CNS 2018 Seattle: Tutorials 

Program for Friday 13 July

Tutorials are intended as introductions into main methodologies of various fields in computational neuroscience. This year, CNS tutorials offer introductory full day courses on cellular and network level modelling as well as specialized half day tutorials. Tutorials are particularly tailored for early stage researchers as well as researchers entering a new field in computational neuroscience.

For inquiries related to these workshops, please contact the tutorials organizer: Please note that the program is not final.

Whole day tutorials

Allen Institute Brain Observatory and Brain Modeling Toolkit tutorial Dr. Yazan Billeh, Dr. Sergey Gratiy and Dr. Saskia de Vries (Allen Institute, Seattle, USA) T1
Multiscale modeling from molecular level to large network level (using NEURON, RxD and NetPyNE) Dr. Salvador Dura-Bernal (SUNY Downstate, USA), Dr. Robert McDougal (Yale University, USA) and Dr. William Lytton (SUNY Downstate, USA) T2
Simulation of large-scale neural networks (using NEST) Dr. Sacha J. van Albada and Philipp Weidel (Jülich Research Centre and JARA, Jülich, Germany) T3


Half-day tutorials

Neuroinformatics resources for computational modelers Dr. Padraig Gleeson (University College London, UK) T4
Modeling and analysis of extracellular potentials Dr. Gaute T. Einevoll (Norwegian University of Life Sciences & University of Oslo, Norway) and Dr. Espen Hagen (Dept. of Physics, University of Oslo, Norway) T5
Single cell RNA-seq analysis for transcriptomic type characterization Dr. Zizhen Yao and Dr. Lucas Graybuck (Allen Institute, Seattle, USA) T6


T1: Allen Institute Brain Observatory and Brain Modeling Toolkit tutorial

  • Dr. Yazan Billeh (Allen Institute, Seattle, USA)
  • Dr. Sergey Gratiy (Allen Institute, Seattle, USA)
  • Dr. Saskia de Vries (Allen Institute, Seattle, USA) 

Description of the tutorial

The first part of the tutorial will introduce the Allen Brain Observatory, an open dataset of neural activity recorded in the visual cortex of the awake mouse. Collected using a standardized 2-photon calcium imaging pipeline, this dataset contains recordings in response to a standard set of visual stimuli from ~40,000 neurons in ~200 experiments, spanning 6 cortical areas, 3 cortical layers, and 6 excitatory Cre-defined cell populations. This tutorial will introduce the scientific context for this pipelined dataset, and demonstrate how to download and access this data using the Allen Software Development Kits (Allen SDK). Working in a Python environment, participants will be led through example analyses of both single cell and population level sensory coding.

The second part of the tutorial will introduce the Brain Modeling ToolKit (BMTK). BMTK is a Python-based software package for building and simulating models of neuronal circuits. It supports simulations at four levels of resolution (biophysically detailed, point-neuron, population statistics, and machine intelligence) by providing wrappers to tools such as NEURON, NEST, diPDE, and TensorFlow. This tutorial will give an overview of BMTK and work through two examples to demonstrate how to build and run networks at different levels of granularity.

This tutorial requires a basic level of Python proficiency and using Python scientific packages such as numpy and pandas.

 Software tools


T2: Multiscale modeling from molecular level to large network level (using NEURON, RxD and NetPyNE)

  • Dr. Salvador Dura-Bernal (SUNY Downstate, USA)
  • Dr. Robert McDougal (Yale University)
  • Dr. William Lytton (SUNY Downstate)

Description of the tutorial

Understanding brain function requires characterizing the interactions occurring across many temporal and spatial scales. Mechanistic multiscale modeling aims to organize and explore these interactions to determine how dynamics at one scale alter or are associated with dynamics at other scales. In this way, multiscale models provide insights into how changes at molecular and cellular levels, caused by development, learning, brain disease, drugs, or other factors, affect the dynamics of local networks and of brain areas. Large neuroscience data-gathering projects throughout the world (e.g. US BRAIN, EU HBP, Allen Institute) are making use of these tools – including the NEURON multiscale simulator – to better understand the vast amounts of information being gathered using many different techniques at different scales [1, 2].

This tutorial will present recent multiscale modeling tool development in the NEURON simulator [3], with an emphasis on reaction diffusion intracellular and extracellular modeling (chemophysiology complementing electrophysiology) and simulation of large biophysically detailed networks. The morning session will introduce 1) the basics of single cell modeling using the NEURON simulator and 2) NEURON's Reaction-Diffusion (RxD) module [4, 5]. RxD provides specification and simulation for molecular scale dynamics (genomics, proteomics, signaling cascades and reaction dynamics) coupled with the electrophysiological dynamics of the cell membrane. The afternoon session will introduce 1) basic network modeling in NEURON [6, 7], and 2) NetPyNE, a high-level Python interface (programmatic and GUI-based) to NEURON that facilitates the development, parallel simulation, and analysis of biological neuronal networks [8, 9, 10]. To finish, we will show an example of combining both tools to explore the effects of molecular-level dynamics in a large network.

