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1. Systemic Oversimplification Limits the Potential for Human-AI Partnership

Author

Jason S. Metcalfe, Brandon S. Perelman, David L. Boothe, and Kaleb Mcdowell

Subjects
Human-AI partnership, human-autonomy teaming, sociotechnical systems, AI ecosystems, function allocation, task complexity, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The modern world is evolving rapidly, especially with respect to the development and proliferation of increasingly intelligent, artificial intelligence (AI) and AI-related technologies. Nevertheless, in many ways, what this class of technologies has offered as return on investment remains less impressive than what has been promised. In the present paper, we argue that the continued failure to realize the potential in modern AI and AI-related technologies is largely attributable to the oversimplified, yet pervasive ways that our global society treats the relationship between these technologies and humans. Oversimplified concepts, once conveyed, tend to perpetuate myths that in turn limit the impact of such technologies in human society. To counter these oversimplifications, we offer a theoretical construct, which we call the landscape of human-AI partnership. This construct characterizes individual capability for real-world task performance as a dynamic function of information certainty, available time to respond, and task complexity. With this, our goal is to encourage more nuanced discourse about novel ways to solve challenges to modern and future sociotechnical societies, but without defaulting to notions that remain rooted in today’s technologies-as-tools ways of thinking. The core of our argument is that society at large must recognize that intelligent technologies are evolving well beyond being mere tools for human use and are instead becoming capable of operating as interdependent teammates. This means that how we think about interactions between humans and AI needs to go beyond a “Human–or–AI” conversation about task assignments to more contextualized “Human–and–AI” way of thinking about how best to capitalize on the strengths hidden within emergent capabilities of unique human-AI partnerships that have yet to be fully realized.

Published
2021
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2. Assessing the Impact of Ih Conductance on Cross-Frequency Coupling in Model Pyramidal Neurons

Author

Melvin A. Felton, Alfred B. Yu, David L. Boothe, Kelvin S. Oie, and Piotr J. Franaszczuk

Subjects
cross-frequency coupling, phase-amplitude coupling, theta-gamma coupling, pyramidal neuron, Ih conductance, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Large cortical and hippocampal pyramidal neurons are elements of neuronal circuitry that have been implicated in cross-frequency coupling (CFC) during cognitive tasks. We investigate potential mechanisms for CFC within these neurons by examining the role that the hyperpolarization-activated mixed cation current (Ih) plays in modulating CFC characteristics in multicompartment neuronal models. We quantify CFC along the soma-apical dendrite axis and tuft of three models configured to have different spatial distributions of Ih conductance density: (1) exponential gradient along the soma-apical dendrite axis, (2) uniform distribution, and (3) no Ih conductance. We simulated two current injection scenarios: distal apical 4 Hz modulation and perisomatic 4 Hz modulation, each with perisomatic, mid-apical, and distal apical 40 Hz injections. We used two metrics to quantify CFC strength—modulation index and height ratio—and we analyzed CFC phase properties. For all models, CFC was strongest in distal apical regions when the 40 Hz injection occurred near the soma and the 4 Hz modulation occurred in distal apical dendrite. The strongest CFC values were observed in the model with uniformly distributed Ih conductance density, but when the exponential gradient in Ih conductance density was added, CFC strength decreased by almost 50%. When Ih was in the model, regions with much larger membrane potential fluctuations at 4 Hz than at 40 Hz had stronger CFC. Excluding the Ih conductance from the model resulted in CFC either reduced or comparable in strength relative to the model with the exponential gradient in Ih conductance. The Ih conductance also imposed order on the phase characteristics of CFC such that minimum (maximum) amplitude 40 Hz membrane potential oscillations occurred during Ih conductance deactivation (activation). On the other hand, when there was no Ih conductance, phase relationships between minimum and maximum 40 Hz oscillation often inverted and occurred much closer together. This analysis can help experimentalists discriminate between CFC that originates from different underlying physiological mechanisms and can help illuminate the reasons why there are differences between CFC strength observed in different regions of the brain and between different populations of neurons based on the configuration of the Ih conductance.

Published
2020
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3. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Author

