Table of contentsA1 Functional advantages of cell-type heterogeneity in neural circuitsTatyana O. SharpeeA2 Mesoscopic modeling of propagating waves in visual cortexAlain DestexheA3 Dynamics and biomarkers of mental disordersMitsuo KawatoF1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneuronsVladislav Sekulić, Frances K. SkinnerF2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brainsDaniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán SomogyváriF3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir JosićO1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generatorsIrene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo VaronaO2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrainEunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun ChoiO3 Modeling auditory stream segregation, build-up and bistabilityJames Rankin, Pamela Osborn Popp, John RinzelO4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fieldsAlejandro Tabas, André Rupp, Emili Balaguer-BallesterO5 A simple model of retinal response to multi-electrode stimulationMatias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish MeffinO6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination taskVeronika Koren, Timm Lochmann, Valentin Dragoi, Klaus ObermayerO7 Input-location dependent gain modulation in cerebellar nucleus neuronsMaria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker SteuberO8 Analytic solution of cable energy function for cortical axons and dendritesHuiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo YuO9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal networkJimin Kim, Will Leahy, Eli ShlizermanO10 Is the model any good? Objective criteria for computational neuroscience model selectionJustas Birgiolas, Richard C. Gerkin, Sharon M. CrookO11 Cooperation and competition of gamma oscillation mechanismsAtthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan GielenO12 A discrete structure of the brain wavesYuri Dabaghian, Justin DeVito, Luca PerottiO13 Direction-specific silencing of the Drosophila gaze stabilization systemAnmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby MaimonO14 What does the fruit fly think about values? A model of olfactory associative learningChang Zhao, Yves Widmer, Simon Sprecher,Walter SennO15 Effects of ionic diffusion on power spectra of local field potentials (LFP)Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen...

Understanding how animals update their decision-making behavior over time is an important problem in neuroscience. Decision-making strategies evolve over the course of learning, and continue to vary even in well-trained animals. However, the standard suite of behavioral analysis tools is ill-equipped to capture the dynamics of these strategies. Here, we present a flexible method for characterizing time-varying behavior during decision-making experiments. We show that it successfully captures trial-to-trial changes in an animal's sensitivity to not only task-relevant stimuli, but also task-irrelevant covariates such as choice, reward, and stimulus history. We use this method to derive insights from training data collected in mice, rats, and human subjects performing auditory discrimination and visual detection tasks. With this approach, we uncover the detailed evolution of an animal's strategy during learning, including adaptation to time-varying task statistics, suppression of sub-optimal strategies, and shared behavioral dynamics between subjects within an experimental population. 45 et al., 2009). Other work from Kattner et al. (2017) extended the standard psychometric curve to allow its parameters to vary continuously across trials. Bak et al. (2016) described a model for defining smoothly evolving weights that could track changing sensitivities to specific behavioral covariates. While the model could track behavioral dynamics in theory, the optimization procedure strongly constrained both the complexity of the model and the size of the data to which it could 50 65 unprecedented insight into the development of behavioral strategies. ResultsOur primary contribution is a method for characterizing the evolution of animal decision-making behavior on a trial-to-trial basis. Our approach consists of a dynamic Bernoulli generalized linear model (GLM), defined by a set of smoothly evolving psychophysical weights. These weights 70 characterize the animal's decision-making strategy at each trial in terms of a linear combination of available task variables. The larger the magnitude of a particular weight, the more the animal's decision relies on the corresponding task variable. Learning to perform a new task therefore involves driving the weights on "relevant" variables (e.g., sensory stimuli) to large values, while driving weights on irrelevant variables (e.g., bias, choice history) toward zero. However, classical 75 modeling approaches require that weights remain constant over long blocks of trials, which precludes tracking of trial-to-trial behavioral changes that arise during learning and in non-stationary task environments. Below, we describe our modeling approach in more detail. Dynamic Psychophysical Model for Decision-Making TasksAlthough our method is applicable to any binary decision-making task, for concreteness we intro-80 duce our method in the context of the task used by the International Brain Lab (IBL) (illustrated in Figure 1A) (IBL et al., 2020). In this visual detection task, a mouse is positioned in f...

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Anaerobic archaea M. acetivorans, a versatile salt-water adapted methanogen, is capable of converting seven different organic molecules into methane. Utilization of these substrates involves three main reaction pathways that are differentially regulated by the organism depending on growth conditions. We have developed a kinetic model for methanogenesis using RNA-seq data, single molecule enumeration of protein abundance (SiMPull) and kinetic parameters from literature the. RNA-seq data generated on different growth conditions was also used to create a transcriptional regulatory model. Stochasticity in gene expression is known to create heterogeneity in an isogenic population. Such heterogeneity in transcriptional regulators, which are known to be present in small copy numbers, can effect formation of different cell phenotypes allowing cells in a colony to utilize different energy sources. Integrating the kinetic model of methanogenesis with a transcriptional regulation model results in a most comprehensive model for methanogenesis to date. We simulate this model using reaction-diffusion master equation (RDME) based Lattice Microbes software package to simulate monoclonal cells and study the variability in pathway and substrate usage under environmental and industrial conditions. Simulated behaviors allow identification and analysis of phenotypes that arise and the resulting sensitivity of methanogenesis to protein copy number, substrate availability and transcriptional regulation noise. These results lay the groundwork necessary for studying the individual behaviors to ultimately simulate a colony of methanogens sharing space and resources.