Background reading and software tools

  • [1] Markram H et al. (2015) Reconstruction and simulation of neocortical microcircuitry. Cell 163:456–492
  • [2] Hawrylycz M, Anastassiou C, Arkhipov A, Berg J, Buice M, Cain N, Gouwens NW, Gratiy S, et al. (2016) Inferring cortical function in the mouse visual system through large-scale systems neuroscience. PNAS, 113(27):7337–7344
  • [3] NEURON:
  • [4] McDougal R, Hines M, Lytton W (2013) Reaction-diffusion in the NEURON simulator. Front. Neuroinform. 7:28.
  • [5] RxD:
  • [6] Migliore M, Cannia C, Lytton WW, Markram H and Hines ML (2006) Parallel network simulations with NEURON. Journal of Computational Neuroscience 21:119-129
  • [7] Lytton WW, Seidenstein AH, Dura-Bernal S, McDougal RA, Schürmann F, Hines ML (2016) Simulation neurotechnologies for advancing brain research: parallelizing large networks in NEURON. Neural Comput. 28:2063–2090
  • [8] NetPyNE:
  • [9] NetPyNE-UI:
  • [10] Dura-Bernal S, Neymotin SA, Suter BA, Shepherd G, Lytton WW (2018) Long-range inputs and H-current regulate different modes of operation in a multiscale model of mouse M1 microcircuits. bioRxiv 201707


T3: Simulation of large-scale neural networks (using NEST)

  • Dr. Sacha J. van Albada (Jülich Research Centre and JARA, Jülich, Germany)
  • Philipp Weidel (Jülich Research Centre and JARA, Jülich, Germany)

Description of the tutorial

This tutorial starts with an introduction to large-scale neuronal networks, giving examples of existing models and identifying some challenges these networks pose for modeling and simulation. This is followed by an introduction to the NEural Simulation Tool (NEST [1]), shedding light on its design principles, which address challenges for large-scale simulations. An overview of the features of NEST is provided, also touching upon advanced properties of neuronal networks like gap-junctions [2]. To familiarize participants with the basic usage of NEST, some simple networks are programmed in hands-on exercises. Next, the tutorial explains how NEST enables parallel simulations via both distributed and threaded computations. Threaded simulations are demonstrated on a cortical microcircuit model [3]. Finally, the tutorial provides an introduction to the NEST Modeling Language (NESTML [4]). In this final hands-on part of the tutorial, the participants learn how to create neuron models in NEST using NESTML.

The tutorial does not assume any prior knowledge of NEST. However, it is recommended that participants install NEST on their laptops beforehand [5]. Furthermore, it is recommended to have VirtualBox installed and to have at least 4 GB of free disk space available.

References and background reading

  • [1] Kunkel S, Morrison A, Weidel P, Eppler JM, Sinha A, Schenck W, … Plesser HE (2017). NEST 2.12.0. Zenodo.
  • [2] Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A and Diesmann M (2015) A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations Front. Neuroinform. 9:22
  • [3] Potjans TC, Diesmann M (2014) The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cereb. Cortex. 24(3):785–806.
  • [4] Plotnikov D, Rumpe B, Blundell I, Ippen T, Eppler JM and Morrison A (2016) NESTML: a modeling language for spiking neurons. arXiv:1606.02882.
  • [5]


T4: Neuroinformatics resources for computational modelers

  • Dr. Padraig Gleeson (University College London, UK)

Description of the tutorial

Neuroinformatics resources are becoming an essential part of computational investigations in neuroscience. A movement towards making data and software freely available to the community means that more and more experimental datasets, general purpose analysis tools and infrastructure for computational modelling and simulation are available for computational neuroscientists to help build, constrain and validate their models.

This tutorial will give an overview of the range of neuroinformatics resources currently available to the community. The first half will give a brief introduction to a number of these under the headings; Experimental datasets; Structured data from literature; Analysis tools; Simulation environments; Model sharing; Computing infrastructure; Open source initiatives. The second half of the tutorial will involve hands on exercises where multiple resource will be accessed, data transformed and analysed and new models executed. Note that this tutorial will focus on neuroinformatics resources for cell and network modelling, and not cover the wide range of neuroimaging or genetics databases.