Leonid L. Rubchinsky, Sungwoo Ahn, Wouter Klijn, Ben Cumming, Stuart Yates, Vasileios Karakasis, Alexander Peyser, Marmaduke Woodman, Sandra Diaz-Pier, James Deraeve, Eliana Vassena, William Alexander, David Beeman, Pawel Kudela, Dana Boatman-Reich, William S. Anderson, Niceto R. Luque, Francisco Naveros, Richard R. Carrillo, Eduardo Ros, Angelo Arleo, Jacob Huth, Koki Ichinose, Jihoon Park, Yuji Kawai, Junichi Suzuki, Hiroki Mori, Minoru Asada, Sorinel A. Oprisan, Austin I. Dave, Tahereh Babaie, Peter Robinson, Alejandro Tabas, Martin Andermann, André Rupp, Emili Balaguer-Ballester, Henrik Lindén, Rasmus K. Christensen, Mari Nakamura, Tania R. Barkat, Zach Tosi, John Beggs, Davide Lonardoni, Fabio Boi, Stefano Di Marco, Alessandro Maccione, Luca Berdondini, Joanna Jędrzejewska-Szmek, Daniel B. Dorman, Kim T. Blackwell, Christoph Bauermeister, Hanna Keren, Jochen Braun, João V. Dornas, Eirini Mavritsaki, Silvio Aldrovandi, Emma Bridger, Sukbin Lim, Nicolas Brunel, Anatoly Buchin, Clifford Charles Kerr, Anton Chizhov, Gilles Huberfeld, Richard Miles, Boris Gutkin, Martin J. Spencer, Hamish Meffin, David B. Grayden, Anthony N. Burkitt, Catherine E. Davey, Liangyu Tao, Vineet Tiruvadi, Rehman Ali, Helen Mayberg, Robert Butera, Cengiz Gunay, Damon Lamb, Ronald L. Calabrese, Anca Doloc-Mihu, Víctor J. López-Madrona, Fernanda S. Matias, Ernesto Pereda, Claudio R. Mirasso, Santiago Canals, Alice Geminiani, Alessandra Pedrocchi, Egidio D’Angelo, Claudia Casellato, Ankur Chauhan, Karthik Soman, V. Srinivasa Chakravarthy, Vignayanandam R. Muddapu, Chao-Chun Chuang, Nan-yow Chen, Mehdi Bayati, Jan Melchior, Laurenz Wiskott, Amir Hossein Azizi, Kamran Diba, Sen Cheng, Elena Y. Smirnova, Elena G. Yakimova, Anton V. Chizhov, Nan-Yow Chen, Chi-Tin Shih, Dorian Florescu, Daniel Coca, Julie Courtiol, Viktor K. Jirsa, Roberto J. M. Covolan, Bartosz Teleńczuk, Richard Kempter, Gabriel Curio, Alain Destexhe, Jessica Parker, Alexander N. Klishko, Boris I. Prilutsky, Gennady Cymbalyuk, Felix Franke, Andreas Hierlemann, Rava Azeredo da Silveira, Stefano Casali, Stefano Masoli, Martina Rizza, Martina Francesca Rizza, Yinming Sun, Willy Wong, Faranak Farzan, Daniel M. Blumberger, Zafiris J. Daskalakis, Svitlana Popovych, Shivakumar Viswanathan, Nils Rosjat, Christian Grefkes, Silvia Daun, Damiano Gentiletti, Piotr Suffczynski, Vadym Gnatkovski, Marco De Curtis, Hyeonsu Lee, Se-Bum Paik, Woochul Choi, Jaeson Jang, Youngjin Park, Jun Ho Song, Min Song, Vicente Pallarés, Matthieu Gilson, Simone Kühn, Andrea Insabato, Gustavo Deco, Katharina Glomb, Adrián Ponce-Alvarez, Petra Ritter, Adria Tauste Campo, Alexander Thiele, Farah Deeba, P. A. Robinson, Sacha J. van Albada, Andrew Rowley, Michael Hopkins, Maximilian Schmidt, Alan B. Stokes, David R. Lester, Steve Furber, Markus Diesmann, Alessandro Barri, Martin T. Wiechert, David A. DiGregorio, Alexander G. Dimitrov, Catalina Vich, Rune W. Berg, Antoni Guillamon, Susanne Ditlevsen, Romain D. Cazé, Benoît Girard, Stéphane Doncieux, Nicolas Doyon, Frank Boahen, Patrick Desrosiers, Edward Laurence, Louis J. Dubé, Russo Eleonora, Daniel Durstewitz, Dominik Schmidt, Tuomo Mäki-Marttunen, Florian Krull, Francesco Bettella, Christoph Metzner, Anna Devor, Srdjan Djurovic, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll, Solveig Næss, Torbjørn V. Ness, Geir Halnes, Eric Halgren, Klas H. Pettersen, Marte J. Sætra, Espen Hagen, Alina Schiffer, Axel Grzymisch, Malte Persike, Udo Ernst, Daniel Harnack, Udo A. Ernst, Nergis Tomen, Stefano Zucca, Valentina Pasquale, Giuseppe Pica, Manuel Molano-Mazón, Michela Chiappalone, Stefano Panzeri, Tommaso Fellin, Kelvin S. Oie, David L. Boothe, Joshua C. Crone, Alfred B. Yu, Melvin A. Felton, Isma Zulfiqar, Michelle Moerel, Peter De Weerd, Elia Formisano, Kelvin Oie, Piotr Franaszczuk, Roland Diggelmann, Michele Fiscella, Domenico Guarino, Jan Antolík, Andrew P. Davison, Yves Frègnac, Benjamin Xavier Etienne, Flavio Frohlich, Jérémie Lefebvre, Encarni Marcos, Maurizio Mattia, Aldo Genovesio, Leonid A. Fedorov, Tjeerd M.H. Dijkstra, Louisa Sting, Howard Hock, Martin A. Giese, Laure Buhry, Clément Langlet, Francesco Giovannini, Christophe Verbist, Stefano Salvadé, Michele Giugliano, James A. Henderson, Hendrik Wernecke, Bulcsú Sándor, Claudius Gros, Nicole Voges, Paulina Dabrovska, Alexa Riehle, Thomas Brochier, Sonja Grün, Yifan Gu, Pulin Gong, Grégory Dumont, Nikita A. Novikov, Boris S. Gutkin, Parul Tewatia, Olivia Eriksson, Andrei Kramer, Joao Santos, Alexandra Jauhiainen, Jeanette H. Kotaleski, Jovana J. Belić, Arvind Kumar, Jeanette Hellgren Kotaleski, Masanori Shimono, Naomichi Hatano, Subutai Ahmad, Yuwei Cui, Jeff Hawkins, Johanna Senk, Karolína Korvasová, Tom Tetzlaff, Moritz Helias, Tobias Kühn, Michael Denker, PierGianLuca Mana, David Dahmen, Jannis Schuecker, Sven Goedeke, Christian Keup, Katja Heuer, Rembrandt Bakker, Paul Tiesinga, Roberto Toro, Wei Qin, Alex Hadjinicolaou, Michael R. Ibbotson, Tatiana Kameneva, William W. Lytton, Lealem Mulugeta, Andrew Drach, Jerry G. Myers, Marc Horner, Rajanikanth Vadigepalli, Tina Morrison, Marlei Walton, Martin Steele, C. Anthony Hunt, Nicoladie Tam, Rodrigo Amaducci, Carlos Muñiz, Manuel Reyes-Sánchez, Francisco B. Rodríguez, Pablo Varona, Joseph T. Cronin, Matthias H. Hennig, Elisabetta Iavarone, Jane Yi, Ying Shi, Bas-Jan Zandt, Werner Van Geit, Christian Rössert, Henry Markram, Sean Hill, Christian O’Reilly, Rodrigo Perin, Huanxiang Lu, Alexander Bryson, Michal Hadrava, Jaroslav Hlinka, Ryosuke Hosaka, Mark Olenik, Conor Houghton, Nicolangelo Iannella, Thomas Launey, Rebecca Kotsakidis, Jaymar Soriano, Takatomi Kubo, Takao Inoue, Hiroyuki Kida, Toshitaka Yamakawa, Michiyasu Suzuki, Kazushi Ikeda, Samira Abbasi, Amber E. Hudson, Detlef H. Heck, Dieter Jaeger, Joel Lee, Skirmantas Janušonis, Maria Luisa Saggio, Andreas Spiegler, William C. Stacey, Christophe Bernard, Davide Lillo, Spase Petkoski, Mark Drakesmith, Derek K. Jones, Ali Sadegh Zadeh, Chandra Kambhampati, Jan Karbowski, Zeynep Gokcen Kaya, Yair Lakretz, Alessandro Treves, Lily W. Li, Joseph Lizier, Cliff C. Kerr, Timothée Masquelier, Saeed Reza Kheradpisheh, Hojeong Kim, Chang Sub Kim, Julia A. Marakshina, Alexander V. Vartanov, Anastasia A. Neklyudova, Stanislav A. Kozlovskiy, Andrey A. Kiselnikov, Kanako Taniguchi, Katsunori Kitano, Oliver Schmitt, Felix Lessmann, Sebastian Schwanke, Peter Eipert, Jennifer Meinhardt, Julia Beier, Kanar Kadir, Adrian Karnitzki, Linda Sellner, Ann-Christin Klünker, Lena Kuch, Frauke Ruß, Jörg Jenssen, Andreas Wree, Paula Sanz-Leon, Stuart A. Knock, Shih-Cheng Chien, Burkhard Maess, Thomas R. Knösche, Charles C. Cohen, Marko A. Popovic, Jan Klooster, Maarten H.P. Kole, Erik A. Roberts, Nancy J. Kopell, Daniel Kepple, Hamza Giaffar, Dima Rinberg, Alex Koulakov, Caroline Garcia Forlim, Leonie Klock, Johanna Bächle, Laura Stoll, Patrick Giemsa, Marie Fuchs, Nikola Schoofs, Christiane Montag, Jürgen Gallinat, Ray X. Lee, Greg J. Stephens, Bernd Kuhn, Luiz Tauffer, Philippe Isope, Katsuma Inoue, Yoshiyuki Ohmura, Shogo Yonekura, Yasuo Kuniyoshi, Hyun Jae Jang, Jeehyun Kwag, Marc de Kamps, Yi Ming Lai, Filipa dos Santos, K. P. Lam, Peter Andras, Julia Imperatore, Jessica Helms, Tamas Tompa, Antonieta Lavin, Felicity H. Inkpen, Michael C. Ashby, Nathan F. Lepora, Aaron R. Shifman, John E. Lewis, Zhong Zhang, Yeqian Feng, Christian Tetzlaff, Tomas Kulvicius, Yinyun Li, Rodrigo F. O. Pena, Davide Bernardi, Antonio C. Roque, Benjamin Lindner, Sebastian Vellmer, Ausra Saudargiene, Tiina Maninen, Riikka Havela, Marja-Leena Linne, Arthur Powanwe, Andre Longtin, Jesús A. Garrido, Joe W. Graham, Salvador Dura-Bernal, Sergio L. Angulo, Samuel A. Neymotin, and Srdjan D. Antic

