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2. 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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3. Conditions for wave trains in spiking neural networks

Author

Johanna Senk, Karolína Korvasová, Jannis Schuecker, Espen Hagen, Tom Tetzlaff, Markus Diesmann, and Moritz Helias

Subjects
Physics, QC1-999
Abstract

Spatiotemporal patterns such as traveling waves are frequently observed in recordings of neural activity. The mechanisms underlying the generation of such patterns are largely unknown. Previous studies have investigated the existence and uniqueness of different types of waves or bumps of activity using neural-field models, phenomenological coarse-grained descriptions of neural-network dynamics. But it remains unclear how these insights can be transferred to more biologically realistic networks of spiking neurons, where individual neurons fire irregularly. Here, we employ mean-field theory to reduce a microscopic model of leaky integrate-and-fire (LIF) neurons with distance-dependent connectivity to an effective neural-field model. In contrast to existing phenomenological descriptions, the dynamics in this neural-field model depends on the mean and the variance in the synaptic input, both determining the amplitude and the temporal structure of the resulting effective coupling kernel. For the neural-field model we employ linear stability analysis to derive conditions for the existence of spatial and temporal oscillations and wave trains, that is, temporally and spatially periodic traveling waves. We first prove that wave trains cannot occur in a single homogeneous population of neurons, irrespective of the form of distance dependence of the connection probability. Compatible with the architecture of cortical neural networks, wave trains emerge in two-population networks of excitatory and inhibitory neurons as a combination of delay-induced temporal oscillations and spatial oscillations due to distance-dependent connectivity profiles. Finally, we demonstrate quantitative agreement between predictions of the analytically tractable neural-field model and numerical simulations of both networks of nonlinear rate-based units and networks of LIF neurons.

Published
2020
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4. Locomotion induced by medial septal glutamatergic neurons is linked to intrinsically generated persistent firing

Author

Ludwig F, Mikulovic S, Remy S, Tom Tetzlaff, Hiroshi Kaneko, Sosulina L, and Karolína Korvasová

Subjects
Glutamatergic, Oscillation (cell signaling), Premovement neuronal activity, Optogenetics, Biology, Neurotransmission, Stimulus (physiology), Locomotor activity, Neuroscience, Theta oscillations
Abstract

Medial septal glutamatergic neurons are active during theta oscillations and locomotor activity. Prolonged optogenetic activation of medial septal glutamatergic neurons drives theta oscillations and locomotion for extended periods of time outlasting the stimulus duration. However, the cellular and circuit mechanisms supporting the maintenance of both theta oscillations and locomotion remain elusive. Specifically, it remains unclear whether the presence of theta oscillations is a necessary prerequisite for locomotion, and whether neuronal activity within the medial septum underlies its persistence. Here we show that a persistent theta oscillation can be induced by a brief transient activation of glutamatergic neurons. Moreover, persistent locomotion is initiated even if the theta oscillation is abolished by blocking synaptic transmission in the medial septum. We observe persistent spiking of medial septal neurons that outlasts the stimulus for several seconds, both in vivo and in vitro. This persistent activity is driven by intrinsic excitability of glutamatergic neurons.

Published
2021
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5. ROS-MUSIC Toolchain for Spiking Neural Network Simulations in a Robotic Environment

Author

Jenia Jitsev, Abigail Morrison, Philipp Weidel, Renato Duarte, and Karolína Korvasová

Subjects
Spiking neural network, Artificial neural network, Quantitative Biology::Neurons and Cognition, business.industry, Computer science, General Neuroscience, Interface (computing), Toolchain, Computer Science::Robotics, Cellular and Molecular Neuroscience, Middleware, Poster Presentation, Robot, Reinforcement learning, Artificial intelligence, business, Nervous system network models
Abstract

Studying a functional, biologically plausible neural network that performs a particular task is highly relevant for progress in both neuroscience and machine learning. Most tasks used to test the function of a simulated neural network are still very artificial and thus too narrow, providing only little insight into the true value of a particular neural network architecture under study. For example, many models of reinforcement learning in the brain rely on a discrete set of environmental states and actions [1]. In order to move closer towards more realistic models, modeling studies have to be conducted in more realistic environments that provide complex sensory input about the states. A way to achieve this is to provide an interface between a robotic and a neural network simulation, such that a neural network controller gains access to a realistic agent which is acting in a complex environment that can be flexibly designed by the experimentalist. To create such an interface, we present a toolchain, consisting of already existing and robust tools, which forms the missing link between robotic and neuroscience with the goal of connecting robotic simulators with neural simulators. This toolchain is a generic solution and is able to combine various robotic simulators with various neural simulators by connecting the Robot Operating System (ROS) [2] with the Multi-Simulation Coordinator (MUSIC) [3]. ROS is the most widely used middleware in the robotic community with interfaces for robotic simulators like Gazebo, Morse, Webots, etc, and additionally allows the users to specify their own robot and sensors in great detail with the Unified Robot Description Language (URDF). MUSIC is a communicator between the major, state-of-the-art neural simulators: NEST, Moose and NEURON. By implementing an interface between ROS and MUSIC, our toolchain is combining two powerful middlewares, and is therefore a multi-purpose generic solution. One main purpose is the translation from continuous sensory data, obtained from the sensors of a virtual robot, to spiking data which is passed to a neural simulator of choice. The translation from continuous data to spiking data is performed using the Neural Engineering Framework (NEF) proposed by Eliasmith & Anderson [4]. By sending motor commands from the neural simulator back to the robotic simulator, the interface is forming a closed loop between the virtual robot and its spiking neural network controller. To demonstrate the functionality of the toolchain and the interplay between all its different components, we implemented one of the vehicles described by Braitenberg [5] using the robotic simulator Gazebo and the neural simulator NEST. In future work, we aim to create a testbench, consisting of various environments for reinforcement learning algorithms, to provide a validation tool for the functionality of biological motivated models of learning.

