25th Annual Computational Neuroscience Meeting: CNS-2016
Bibliographic record
Abstract
Table of contents\n A1 Functional advantages of cell-type heterogeneity in neural circuits\n Tatyana O. Sharpee\n A2 Mesoscopic modeling of propagating waves in visual cortex\n Alain Destexhe\n A3 Dynamics and biomarkers of mental disorders\n Mitsuo Kawato\n F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons\n Vladislav Sekulić, Frances K. Skinner\n F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains\n Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári\n F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.\n Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić\n O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators\n Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona\n O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain\n Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi\n O3 Modeling auditory stream segregation, build-up and bistability\n James Rankin, Pamela Osborn Popp, John Rinzel\n O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields\n Alejandro Tabas, André Rupp, Emili Balaguer-Ballester\n O5 A simple model of retinal response to multi-electrode stimulation\n Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin\n O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task\n Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer\n O7 Input-location dependent gain modulation in cerebellar nucleus neurons\n Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber\n O8 Analytic solution of cable energy function for cortical axons and dendrites\n Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu\n O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network\n Jimin Kim, Will Leahy, Eli Shlizerman\n O10 Is the model any good? Objective criteria for computational neuroscience model selection\n Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook\n O11 Cooperation and competition of gamma oscillation mechanisms\n Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen\n O12 A discrete structure of the brain waves\n Yuri Dabaghian, Justin DeVito, Luca Perotti\n O13 Direction-specific silencing of the Drosophila gaze stabilization system\n Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon\n O14 What does the fruit fly think about values? A model of olfactory associative learning\n Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn\n O15 Effects of ionic diffusion on power spectra of local field potentials (LFP)\n Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll\n O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits\n Yasunori Yamada\n O17 Spatial coarse-graining the brain: origin of minicolumns\n Moira L. Steyn-Ross, D. Alistair Steyn-Ross\n O18 Modeling large-scale cortical networks with laminar structure\n Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang\n O19 Information filtering by partial synchronous spikes in a neural population\n Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner\n O20 Decoding context-dependent olfactory valence in Drosophila\n \n Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama\n P1 Neural network as a scale-free network: the role of a hub\n B. Kahng\n P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging\n Nicoladie D. Tam\n P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique\n Nicoladie D.Tam, Luca Pollonini, George Zouridakis\n P4 Modeling jamming avoidance of weakly electric fish\n Jaehyun Soh, DaeEun Kim\n P5 Synergy and redundancy of retinal ganglion cells in prediction\n Minsu Yoo, S. E. Palmer\n P6 A neural field model with a third dimension representing cortical depth\n Viviana Culmone, Ingo Bojak\n P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord\n Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk\n P8 The recognition dynamics in the brain\n Chang Sub Kim\n P9 Multivariate spike train analysis using a positive definite kernel\n Taro Tezuka\n P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia\n Pangyu Joo\n P11 The ionic basis of heterogeneity affects stochastic synchrony\n Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban\n P12 Circular statistics of noise in spike trains with a periodic component\n Petr Marsalek\n P14 Representations of directions in EEG-BCI using Gaussian readouts\n Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong\n P15 Action selection and reinforcement learning in basal ganglia during reaching movements\n Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak\n P17 Axon guidance: modeling axonal growth in T-Junction assay\n Csaba Forro, Harald Dermutz, László Demkó, János Vörös\n P19 Transient cell assembly networks encode persistent spatial memories\n Yuri Dabaghian, Andrey Babichev\n P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons\n Haiping Huang\n P21 Design of biologically-realistic simulations for motor control\n Sergio Verduzco-Flores\n P22 Towards understanding the functional impact of the behavioural variability of neurons\n Filipa Dos Santos, Peter Andras\n P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia\n Christoph Metzner, Achim Schweikard, Bartosz Zurowski\n P24 Memory recall and spike frequency adaptation\n James P. Roach, Leonard M. Sander, Michal R. Zochowski\n P25 Stability of neural networks and memory consolidation preferentially occur near criticality\n Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski\n P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems\n Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou\n P27 Neurofield: a C++ library for fast simulation of 2D neural field models\n Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao\n P28 Action-based grounding: Beyond encoding/decoding in neural code\n Yoonsuck Choe, Huei-Fang Yang\n P29 Neural computation in a dynamical system with multiple time scales\n Yuanyuan Mi, Xiaohan Lin, Si Wu\n P30 Maximum entropy models for 3D layouts of orientation selectivity\n Joscha Liedtke, Manuel Schottdorf, Fred Wolf\n P31 A behavioral assay for probing computations underlying curiosity in rodents\n Yoriko Yamamura, Jeffery R. Wickens\n P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models\n Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver\n P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm\n Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang\n P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis\n Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon\n P35 Dynamics of cooperative excitatory and inhibitory plasticity\n Everton J. Agnes, Tim P. Vogels\n P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons\n William F. Podlaski, Tim P. Vogels\n P37 Phenomenological neural model for adaptation of neurons in area IT\n Martin Giese, Pradeep Kuravi, Rufin Vogels\n P38 I
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.173 | 0.110 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".