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurophysiology and neuropsychology, QP351-495
Published
2017
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4. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3

Author

Adam J. H. Newton, Alexandra H. Seidenstein, Robert A. McDougal, Alberto Pérez-Cervera, Gemma Huguet, Tere M-Seara, Caroline Haimerl, David Angulo-Garcia, Alessandro Torcini, Rosa Cossart, Arnaud Malvache, Kaoutar Skiker, Mounir Maouene, Gianmarco Ragognetti, Letizia Lorusso, Andrea Viggiano, Angelo Marcelli, Rosa Senatore, Antonio Parziale, S. Stramaglia, M. Pellicoro, L. Angelini, E. Amico, H. Aerts, J. Cortés, S. Laureys, D. Marinazzo, I. Bassez, L. Faes, Hannes Almgren, Adeel Razi, Frederik Van de Steen, Ruth Krebs, Hannelore Aerts, Lida Kanari, Pawel Dlotko, Martina Scolamiero, Ran Levi, Julian Shillcock, Christiaan P.J. de Kock, Kathryn Hess, Henry Markram, Cheng Ly, Gary Marsat, Tom Gillespie, Malin Sandström, Mathew Abrams, Jeffrey S. Grethe, Maryann Martone, Robin De Gernier, Sergio Solinas, Christian Rössert, Marc Haelterman, Serge Massar, Valentina Pasquale, Vito Paolo Pastore, Sergio Martinoia, Paolo Massobrio, Cristiano Capone, Núria Tort-Colet, Maria V. Sanchez-Vives, Maurizio Mattia, Ali Almasi, Shaun L. Cloherty, David B. Grayden, Yan T. Wong, Michael R. Ibbotson, Hamish Meffin, Luke Y. Prince, Krasimira Tsaneva-Atanasova, Jack R. Mellor, Alberto Mazzoni, Manuela Rosa, Jacopo Carpaneto, Luigi M. Romito, Alberto Priori, Silvestro Micera, Rosanna Migliore, Carmen Alina Lupascu, Francesco Franchina, Luca Leonardo Bologna, Armando Romani, Sára Saray, Werner Van Geit, Szabolcs Káli, Alex Thomson, Audrey Mercer, Sigrun Lange, Joanne Falck, Eilif Muller, Felix Schürmann, Dmitrii Todorov, Robert Capps, William Barnett, Yaroslav Molkov, Federico Devalle, Diego Pazó, Ernest Montbrió, Gabriela Mochol, Habiba Azab, Benjamin Y. Hayden, Rubén Moreno-Bote, Pragathi Priyadharsini Balasubramani, Srinivasa V. Chakravarthy, Vignayanandam R. Muddapu, Medorian D. Gheorghiu, Bartul Mimica, Jonathan Withlock, Raul C. Mureșan, Jennifer L. Zick, Kelsey Schultz, Rachael K. Blackman, Matthew V. Chafee, Theoden I. Netoff, Nicholas Roberts, Vivek Nagaraj, Andrew Lamperski, Logan L. Grado, Matthew D. Johnson, David P. Darrow, Davide Lonardoni, Hayder Amin, Stefano Di Marco, Alessandro Maccione, Luca Berdondini, Thierry Nieus, Marcel Stimberg, Dan F. M. Goodman, Thomas Nowotny, Veronika Koren, Valentin Dragoi, Klaus Obermayer, Samy Castro, Mariano Fernandez, Wael El-Deredy, Kesheng Xu, Jean Paul Maidana, Patricio Orio, Weiliang Chen, Iain Hepburn, Francesco Casalegno, Adrien Devresse, Aleksandr Ovcharenko, Fernando Pereira, Fabien Delalondre, Erik De Schutter, Peter Bratby, Andrew R. Gallimore, Guido Klingbeil, Criseida Zamora, Yunliang Zang, Patrick Crotty, Eric Palmerduca, Alberto Antonietti, Claudia Casellato, Csaba Erö, Egidio D’Angelo, Marc-Oliver Gewaltig, Alessandra Pedrocchi, Ilja Bytschok, Dominik Dold, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici, Hui-An Shen, Simone Carlo Surace, Jean-Pascal Pfister, Baptiste Lefebvre, Olivier Marre, Pierre Yger, Athanasia Papoutsi, Jiyoung Park, Ryan Ash, Stelios Smirnakis, Panayiota Poirazi, Richard A. Felix, Alexander G. Dimitrov, Christine Portfors, Silvia Daun, Tibor I. Toth, Joanna Jędrzejewska-Szmek, Nadine Kabbani, Kim T. Blackwel, Bahar Moezzi, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Nicolangelo Iannella, Michael C. Ridding, Jochen Triesch, Reinoud Maex, Karen Safaryan, Volker Steuber, Rongxiang Tang, Yi-Yuan Tang, Darya V. Verveyko, Alexey R. Brazhe, Andrey Yu Verisokin, Dmitry E. Postnov, Cengiz Günay, Gabriella Panuccio, Michele Giugliano, Astrid A. Prinz, Pablo Varona, Mikhail I. Rabinovich, Jack Denham, Thomas Ranner, Netta Cohen, Maria Reva, Nelson Rebola, Tekla Kirizs, Zoltan Nusser, David DiGregorio, Eirini Mavritsaki, Panos Rentzelas, Nikul H. Ukani, Adam Tomkins, Chung-Heng Yeh, Wesley Bruning, Allison L. Fenichel, Yiyin Zhou, Yu-Chi Huang, Dorian Florescu, Carlos Luna Ortiz, Paul Richmond, Chung-Chuan Lo, Daniel Coca, Ann-Shyn Chiang, Aurel A. Lazar, Jennifer L. Creaser, Congping Lin, Peter Ashwin, Jonathan T. Brown, Thomas Ridler, Daniel Levenstein, Brendon O. Watson, György Buzsáki, John Rinzel, Rodica Curtu, Anh Nguyen, Sahand Assadzadeh, Peter A. Robinson, Paula Sanz-Leon, Caroline G. Forlim, Lírio O. B. de Almeida, Reynaldo D. Pinto, Francisco B. Rodríguez, Ángel Lareo, Caroline Garcia Forlim, Aaron Montero, Thiago Mosqueiro, Ramon Huerta, Francisco B. Rodriguez, Vinicio Changoluisa, Vinícius L. Cordeiro, César C. Ceballos, Nilton L. Kamiji, Antonio