Tutorial content

Open source at:


T5: Modeling and analysis of extracellular potentials

  • Dr. Gaute T. Einevoll (Norwegian University of Life Sciences & University of Oslo, Norway)
  • Dr. Espen Hagen (Dept. of Physics, University of Oslo, Norway)


Description of the tutorial:

While extracellular electrical recordings have been one of the main workhorses in electrophysiology, the interpretation of such recordings is not trivial [1, 2, 3], as the measured signals result of both local and remote neuronal activity. The recorded extracellular potentials in general stem from a complicated sum of contributions from all transmembrane currents of the neurons in the vicinity of the electrode contact. The duration of spikes, the extracellular signatures of neuronal action potentials, is so short that the high-frequency part of the recorded signal, the multi-unit activity (MUA), often can be sorted into spiking contributions from the individual neurons surrounding the electrode [4]. No such simplifying feature aids us in the interpretation of the low-frequency part, the local field potential (LFP). To take a full advantage of the new generation of silicon-based multielectrodes recording from tens, hundreds or thousands of positions simultaneously, we thus need to develop new data analysis methods and models grounded in the biophysics of extracellular potentials [1, 3, 4]. This is the topic of the present tutorial.

In the tutorial we will go through

  • the biophysics of extracellular recordings in the brain,

  • a scheme for biophysically detailed modeling of extracellular potentials and the application to modeling single spikes [5–7], MUAs [8] and LFPs, both from single neurons [9] and populations of neurons [8, 10–12],

  • LFPy ( [13], a versatile tool based on Python and the NEURON simulation environment [14] ( for calculation of extracellular potentials around neurons and networks of neurons, as well as corresponding electroencephalography (EEG) and magnetoencephalography (MEG) signals.

References and background reading

  • [1] KH Pettersen et al., “Extracellular spikes and CSD” in Handbook of Neural Activity Measurement, Cambridge (2012)
  • [2] G Buzsaki et al., Nat Rev Neurosci 13:407 (2012)
  • [3] GT Einevoll et al., Nat Rev Neurosci 14:770 (2013)
  • [4] GT Einevoll et al., Curr Op Neurobiol 22:11 (2012)
  • [5] G Holt, C Koch, J Comp Neurosci 6:169 (1999)
  • [6] J Gold et al., J Neurophysiol 95:3113 (2006)
  • [7] KH Pettersen and GT Einevoll, Biophys J 94:784 (2008)
  • [8] KH Pettersen et al., J Comp Neurosci 24:291 (2008)
  • [9] H Lindén et al., J Comp Neurosci 29: 423 (2010)
  • [10] H Lindén et al., Neuron 72:859 (2011)
  • [11] S Łęski et al., PLoS Comp Biol 9:e1003137 (2013)
  • [12] E Hagen et al., Cereb Cortex 26:4461 (2016)
  • [13] H Lindén et al., Front Neuroinf 7:41 (2014)
  • [14] ML Hines et al., Front Neuroinf 3:1 (2009)


T6: Single cell RNA-seq analysis for transcriptomic type characterization

  • Dr. Zizhen Yao (Allen Institute, Seattle, USA)
  • Dr. Lucas Graybuck (Allen Institute, Seattle, USA)

Description of the tutorial

The functional interplay of neural cell types gives rise to the complex, emergent function of neural tissues. To fully understand the biology of the brain, we need to be able to distinguish and describe these cell types, and identify markers that can be used to selectively label cell types for further study [1]. One scalable and comprehensive method for identifying cell types in the brain is single cell RNA-sequencing. High-quality and large scale scRNA-seq datasets provide data about the expression of thousands of genes from thousands of individual cells. With this starting point, we can perform clustering analyses to identify the cell types of mouse and human brains.

In the first half of this tutorial, we will first give an introduction of single cell RNA-seq technology, with an overview of multiple single cell studies in CNS, and commonly used computational tools. Then, we will focus on the recent comprehensive survey of mouse cortical cell types conducted by the Allen Institute for Brain Science, and give a summary of what we have learned about cell types in this study.

In the second half of the tutorial, we will introduce the single cell analysis tools we have developed at the Allen Institute for Brain Science. To enable users to apply our analysis methods to their own datasets, we have developed the scrattch suite for R, which includes scrattch.iterclust (iterative clustering methods), scrattch.vis (data visualization methods), and (file and format handling). In the tutorial, we’ll demonstrate how these packages can be used to cluster scRNA-seq data generated for 1,679 cells from Tasic, et al. 2016. Nat. Neurosci [2].

Suggested reading

  • [1] Poulin JF, Tasic B, Hjerling-Leffler J, Trimarchi JM, Awatramani R. Disentangling neural cell diversity using single-cell transcriptomics. Nat Neurosci. 2016;19(9):1131-1141.
  • [2] Tasic B, Venon M, et al. Adult Mouse Cortical Cell Taxonomy by Single Cell Transcriptomics. Nat Neurosci. 2016; 19(2): 335-346.
  • [3] Tasic B, Yao Z, et al. Shared and distinct transcriptomic cell types across neocortical areas.
  • [4] Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202-1214.