dc.date.accessioned | 2020-01-23T11:33:43Z | |
dc.date.available | 2020-01-23T11:33:43Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | http://hdl.handle.net/10852/72480 | |
dc.description.abstract | With the advent of high-density multi-electrode arrays we are now able to measure the activity of hundreds of neurons simultaneously, even at the sub-cellular level. However, next-generation devices introduce novel grand challenges and the need for appropriate tools to handle the rich information that can be recorded. The work presented in this thesis has therefore focused on developing and benchmarking new tools and methods for using such devices at their full potential.
Main research findings
Neurons use tiny electrical signals to communicate with each other. By inserting electrodes in the brain, we can read from neurons (record electrical activity) and even write to them (induce activity by electrical stimulation).
In recent years there has been a huge development in neural devices: neuroscientists can now use probes with several hundreds of very closely-spaced electrodes called Multi-Electrode Arrays. The goal of my PhD was to develop methods and tools to improve the way we read from and write to the brain tissue using these newly developed probes.
In order to achieve my goal, I followed a computationally-assisted approach. The idea is to use very detailed models of single neurons (mathematical description of how the neuron behaves) to run simulations, that can be used to guide the development of new analysis methods. I used this approach to tackle several open problems of extracellular electrophysiology, including spike sorting, neuron localization, cell-type classification, and selective electrical microstimulation of neurons.
The outcome of this work is a collection of analytical and computational tools that will contribute to shed light on how this extremely fascinating and complicated organ that sits on our shoulders works. | en_US |
dc.language.iso | en | en_US |
dc.relation.haspart | Paper I: Buccino AP, and Einevoll GT. MEArec: a fast and customizable testbench simulator for extracellular spiking activity. Neuroinform (2020). DOI: 10.1007/s12021-020-09467-7. The paper is included in the thesis. The published version is available at https://doi.org/10.1007/s12021-020-09467-7 | |
dc.relation.haspart | Paper II: Buccino, AP, Hurwitz C, Magland J, Garcia S, Siegle JH, Hurwitz R, and Hennig MH. SpikeInterface, a unified framework for spike sorting. eLIFE (2020). DOI: 10.7554/eLife.61834. The paper is included in the thesis. The published version is available at https://doi.org/10.7554/eLife.61834 | |
dc.relation.haspart | Paper III: Buccino AP, Hagen E, Einevoll GT, Häfliger PD, and Cauwenberghs G. Independent component analysis for fully automated multi-electrode array spike sorting. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2018), pp. 2627–2630. DOI: 10.1109/EMBC.2018.8512788. The paper is not available in DUO due to publisher restrictions. The published version is available at: https://doi.org/10.1109/EMBC.2018.8512788 | |
dc.relation.haspart | Paper IV: Buccino AP, Hsu S-H, and Cauwenberghs, G. Real-time spike sorting for multi-electrode arrays with online independent component analysis. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) (2018), pp. 1–4. DOI: 10.1109/BIOCAS.2018.8584797. The paper is not available in DUO due to publisher restrictions. The published version is available at: https://doi.org/10.1109/BIOCAS.2018.8584797 | |
dc.relation.haspart | Paper V: Buccino AP, Kordovan M, Ness TV, Merkt B, Häfliger PD, Fyhn M, Cauwenberghs G, Rotter S, and Einevoll GT. Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification. In: Journal of Neurophysiology 120-3 (2018), pp. 1212–1232. DOI: 10.1152/jn.00210.2018. The article is included in the thesis. Also available in DUO: http://urn.nb.no/URN:NBN:no-71167 | |
dc.relation.haspart | Paper VI: Buccino AP, Stöber T, Næss S, Cauwenberghs G, and Häfliger PD. Extracellular single neuron stimulation with high-density multielectrode array. In: 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS) (2016), pp. 520–523. DOI: 10.1109/BioCAS.2016.7833846. The paper is not available in DUO due to publisher restrictions. The published version is available at: https://doi.org/10.1109/BioCAS.2016.7833846 | |
dc.relation.haspart | Paper VII: Buccino AP, Kuchta M, Jæger KH, Ness TV, Berthet P, Mardal KA, Cauwenberghs G, and Tveito A. How does the presence of neural probes affect extracellular potentials? In: Journal of Neural Engineering 16–2 (2019), pp. 026030. DOI: 10.1088/1741-2552/ab03a1. The article is included in the thesis. Also available in DUO: http://hdl.handle.net/10852/72481 | |
dc.relation.uri | https://doi.org/10.1007/s12021-020-09467-7 | |
dc.relation.uri | https://doi.org/10.1109/BIOCAS.2018.8584797 | |
dc.relation.uri | http://urn.nb.no/URN:NBN:no-71167 | |
dc.relation.uri | https://doi.org/10.1109/BioCAS.2016.7833846 | |
dc.relation.uri | http://hdl.handle.net/10852/72481 | |
dc.relation.uri | https://doi.org/10.7554/eLife.61834 | |
dc.relation.uri | https://doi.org/10.1109/EMBC.2018.8512788 | |
dc.title | A computationally-assisted approach to extracellular neural electrophysiology with multi-electrode arrays | en_US |
dc.type | Doctoral thesis | en_US |
dc.creator.author | Buccino, Alessio Paolo | |
dc.identifier.urn | URN:NBN:no-75626 | |
dc.type.document | Doktoravhandling | en_US |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/72480/3/PhD-Buccino-2020.pdf | |