C. Roque, William W. Lytton, Andrew Knox, Joshua J. C. Rosenthal, Svitlana Popovych, Liqing Liu, Bin A. Wang, Tibor I. Tóth, Christian Grefkes, Gereon R. Fink, Nils Rosjat, Abraham Perez-Trujillo, Andres Espinal, Marco A. Sotelo-Figueroa, Ivan Cruz-Aceves, Horacio Rostro-Gonzalez, Martin Zapotocky, Martina Hoskovcová, Jana Kopecká, Olga Ulmanová, Evžen Růžička, Matthias Gärtner, Sevil Duvarci, Jochen Roeper, Gaby Schneider, Stefan Albert, Katharina Schmack, Michiel Remme, Susanne Schreiber, Michele Migliore, Carmen A. Lupascu, Luca L. Bologna, Stefano M. Antonel, Jean-Denis Courcol, Sami Utku Çelikok, Eva M. Navarro-López, Neslihan Serap Şengör, Rahmi Elibol, Neslihan Serap Sengor, Mustafa Yasir Özdemir, Tianyi Li, Angelo Arleo, Denis Sheynikhovich, Akihiro Nakamura, Masanori Shimono, Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin, Sadra Sadeh, Padraig Gleeson, R. Angus Silver, Alexandra Pierri Chatzikalymniou, Frances K. Skinner, Lazaro M. Sanchez-Rodriguez, Roberto C. Sotero, Loreen Hertäg, Owen Mackwood, Henning Sprekeler, Steffen Puhlmann, Simon N. Weber, David Higgins, Laura B. Naumann, Ramakrisnan Iyer, Stefan Mihalas, Valentina Ticcinelli, Tomislav Stankovski, Peter V. E. McClintock, Aneta Stefanovska, Predrag Janjić, Dimitar Solev, Gerald Seifert, Ljupčo Kocarev, Christian Steinhäuser, Mehrdad Salmasi, Stefan Glasauer, Martin Stemmler, Danke Zhang, Chi Zhang, Armen Stepanyants, Julia Goncharenko, Lieke Kros, Neil Davey, Chris de Zeeuw, Freek Hoebeek, Ankur Sinha, Roderick Adams, Michael Schmuker, Maria Psarrou, Maria Schilstra, Benjamin Torben-Nielsen, Christoph Metzner, Achim Schweikard, Tuomo Mäki-Marttunen, Bartosz Zurowski, Daniele Marinazzo, Luca Faes, Sebastiano Stramaglia, Henry O. C. Jordan, Simon M. Stringer, Elżbieta Gajewska-Dendek, Piotr Suffczyński, Nicoladie Tam, George Zouridakis, Luca Pollonini, Mojtaba Madadi Asl, Alireza Valizadeh, Peter A. Tass, Andreas Nold, Wei Fan, Sara Konrad, Heiko Endle, Johannes Vogt, Tatjana Tchumatchenko, Juliane Herpich, Christian Tetzlaff, Jannik Luboeinski, Timo Nachstedt, Manuel Ciba, Andreas Bahmer, Christiane Thielemann, Eric S. Kuebler, Joseph S. Tauskela, Jean-Philippe Thivierge, Rembrandt Bakker, María García-Amado, Marian Evangelio, Francisco Clascá, Paul Tiesinga, Christopher L. Buckley, Taro Toyoizumi, Alexis M. Dubreuil, Rémi Monasson, Alessandro Treves, Davide Spalla, Sophie Rosay, Florence I. Kleberg, Willy Wong, Bruno de Oliveira Floriano, Toshihiko Matsuo, Tetsuya Uchida, Domenica Dibenedetto, Kâmil Uludağ, Abdorreza Goodarzinick, Maximilian Schmidt, Claus C. Hilgetag, Markus Diesmann, Sacha J. van Albada, Michael Fauth, Mark van Rossum, Manuel Reyes-Sánchez, Rodrigo Amaducci, Carlos Muñiz, Irene Elices, David Arroyo, Rafael Levi, Ben Cohen, Carson Chow, Shashaank Vattikuti, Elena Bertolotti, Raffaella Burioni, Matteo di Volo, Alessandro Vezzani, Bayar Menzat, Tim P. Vogels, Nobuhiko Wagatsuma, Susmita Saha, Reena Kapoor, Robert Kerr, John Wagner, Luis C. Garcia del Molino, Guangyu Robert Yang, Jorge F. Mejias, Xiao-Jing Wang, Hanbing Song, Joseph Goodliffe, Jennifer Luebke, Christina M. Weaver, John Thomas, Nishant Sinha, Nikhita Shaju, Tomasz Maszczyk, Jing Jin, Sydney S. Cash, Justin Dauwels, M. Brandon Westover, Maryam Karimian, Michelle Moerel, Peter De Weerd, Thomas Burwick, Ronald L. Westra, Romesh Abeysuriya, Jonathan Hadida, Stamatios Sotiropoulos, Saad Jbabdi, Mark Woolrich, Chama Bensmail, Borys Wrobel, Xiaolong Zhou, Zilong Ji, Xiao Liu, Yan Xia, Si Wu, Xiao Wang, Mingsha Zhang, Netanel Ofer, Orit Shefi, Gur Yaari, Ted Carnevale, Amit Majumdar, Subhashini Sivagnanam, Kenneth Yoshimoto, Elena Y. Smirnova, Dmitry V. Amakhin, Sergey L. Malkin, Aleksey V. Zaitsev, Anton V. Chizhov, Margarita Zaleshina, Alexander Zaleshin, Victor J. Barranca, George Zhu, Quinton M. Skilling, Daniel Maruyama, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski, Jiaxing Wu, Sara Aton, Scott Rich, Victoria Booth, Maral Budak, Salvador Dura-Bernal, Samuel A. Neymotin, Benjamin A. Suter, Gordon M. G. Shepherd, Melvin A. Felton, Alfred B. Yu, David L. Boothe, Kelvin S. Oie, Piotr J. Franaszczuk, Sergey A. Shuvaev, Batuhan Başerdem, Anthony Zador, Alexei A. Koulakov, Víctor J. López-Madrona, Ernesto Pereda, Claudio R. Mirasso, Santiago Canals, Stefano Masoli, Udaya B. Rongala, Anton Spanne, Henrik Jorntell, Calogero M. Oddo, Alexander V. Vartanov, Anastasia K. Neklyudova, Stanislav A. Kozlovskiy, Andrey A. Kiselnikov, Julia A. Marakshina, Maria Teleńczuk, Bartosz Teleńczuk, Alain Destexhe, Paula T. Kuokkanen, Anna Kraemer, Thomas McColgan, Catherine E. Carr, and Richard Kempter

