Nature reviews. Neuroscience, Год журнала: 2022, Номер 23(12), С. 744 - 766
Опубликована: Ноя. 3, 2022
Язык: Английский
Nature reviews. Neuroscience, Год журнала: 2022, Номер 23(12), С. 744 - 766
Опубликована: Ноя. 3, 2022
Язык: Английский
Nature Neuroscience, Год журнала: 2015, Номер 18(9), С. 1213 - 1225
Опубликована: Авг. 26, 2015
Язык: Английский
Процитировано
1169Nature reviews. Neuroscience, Год журнала: 2017, Номер 18(4), С. 222 - 235
Опубликована: Март 17, 2017
Язык: Английский
Процитировано
648Nature Neuroscience, Год журнала: 2016, Номер 19(9), С. 1142 - 1153
Опубликована: Авг. 26, 2016
Язык: Английский
Процитировано
620Nature Reviews Materials, Год журнала: 2016, Номер 1(10)
Опубликована: Сен. 27, 2016
Язык: Английский
Процитировано
597Nature reviews. Neuroscience, Год журнала: 2019, Номер 20(6), С. 330 - 345
Опубликована: Март 4, 2019
Язык: Английский
Процитировано
585PLoS Computational Biology, Год журнала: 2017, Номер 13(3), С. e1005423 - e1005423
Опубликована: Март 14, 2017
Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting each neuron from raw fluorescence imaging data is nontrivial problem. We present fast online active set method to solve this sparse non-negative deconvolution Importantly, algorithm progresses through time series sequentially beginning end, thus enabling real-time estimation neural during session. Our generalization pool adjacent violators (PAVA) isotonic regression and inherits its linear-time computational complexity. gain remarkable increases in processing speed: more than one order magnitude compared currently employed state art convex solvers relying on interior point methods. Unlike these approaches, our can exploit warm starts; therefore optimizing model hyperparameters only requires handful passes data. A minor modification further improve quality inference by imposing constraint minimum spike size. The enables simultaneous $O(10^5)$ traces whole-brain larval zebrafish laptop.
Язык: Английский
Процитировано
533Neuron, Год журнала: 2017, Номер 95(2), С. 424 - 435.e6
Опубликована: Июль 1, 2017
Язык: Английский
Процитировано
508Nature Methods, Год журнала: 2016, Номер 13(4), С. 325 - 328
Опубликована: Фев. 15, 2016
Язык: Английский
Процитировано
442Neuron, Год журнала: 2015, Номер 88(2), С. 264 - 276
Опубликована: Окт. 1, 2015
Язык: Английский
Процитировано
395Journal of Computational Neuroscience, Год журнала: 2016, Номер 41(1), С. 1 - 14
Опубликована: Июнь 11, 2016
The language of graph theory, or network science, has proven to be an exceptional tool for addressing myriad problems in neuroscience. Yet, the use networks is predicated on a critical simplifying assumption: that quintessential unit interest brain dyad – two nodes (neurons regions) connected by edge. While rarely mentioned, this fundamental assumption inherently limits types neural structure and function graphs can used model. Here, we describe generalization overcomes these limitations, thereby offering broad range new possibilities terms modeling measuring phenomena. Specifically, explore simplicial complexes: developed field mathematics known as algebraic topology, increasing applicability real data due rapidly growing computational toolset. We review underlying mathematical formalism well budding literature applying complexes data, from electrophysiological recordings animal models hemodynamic fluctuations humans. Based flexibility tools recent ground-breaking insights into function, posit framework potential eclipse theory unraveling mysteries cognition.
Язык: Английский
Процитировано
372