Published
2020
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6. Linearization of solution operators for state-dependent delay equations: A simple example

Author

Odo Diekmann and Karolína Korvasová

Subjects
education.field_of_study, Applied Mathematics, Population, Mathematical analysis, Stability (learning theory), State dependent, Simple (abstract algebra), Linearization, Discrete Mathematics and Combinatorics, Renewal equation, Differentiable function, Feedback linearization, education, Analysis, Mathematics
Abstract

For state-dependent delay equations, it may easily happen that the equation is not differentiable. This hampers the formulation and the proof of the Principle of Linearized Stability. The fact that an equation is not differentiable does not, by itself, imply that the solution operators are not differentiable. And indeed, the aim of this paper is to present a simple example with differentiable solution operators despite of lack of differentiability of the equation. The example takes the form of a renewal equation and is motivated by a population dynamical model.

Published
2015
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7. Investigating the Turing conditions for diffusion-driven instability in the presence of a binding immobile substrate

Author

Eamonn A. Gaffney, Karolína Korvasová, Václav Klika, Marta Araújo Tavares Ferreira, and Philip K. Maini

Subjects
Statistics and Probability, Physics, General Immunology and Microbiology, Applied Mathematics, Diffusion, Pattern formation, Numerical Analysis, Computer-Assisted, General Medicine, Models, Biological, Instability, General Biochemistry, Genetics and Molecular Biology, Substrate Specificity, Kinetics, Matrix (mathematics), Chemical physics, Modeling and Simulation, Pairing, Bound state, Relaxation (physics), General Agricultural and Biological Sciences, Turing, computer, computer.programming_language
Abstract

Turing's diffusion-driven instability for the standard two species reaction-diffusion system is only achievable under well-known and rather restrictive conditions on both the diffusion rates and the kinetic parameters, which necessitates the pairing of a self-activator with a self-inhibitor. In this study we generalize the standard two-species model by considering the case where the reactants can bind to an immobile substrate, for instance extra-cellular matrix, and investigate the influence of this dynamics on Turing's diffusion-driven instability. Such systems have been previously studied on the grounds that binding of the self-activator to a substrate may effectively reduce its diffusion rate and thus induce a Turing instability for species with equal diffusion coefficients, as originally demonstrated by Lengyel and Epstein (1992) under the assumption that the bound state dynamics occurs on a fast timescale. We, however, analyse the full system without any separation of timescales and demonstrate that the full system also allows a relaxation of the standard constraints on the reaction kinetics for the Turing instability, increasing the type of interactions that could give rise to spatial patterning. In particular, we show that two self-activators can undertake a diffusively driven instability in the presence of a binding immobile substrate, highlighting that the interactions required of a putative biological Turing instability need not be associated with a self-activator-self-inhibitor morphogen pair.

Published
2015
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8. Collective motion of dimers

Author

Karolína Korvasová, Kerry A. Landman, Catherine J. Penington, and Barry D. Hughes

Subjects
Physics, education.field_of_study, Partial differential equation, Dimer, Population, Collective motion, Cell Communication, Models, Biological, Square lattice, chemistry.chemical_compound, chemistry, Cell Movement, Lattice (order), Master equation, Animals, Humans, Computer Simulation, Statistical physics, Discrete event simulation, education
Abstract

We consider a discrete agent-based model on a one-dimensional lattice and a two-dimensional square lattice, where each agent is a dimer occupying two sites. Agents move by vacating one occupied site in favor of a nearest-neighbor site and obey either a strict simple exclusion rule or a weaker constraint that permits partial overlaps between dimers. Using indicator variables and careful probability arguments, a discrete-time master equation for these processes is derived systematically within a mean-field approximation. In the continuum limit, nonlinear diffusion equations that describe the average agent occupancy of the dimer population are obtained. In addition, we show that multiple species of interacting subpopulations give rise to advection-diffusion equations. Averaged discrete simulation data compares very well with the solution to the continuum partial differential equation models. Since many cell types are elongated rather than circular, this work offers insight into population-level behavior of collective cellular motion.

Published
2012
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