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurophysiology and neuropsychology, QP351-495
Published
2017
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5. Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator

Author

Joshua C. Crone, Manuel M. Vindiola, Alfred B. Yu, David L. Boothe, David Beeman, Kelvin S. Oie, and Piotr J. Franaszczuk

Subjects
large-scale simulation, spiking neuronal network, computational neuroscience, multi-compartment neuron model, high performance computing, multiscale modeling, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

In this paper, we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50–74 compartments per neuron. After making some modifications to the source code for GENESIS and its parallel implementation, PGENESIS, particularly to improve memory usage, we find that PGENESIS is able to efficiently scale on supercomputing resources to network sizes as large as 9 × 106 neurons with 18 × 109 synapses and 2.2 × 106 neurons with 45 × 109 synapses. The modifications to GENESIS that enabled these large scale simulations have been incorporated into the May 2019 Official Release of PGENESIS 2.4 available for download from the GENESIS web site (genesis-sim.org).

Published
2019
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6. Resonance Analysis as a Tool for Characterizing Functional Division of Layer 5 Pyramidal Neurons

Author

Melvin A. Felton Jr., Alfred B. Yu, David L. Boothe, Kelvin S. Oie, and Piotr J. Franaszczuk

Subjects
resonance, layer 5 pyramidal neuron, functional division, electrical coupling, association, ionic conductance, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Evidence suggests that layer 5 pyramidal neurons can be divided into functional zones with unique afferent connectivity and membrane characteristics that allow for post-synaptic integration of feedforward and feedback inputs. To assess the existence of these zones and their interaction, we characterized the resonance properties of a biophysically-realistic compartmental model of a neocortical layer 5 pyramidal neuron. Consistent with recently published theoretical and empirical findings, our model was configured to have a “hot zone” in distal apical dendrite and apical tuft where both high- and low-threshold Ca2+ ionic conductances had densities 1–2 orders of magnitude higher than anywhere else in the apical dendrite. We simulated injection of broad spectrum sinusoidal currents with linearly increasing frequency to calculate the input impedance of individual compartments, the transfer impedance between the soma and key compartments within the dendritic tree, and a dimensionless term we introduce called resonance quality. We show that input resonance analysis distinguished at least four distinct zones within the model based on properties of their frequency preferences: basal dendrite which displayed little resonance; soma/proximal apical dendrite which displayed resonance at 5–23 Hz, strongest at 5–10 Hz and hyperpolarized/resting membrane potentials; distal apical dendrite which displayed resonance at 8–19 Hz, strongest at 10 Hz and depolarized membrane potentials; and apical tuft which displayed a weak resonance largely between 8 and 10 Hz across a wide range of membrane potentials. Transfer resonance analysis revealed that changes in subthreshold electrical coupling were found to modulate the transfer resonant frequency of signals transmitted from distal apical dendrite and apical tuft to the soma, which would impact the frequencies that individual neurons are expected to respond to and reinforce. Furthermore, eliminating the hot zone was found to reduce amplification of resonance within the model, which contributes to reduced excitability when perisomatic and distal apical regions receive coincident stimulating current injections. These results indicate that the interactions between different functional zones should be considered in a more complete understanding of neuronal integration. Resonance analysis may therefore be a useful tool for assessing the integration of inputs across the entire neuronal membrane.

Published
2018
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7. Impact of Neuronal Membrane Damage on the Local Field Potential in a Large-Scale Simulation of Cerebral Cortex

Author

David L. Boothe, Alfred B. Yu, Pawel Kudela, William S. Anderson, Jean M. Vettel, and Piotr J. Franaszczuk

Subjects
local field potential, Cerebral Cortex, Modeling and simulation, traumatic brain injury, neuronal network, Neurology. Diseases of the nervous system, RC346-429
Abstract

Within multiscale brain dynamics, the structure–function relationship between cellular changes at a lower scale and coordinated oscillations at a higher scale is not well understood. This relationship may be particularly relevant for understanding functional impairments after a mild traumatic brain injury (mTBI) when current neuroimaging methods do not reveal morphological changes to the brain common in moderate to severe TBI such as diffuse axonal injury or gray matter lesions. Here, we created a physiology-based model of cerebral cortex using a publicly released modeling framework (GEneral NEural SImulation System) to explore the possibility that performance deficits characteristic of blast-induced mTBI may reflect dysfunctional, local network activity influenced by microscale neuronal damage at the cellular level. We operationalized microscale damage to neurons as the formation of pores on the neuronal membrane based on research using blast paradigms, and in our model, pores were simulated by a change in membrane conductance. We then tracked changes in simulated electrical activity. Our model contained 585 simulated neurons, comprised of 14 types of cortical and thalamic neurons each with its own compartmental morphology and electrophysiological properties. Comparing the functional activity of neurons before and after simulated damage, we found that simulated pores in the membrane reduced both action potential generation and local field potential (LFP) power in the 1–40 Hz range of the power spectrum. Furthermore, the location of damage modulated the strength of these effects: pore formation on simulated axons reduced LFP power more strongly than did pore formation on the soma and the dendrites. These results indicate that even small amounts of cellular damage can negatively impact functional activity of larger scale oscillations, and our findings suggest that multiscale modeling provides a promising avenue to elucidate these relationships.

