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361. Overview of NSTX Upgrade Initial Results and Modelling Highlights

Author(s):
Menard, J. E.; Allain, J. P.; Battaglia, D. J.; Bedoya, F.; Bell, R. E.; Belova, E.; Berkery, J. W.; Boyer, M. D.; Crocker, N.; Diallo, A.; Ebrahimi, F.; Ferraro, N.; Fredrickson, E.; Frerichs, H.; Gerhardt, S.; Gorelenkov, N.; Guttenfelder, W.; Heidbrink, W.; Kaita, R.; Kaye, S. M.; Kriete, D. M.; Kubota, S.; LeBlanc, B. P.; Liu, D.; Lunsford, R.; Mueller, D.; Myers, C. E.; Ono, M.; Park, J. -K.; Podesta, M.; Raman, R.; Reinke, M.; Ren, Y.; Sabbagh, S. M.; Schmitz, O.; Scotti, F.; Sechrest, Y.; Skinner, C. H.; Smith, D. R.; Soukhanovskii, V.; Stoltzfus-Dueck, T.; Yuh, H.; Wang, Z.; Waters, I.; Ahn, J. -W.; Andre, A.; Barchfeld, R.; Beiersdorfer, P.; Bertelli, N.; Bhattacharjee, A.; Boyle, D.; Brennan, D.; Buttery, R.; Capece, A.; Canal, G.; Canik, J.; Chang, C. S.; Darrow, D.; Delgado-Aparicio, L.; Domier, C.; Ethier, S.; Evans, T.; Ferron, J.; Finkenthal, M.; Fonck, R.; Gan, K.; Gates, D.; Goumiri, I.; Gray, T.; Hosea, J.; Humphreys, D.; Jarboe, T.; Jardin, S.; Jaworski, M. A.; Koel, B.; Kolemen, E.; Ku, S.; LaHaye, R. J.; Levinton, F.; Luhmann Jr., N.; Maingi, R.; Maqueda, R.; McKee, G.; Meier, E.; Myra, J.; Perkins, R.; Poli, F.; Rhodes, T.; Riquezes, J.; Rowley, C.; Russell, D.; Schuster, E.; Stratton, B.; Stutman, D.; Taylor, G.; Tritz, K.; Wang, W.; Wirth, B.; Zweben, S. J.
Type:
Dataset
Issue Date:
2017

368. The Structured `Low Temperature' Phase of the Retinal Population Code

Author(s):
Ioffe, Mark Lev; Berry II, Michael J.
Abstract:
Recent advances in experimental techniques have allowed the simultaneous recordings of populations of hundreds of neurons, fostering a debate about the nature of the collective structure of population neural activity. Much of this debate has focused on the empirical findings of a phase transition in the parameter space of maximum entropy models describing the measured neural probability distributions, interpreting this phase transition to indicate a critical tuning of the neural code. Here, we instead focus on the possibility that this is a first-order phase transition which provides evidence that the real neural population is in a `structured', collective state. We show that this collective state is robust to changes in stimulus ensemble and adaptive state. We find that the pattern of pairwise correlations between neurons has a strength that is well within the strongly correlated regime and does not require fine tuning, suggesting that this state is generic for populations of 100+ neurons. We find a clear correspondence between the emergence of a phase transition, and the emergence of attractor-like structure in the inferred energy landscape. A collective state in the neural population, in which neural activity patterns naturally form clusters, provides a consistent interpretation for our results.
Type:
Dataset
Issue Date:
2017