Scholar 

Marouf, M., Machart, P., Bansal, V., Kilian, C., Magruder, D. S., Krebs, C. F., & Bonn, S. (2020). Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nature Communications. https://doi.org/10.1038/s41467-019-14018-z

Article  PubMed  PubMed Central  Google Scholar 

Masland, R. H. (2004). Neuronal cell types. Current Biology, 14(13), R497–R500. https://doi.org/10.1016/j.cub.2004.06.035

Article  CAS  PubMed  Google Scholar 

Molano-Mazon, M., Onken, A., Piasini, E., & Panzeri, S. (2018). Synthesizing realistic neural population activity patterns using generative adversarial networks (Version 2). ArXiv. https://doi.org/10.48550/ARXIV.1803.00338

Article  Google Scholar 

Mosher, C. P., Wei, Y., Kamiński, J., Nandi, A., Mamelak, A. N., Anastassiou, C. A., & Rutishauser, U. (2020). Cellular classes in the human brain revealed in vivo by Heartbeat-Related modulation of the extracellular action potential waveform. Cell Reports, 30(10), 3536–3551e6. https://doi.org/10.1016/j.celrep.2020.02.027

Article  CAS 

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