Published
2017
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14. Systemic Oversimplification Limits the Potential for Human-AI Partnership

Author

David L. Boothe, Jason S. Metcalfe, Kaleb McDowell, and Brandon S. Perelman

Subjects
Sociotechnical system, General Computer Science, AI ecosystems, media_common.quotation_subject, task complexity, function allocation, 02 engineering and technology, sociotechnical systems, Human-AI partnership, Argument, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, General Materials Science, Sociology, Electrical and Electronic Engineering, Function (engineering), 050107 human factors, media_common, human-autonomy teaming, 05 social sciences, General Engineering, Certainty, TK1-9971, Interdependence, Task analysis, 020201 artificial intelligence & image processing, Engineering ethics, Electrical engineering. Electronics. Nuclear engineering, Global citizenship, Construct (philosophy)
Abstract

The modern world is evolving rapidly, especially with respect to the development and proliferation of increasingly intelligent, artificial intelligence (AI) and AI-related technologies. Nevertheless, in many ways, what this class of technologies has offered as return on investment remains less impressive than what has been promised. In the present paper, we argue that the continued failure to realize the potential in modern AI and AI-related technologies is largely attributable to the oversimplified, yet pervasive ways that our global society treats the relationship between these technologies and humans. Oversimplified concepts, once conveyed, tend to perpetuate myths that in turn limit the impact of such technologies in human society. To counter these oversimplifications, we offer a theoretical construct, which we call the landscape of human-AI partnership. This construct characterizes individual capability for real-world task performance as a dynamic function of information certainty, available time to respond, and task complexity. With this, our goal is to encourage more nuanced discourse about novel ways to solve challenges to modern and future sociotechnical societies, but without defaulting to notions that remain rooted in today’s technologies-as-tools ways of thinking. The core of our argument is that society at large must recognize that intelligent technologies are evolving well beyond being mere tools for human use and are instead becoming capable of operating as interdependent teammates. This means that how we think about interactions between humans and AI needs to go beyond a “Human–or–AI” conversation about task assignments to more contextualized “Human–and–AI” way of thinking about how best to capitalize on the strengths hidden within emergent capabilities of unique human-AI partnerships that have yet to be fully realized.

Published
2021
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17. Assessing the Impact of Ih Conductance on Cross-Frequency Coupling in Model Pyramidal Neurons

Author

David L. Boothe, Melvin Felton, Piotr J. Franaszczuk, Kelvin S. Oie, and Alfred B. Yu

Subjects
0301 basic medicine, phase-amplitude coupling, Materials science, Neuroscience (miscellaneous), Dendrite, cross-frequency coupling, lcsh:RC321-571, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Apical dendrite, Phase (matter), medicine, theta-gamma coupling, pyramidal neuron, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Membrane potential, Oscillation, Ih conductance, Conductance, Coupling (electronics), 030104 developmental biology, Amplitude, medicine.anatomical_structure, Biophysics, 030217 neurology & neurosurgery
Abstract

Large cortical and hippocampal pyramidal neurons are elements of neuronal circuitry that have been implicated in cross-frequency coupling (CFC) during cognitive tasks. We investigate potential mechanisms for CFC within these neurons by examining the role that the hyperpolarization-activated mixed cation current (Ih) plays in modulating CFC characteristics in multicompartment neuronal models. We quantify CFC along the soma-apical dendrite axis and tuft of three models configured to have different spatial distributions of Ih conductance density: (1) exponential gradient along the soma-apical dendrite axis, (2) uniform distribution, and (3) no Ih conductance. We simulated two current injection scenarios: distal apical 4 Hz modulation and perisomatic 4 Hz modulation, each with perisomatic, mid-apical, and distal apical 40 Hz injections. We used two metrics to quantify CFC strength-modulation index and height ratio-and we analyzed CFC phase properties. For all models, CFC was strongest in distal apical regions when the 40 Hz injection occurred near the soma and the 4 Hz modulation occurred in distal apical dendrite. The strongest CFC values were observed in the model with uniformly distributed Ih conductance density, but when the exponential gradient in Ih conductance density was added, CFC strength decreased by almost 50%. When Ih was in the model, regions with much larger membrane potential fluctuations at 4 Hz than at 40 Hz had stronger CFC. Excluding the Ih conductance from the model resulted in CFC either reduced or comparable in strength relative to the model with the exponential gradient in Ih conductance. The Ih conductance also imposed order on the phase characteristics of CFC such that minimum (maximum) amplitude 40 Hz membrane potential oscillations occurred during Ih conductance deactivation (activation). On the other hand, when there was no Ih conductance, phase relationships between minimum and maximum 40 Hz oscillation often inverted and occurred much closer together. This analysis can help experimentalists discriminate between CFC that originates from different underlying physiological mechanisms and can help illuminate the reasons why there are differences between CFC strength observed in different regions of the brain and between different populations of neurons based on the configuration of the Ih conductance.

Published
2020
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18. Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator

Author

Piotr J. Franaszczuk, David Beeman, David L. Boothe, Joshua C. Crone, Manuel M. Vindiola, Alfred B. Yu, and Kelvin S. Oie

Subjects
Source code, spiking neuronal network, Scale (ratio), Computer science, media_common.quotation_subject, Biomedical Engineering, Neuroscience (miscellaneous), Parallel computing, 050105 experimental psychology, lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, 0501 psychology and cognitive sciences, Technology and Code, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, multi-compartment neuron model, media_common, Web site, Computational neuroscience, Artificial neural network, large-scale simulation, 05 social sciences, Supercomputer, Multiscale modeling, multiscale modeling, Computer Science Applications, high performance computing, Benchmark (computing), 030217 neurology & neurosurgery, Neuroscience, computational neuroscience
Abstract

In this paper, we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50-74 compartments per neuron. After making some modifications to the source code for GENESIS and its parallel implementation, PGENESIS, particularly to improve memory usage, we find that PGENESIS is able to efficiently scale on supercomputing resources to network sizes as large as 9 × 106 neurons with 18 × 109 synapses and 2.2 × 106 neurons with 45 × 109 synapses. The modifications to GENESIS that enabled these large scale simulations have been incorporated into the May 2019 Official Release of PGENESIS 2.4 available for download from the GENESIS web site (genesis-sim.org).

Published
2019

19. Bayesian Topological Learning for Brain State Classification

Author

Christopher Oballe, David L. Boothe, Farzana Nasrin, and Vasileios Maroulas

Subjects
0301 basic medicine, FOS: Computer and information sciences, Computer Science - Machine Learning, Current (mathematics), Computer science, Bayesian probability, Machine Learning (stat.ML), Electroencephalography, Topology, Machine Learning (cs.LG), Methodology (stat.ME), 03 medical and health sciences, Naive Bayes classifier, 0302 clinical medicine, Statistics - Machine Learning, medicine, Statistics - Methodology, Persistent homology, medicine.diagnostic_test, Quantitative Biology::Neurons and Cognition, Bayes factor, Nonlinear system, 030104 developmental biology, ComputingMethodologies_PATTERNRECOGNITION, Topological data analysis, 030217 neurology & neurosurgery
Abstract

Investigation of human brain states through electroencephalograph (EEG) signals is a crucial step in human-machine communications. However, classifying and analyzing EEG signals are challenging due to their noisy, nonlinear and nonstationary nature. Current methodologies for analyzing these signals often fall short because they have several regularity assumptions baked in. This work provides an effective, flexible and noise-resilient scheme to analyze EEG by extracting pertinent information while abiding by the 3N (noisy, nonlinear and nonstationary) nature of data. We implement a topological tool, namely persistent homology, that tracks the evolution of topological features over time intervals and incorporates individual's expectations as prior knowledge by means of a Bayesian framework to compute posterior distributions. Relying on these posterior distributions, we apply Bayes factor classification to noisy EEG measurements. The performance of this Bayesian classification scheme is then compared with other existing methods for EEG signals., Comment: 7 pages and 4 figures

