Skip to article frontmatterSkip to article content
Site not loading correctly?

This may be due to an incorrect BASE_URL configuration. See the MyST Documentation for reference.

📖 Reading material

Observing

Analysing

Manipulating

References
  1. Jun, J. J., Steinmetz, N. A., Siegle, J. H., Denman, D. J., Bauza, M., Barbarits, B., Lee, A. K., Anastassiou, C. A., Andrei, A., Aydın, Ç., Barbic, M., Blanche, T. J., Bonin, V., Couto, J., Dutta, B., Gratiy, S. L., Gutnisky, D. A., Häusser, M., Karsh, B., … Harris, T. D. (2017). Fully integrated silicon probes for high-density recording of neural activity. Nature, 551(7679), 232–236. 10.1038/nature24636
  2. Steinmetz, N. A., Aydin, C., Lebedeva, A., Okun, M., Pachitariu, M., Bauza, M., Beau, M., Bhagat, J., Böhm, C., Broux, M., Chen, S., Colonell, J., Gardner, R. J., Karsh, B., Kloosterman, F., Kostadinov, D., Mora-Lopez, C., O’Callaghan, J., Park, J., … Harris, T. D. (2021). Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science, 372(6539). 10.1126/science.abf4588
  3. Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M., & Keller, P. J. (2013). Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nature Methods, 10(5), 413–420. 10.1038/nmeth.2434
  4. Panier, T., Romano, S. A., Olive, R., Pietri, T., Sumbre, G., Candelier, R., & Debrégeas, G. (2013). Fast functional imaging of multiple brain regions in intact zebrafish larvae using Selective Plane Illumination Microscopy. Frontiers in Neural Circuits, 7. 10.3389/fncir.2013.00065
  5. Finn, E. S., Poldrack, R. A., & Shine, J. M. (2023). Functional neuroimaging as a catalyst for integrated neuroscience. Nature, 623(7986), 263–273. 10.1038/s41586-023-06670-9
  6. O’Keefe, J., & Dostrovsky, J. (1971). The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Research, 34(1), 171–175. 10.1016/0006-8993(71)90358-1
  7. Fyhn, M., Hafting, T., Treves, A., Moser, M.-B., & Moser, E. I. (2007). Hippocampal remapping and grid realignment in entorhinal cortex. Nature, 446(7132), 190–194. 10.1038/nature05601
  8. Glaser, J. I., Benjamin, A. S., Chowdhury, R. H., Perich, M. G., Miller, L. E., & Kording, K. P. (2020). Machine Learning for Neural Decoding. Eneuro, 7(4), ENEURO.0506-19.2020. 10.1523/eneuro.0506-19.2020
  9. Williams, A. H., Kim, T. H., Wang, F., Vyas, S., Ryu, S. I., Shenoy, K. V., Schnitzer, M., Kolda, T. G., & Ganguli, S. (2018). Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis. Neuron, 98(6), 1099-1115.e8. 10.1016/j.neuron.2018.05.015
  10. Lopes, G., Nogueira, J., Dimitriadis, G., Menendez, J. A., Paton, J. J., & Kampff, A. R. (2023). A robust role for motor cortex. Frontiers in Neuroscience, 17. 10.3389/fnins.2023.971980
  11. Pastrana, E. (2010). Optogenetics: controlling cell function with light. Nature Methods, 8(1), 24–25. 10.1038/nmeth.f.323
  12. Fakhar, K., & Hilgetag, C. C. (2022). Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain. PLOS Computational Biology, 18(6), e1010250. 10.1371/journal.pcbi.1010250