Published
2019
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20. Leveraging In Situ Data Analysis to Enable Computational Steering of Brain's Neocortex Simulations with GENESIS

Author

Michela Taufer, Sean McDaniel-Gray, David L. Boothe, Dale R. Shires, and Alfred B. Yu

Subjects
0301 basic medicine, 03 medical and health sciences, Computational model, 030104 developmental biology, 0302 clinical medicine, Computer engineering, Computer science, media_common.quotation_subject, Fidelity, Supercomputer, Computational steering, 030217 neurology & neurosurgery, media_common
Abstract

To investigate the origin and functional role of the brain's electrical activity in specific frequency ranges, scientists have employed biologically accurate computational models of the brain. The onset of extreme-scale computing brings the promise of allowing neuroscientists the ability to examine the brain in silico at unprecedented fidelity and resolution that is not possible using traditional methods such as the electroencephalogram. The large amount of data produced by high fidelity simulations are becoming increasingly difficult to save and transform into scientific insights for runtime simulations. One attractive solution is to modify the data analysis workflow from one that is accomplished post-simulation to one that is completed in situ at the cost of confining the analysis to the level of local data rather than a single global view. The missing global view of data has the potential to introduce inaccuracies in the simulation's findings. In this paper, we present the integration of in situ analysis into a simulation of the electrical activity of a brain's neuronal network to enable computational steering of the simulation itself at runtime (i.e., without stopping, performing post-simulation analysis, and restarting the simulation). We evaluate the accuracy of our in situ analysis versus the traditional post-simulation analysis, showing how we can gain meaningful global insights from local data. We demonstrate the integration's effectiveness in steering the electrical activity of a simulated neuronal network at runtime from electroencephalograph's (EEG) alpha frequency band (8-13Hz) to the beta frequency band (13-40Hz).

Published
2018
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21. Study of Neocortex Simulations with GENESIS on High Performance Computing Resources

Author

Joshua C. Crone, Dale R. Shires, Michela Taufer, David L. Boothe, Sean McDaniel, Alfred B. Yu, and Song J. Park

Subjects
0301 basic medicine, Theoretical computer science, Neocortex, Computer science, Supercomputer, Brain simulation, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, medicine.anatomical_structure, Encoding (memory), medicine, Differential (infinitesimal), 030217 neurology & neurosurgery
Abstract

One significant challenge in neuroscience is understanding the cooperative behavior of large numbers of neurons. Models of neuronal networks allow scientists to explore the impact of differential neuronal connectivity using analysis techniques and information not available experimentally. However, modeling realistic neurobiological processes and encoding them in computer simulations is challenging, as increasing computing and data requirements are all of concern. In this work we study the performance of neocortex simulations using GEneral NEural SImulation System (GENESIS), a well-known multi-function brain simulation package, supported by high performance computing resources. The contribution of our work is threefold. First, we study the impact of platforms (i.e., single fat nodes versus high-end clusters) and their features on the performance and data generation for a small scale model of neocortex. Second, we assess the impact of the model complexity (i.e., number of cells and cell connectivity)on the performance and data generation for increasingly large versions of the neocortex model on high-end clusters. Third, we provide selected scientific results obtained by increasing the model complexity. We show that the more realistic and rigorous modeling of neocortex functions is computationally feasible but requires high performance computing resources to mitigate the growing computing and data requests of the associated simulations.

Published
2016
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22. A model of limbed locomotion for a four muscle system

Author

Avis H. Cohen and David L. Boothe

Subjects
CpG site, Artificial Intelligence, Cognitive Neuroscience, Central pattern generator, Function (mathematics), Biology, Neuroscience, Coactivation, Computer Science Applications
Abstract

The spinal central pattern generator (CPG) for locomotion provides an output of proper timing and amplitude sufficient to create walking behavior. One important aspect of this output is the coactivation of antagonistic muscle groups. Two models are presented here, each of which models this coactivation. Each model contains a cell the function of which is to eliminate inhibition between the flexor and extensor neuronal groups.

Published
2002
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23. [Untitled]

Author

David L. Boothe and Avis H. Cohen

Subjects
Adaptive control, Computer science, business.industry, Feed forward, Central pattern generator, Sensory system, medicine.anatomical_structure, CpG site, Artificial Intelligence, Digital pattern generator, Motor system, medicine, Artificial intelligence, business, Neuroscience, Motor cortex
Abstract

Rhythmic movements in biological systems are produced in part by central circuits called central pattern generators (CPGs). For example, locomotion in vertebrates derives from the spinal CPG with activity initiated by the brain and controlled by sensory feedback. Sensory feedback is traditionally viewed as controlling CPGs cycle by cycle, with the brain commanding movements on a top down basis. We present an alternative view which in sensory feedback alters the properties of the CPG on a fast as well as a slow time scale. The CPG, in turn, provides feedforward filtering of the sensory feedback. This bidirectional interaction is widespread across animals, suggesting it is a common feature of motor systems, and, therefore, might offer a new way to view sensorimotor interactions in all systems including robotic systems. Bidirectional interactions are also apparent between the cerebral cortex and the CPG. The motor cortex doesn‘t simply command muscle contractions, but rather operates with the CPG to produce adaptively structured movements. To facilitate these adaptive interactions, the motor cortex receives feedback from the CPG that creates a temporal activity pattern mirroring the spinal motor output during locomotion. Thus, the activity of the motor cortical cells is shaped by the spinal pattern generator as they drive motor commands. These common features of CPG structure and function are suggested as offering a new perspective for building robotic systems. CPGs offer a potential for adaptive control, especially when combined with the principles of sensorimotor integration described here.

Published
1999
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24. Phase Locking Asymmetries at Flexor-Extensor Transitions during Fictive Locomotion

Author

Todd W. Troyer, David L. Boothe, and Avis H. Cohen

Subjects
Circuit Models, Phase (waves), lcsh:Medicine, Neurophysiology, Context (language use), Neural Homeostasis, Inhibitory postsynaptic potential, Phase locking, Midbrain, 03 medical and health sciences, 0302 clinical medicine, Molecular Cell Biology, Animals, Alternation (linguistics), Latency (engineering), lcsh:Science, Muscle, Skeletal, Biology, 030304 developmental biology, Physics, Neurons, Computational Neuroscience, Motor Systems, Motor Neurons, 0303 health sciences, Multidisciplinary, Applied Mathematics, lcsh:R, Central pattern generator, Computational Biology, musculoskeletal system, Spinal Cord, Nonlinear Dynamics, Cellular Neuroscience, Cats, lcsh:Q, Cellular Types, Neuroscience, 030217 neurology & neurosurgery, Mathematics, Locomotion, Research Article
Abstract

The motor output for walking is produced by a network of neurons termed the spinal central pattern generator (CPG) for locomotion. The basic building block of this CPG is a half-center oscillator composed of two mutually inhibitory sets of interneurons, each controlling one of the two dominant phases of locomotion: flexion and extension. To investigate symmetry between the two components of this oscillator, we analyzed the statistics of natural variation in timing during fictive locomotion induced by stimulation of the midbrain locomotor region in the cat. As a complement to previously published analysis of these data focused on burst and cycle durations, we present a new analysis examining the strength of phase locking at the transitions between flexion and extension. Across our sample of nerve pairs, phase locking at the transition from extension to flexion (E to F) is stronger than at the transition from flexion to extension (F to E). This pattern did not reverse when considering bouts of fictive locomotion that were flexor vs. extensor dominated, demonstrating that asymmetric locking at the transitions between phases is dissociable from which phase dominates cycle duration. We also find that the strength of phase locking is correlated with the mean latency between burst offset and burst onset. These results are interpreted in the context of a hypothesis where network inhibition and intrinsic oscillatory mechanisms make distinct contributions to flexor-extensor alternation in half-center networks.

Published
2013

25. Parameter dependent changes in strength of phase locking in a stochastic simulated central pattern generator

Author

Avis H. Cohen, David L Boothe, and Todd W. Troyer

Subjects
Computer science, General Neuroscience, lcsh:QP351-495, Central pattern generator, Function (mathematics), Inhibitory postsynaptic potential, Topology, Identity (music), Phase locking, lcsh:RC321-571, Cellular and Molecular Neuroscience, lcsh:Neurophysiology and neuropsychology, nervous system, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Simulation, Parameter dependent
Abstract

Walking behavior is produced by an oscillatory network of neurons termed the spinal central pattern generator for locomotion (sCPG). The basic building block of this sCPG is a half-center oscillator composed of two mutually inhibitory sets of interneurons each controlling one of the two dominant phases of the locomotor cycle: flexion and extension. The identity and function of neurons making up the sCPG are currently poorly characterized [1].

Published
2008

26. Temporal correlations in stochastic models of double bursting during simulated locomotion

Author

David L. Boothe, Todd W. Troyer, and Avis H. Cohen

Subjects
Communication, Physiology, Stochastic modelling, business.industry, General Neuroscience, Models, Neurological, Statistics as Topic, Theta model, Central pattern generator, Lampreys, Neural Inhibition, Efferent Pathways, Bursting, Neurons, Efferent, Spinal Cord, Biological Clocks, Alternation (formal language theory), Animals, Humans, Computer Simulation, Nerve Net, Biological system, business, Locomotion, Mathematics
Abstract

The output of the spinal central pattern generator for locomotion falls into two broad categories: alternation between antagonistic muscles and double bursting within muscles acting on multiple joints. We first model an alternating half-center and then present two different models of double bursting. The first double-bursting model consists of a central clock with an explicit one-to-one mapping between interneuron activity and model output. The second double-bursting model consists of a half-center with an added feedback neuron. Models are built using rate-coded leaky integrator neurons with slow self-inhibition. Structure-function relationships are explored by the addition of noise. The interaction of noise with the dynamics of each network creates a unique pattern of correlation between phases of the simulated cycle. The effects of noise can be explained by perturbation of deterministic versions of the networks. Three basic results were obtained: slow self-inhibitory currents lead to correlations between parts of the step cycle that are separated in time and network relative; model outputs are most sensitive to perturbations presented just before competitive switches in network activity, and clock-like models possess substantial symmetries within the correlation structure of burst durations, whereas the correlation structure of feedback models are asymmetric. Our models suggest that variability in burst length durations can be analyzed to make inferences about the structure of the spinal networks for locomotion. In particular, correlation patterns within double-bursting outputs may yield important clues regarding the interaction between more central, clock-like networks and feedback from more peripheral interneurons.

Published
2005

27. Temporal correlations in stochastic models of double bursting during simulated locomotion.

Author

David L Boothe, Avis H Cohen, and Todd W Troyer

Subjects
*TISSUES, *NEURONS, *NERVOUS system, *NEUROSCIENCES
Abstract

The output of the spinal central pattern generator for locomotion falls into two broad categories: alternation between antagonistic muscles and double bursting within muscles acting on multiple joints. We first model an alternating half-center and then present two different models of double bursting. The first double-bursting model consists of a central clock with an explicit one-to-one mapping between interneuron activity and model output. The second double-bursting model consists of a half-center with an added feedback neuron. Models are built using rate-coded leaky integrator neurons with slow self-inhibition. Structure-function relationships are explored by the addition of noise. The interaction of noise with the dynamics of each network creates a unique pattern of correlation between phases of the simulated cycle. The effects of noise can be explained by perturbation of deterministic versions of the networks. Three basic results were obtained: slow self-inhibitory currents lead to correlations between parts of the step cycle that are separated in time and network relative; model outputs are most sensitive to perturbations presented just before competitive switches in network activity, and clock-like models possess substantial symmetries within the correlation structure of burst durations, whereas the correlation structure of feedback models are asymmetric. Our models suggest that variability in burst length durations can be analyzed to make inferences about the structure of the spinal networks for locomotion. In particular, correlation patterns within double-bursting outputs may yield important clues regarding the interaction between more central, clock-like networks and feedback from more peripheral interneurons. [ABSTRACT FROM AUTHOR]

Published
2006
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28. Phase locking asymmetries at flexor-extensor transitions during fictive locomotion.

Author

David L Boothe, Avis H Cohen, and Todd W Troyer

Subjects
Medicine, Science
Abstract

The motor output for walking is produced by a network of neurons termed the spinal central pattern generator (CPG) for locomotion. The basic building block of this CPG is a half-center oscillator composed of two mutually inhibitory sets of interneurons, each controlling one of the two dominant phases of locomotion: flexion and extension. To investigate symmetry between the two components of this oscillator, we analyzed the statistics of natural variation in timing during fictive locomotion induced by stimulation of the midbrain locomotor region in the cat. As a complement to previously published analysis of these data focused on burst and cycle durations, we present a new analysis examining the strength of phase locking at the transitions between flexion and extension. Across our sample of nerve pairs, phase locking at the transition from extension to flexion (E to F) is stronger than at the transition from flexion to extension (F to E). This pattern did not reverse when considering bouts of fictive locomotion that were flexor vs. extensor dominated, demonstrating that asymmetric locking at the transitions between phases is dissociable from which phase dominates cycle duration. We also find that the strength of phase locking is correlated with the mean latency between burst offset and burst onset. These results are interpreted in the context of a hypothesis where network inhibition and intrinsic oscillatory mechanisms make distinct contributions to flexor-extensor alternation in half-center networks.

Published
2013
Full Text
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