CNS 2017 Antwerp: Tutorials
Preliminary Program:
Whole day tutorials
  • T1: Subcellular modeling organized by Drs. Andrew Gallimore and Weiliang Chen (Okinawa Institute of Science and Technology, Japan)
Half-day tutorials
T1: Subcellular modeling (full day)
Dr. Andrew Gallimore (Okinawa Institute of Science and Technology, Japan)
Dr. Weiliang Chen (Okinawa Institute of Science and Technology, Japan)

T2: Detailed modeling of structure and function at the cellular level (full day)

Dr. Ben Torben-Nielsen (University of Hertfordshire, UK)
Dr. Elisabetta Iavarone (EPFL, Switzerland)

Description of the tutorial:
In the morning session, we introduce the morphology of dendrites and axons, the specialised input and output arborisations of neurons. Their shape is pivotal for brain functioning for two reasons: First, overlap between dendrites and axons defines the micro-circuit. Second, the shape and membrane composition of dendrites define how inputs are transformed into relevant outputs. In this tutorial, we will start by explaining the importance of morphologies and how to quantify them (say, in order to distinguish healthy from pathological morphologies). We will touch on algorithmic synthesis of large numbers of unique neuronal morphologies for application in large-scale modelling efforts. We finish the morning session with a hands-on tutorial using btmorph [1] to analyse populations of neuronal morphologies.

In the afternoon session, we explain how neuronal dynamics takes place at the single neuron level and how dendrites turn input signals into an output. We briefly explain the conductance-based and compartmental-modelling paradigms to simulate the dynamics on neurons with detailed membrane composition and elaborate neuronal morphologies. We then proceed to show several free community resources to construct, simulate, share and analyse single neuron models. We end the afternoon session with a hands-on demonstration of how to construct and simulate detailed models of neurons using NEURON and python [2].

References and background reading:
[1] Torben-Nielsen B. An efficient and extendable Python library to analyze neuronal morphologies. Neuroinformatics 12:619-622, 2014 .
[2] James G.K., Hines M., Hill S., Goodman P.H., Markram H.,1 Schürmann F. Component-Based Extension Framework for Large-Scale Parallel Simulations in NEURON. Front.Neuroinformatics, 3:1-12, 2009.
[3] Torben-Nielsen B., Cuntz H. Introduction to dendritic morphology, The computing dendrite, Springer, 2014.
[4] London M., Häusser M. Dendritic computation. Annu Rev Neurosci. 28:503-32, 2005.
[5] Parekh R., Ascoli G. Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience. Neuron 77(6): 1017–1038, 2013.
[6] Silver A. Neuronal arithmetic. Nature Reviews Neuroscience 11, 474-489, 2010.
T3: Simulation of large-scale neural network (full day)
Dr. Sacha J. van Albada (Jülich Research Centre and JARA, Jülich, Germany)
Jonas Stapmanns (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 memory 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. The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model. Cereb. Cortex. 2014;24(3):785–806. DOI: 10.1093/cercor/bhs358.
[4] Plotnikov, D., Rumpe, B., Blundell, I., Ippen, T., Eppler, J.M. and Morrison, A., 2016. NESTML: a modeling language for spiking neurons. arXiv preprint arXiv:1606.02882.
T4: Modeling and analysis of extracellular potentials (half day)

Prof. 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 [8,10,11],

  • LFPy ( [12], a versatile tool based on Python and the NEURON simulation environment [13] ( for calculation of extracellular potentials around neurons, and

  • new results from applying the biophysical forward-modeling scheme to predict LFPs from comprehensive point-neuron network models, in particular Potjans and Diesmann’s model of the early sensory cortical microcircuit using hybridLFPy [14,15] will be presented.   

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] H Lindén et al., Front Neuroinf 7:41 (2014)
[13] ML Hines et al., Front Neuroinf 3:1 (2009)
[14] TC Potjans and M Diesmann, Cereb Cortex 24:785 (2014)
[15] E Hagen et al., Cereb Cortex  26:4461 (2016)

 T5: Neuroscience data analysis (half day)
Dr. Arvind Kumar (KTH, Stockholm)
Dr. Michael Denker (Jülich Research Centre)



In this tutorial we will explain the theory and practical issues associated with some of the most common tools to analyse spiking activity. Specifically, we will focus on the estimation of firing rate, spike train irregularity, pairwise and higher order correlations, trial-by-trial variability and co-variability, spectrum, dimensionality reduction and estimation (e.g. Gaussian factor analysis). The tutorial will be split in two parts.
In the first part we will provide the theoretical background behind the analysis methods and interpretation of the results. In the second part we
will demonstrate how various tools from neuroinformatics, in particular the Python libraries Neo and Elephant, work together in building up
robust and reproducible analysis workflows. In addition, we will discuss practical issues related to the analysis methods and interpretation of
the results. All through the tutorial we will focus on the spiking activity but most of the methods can be generalized to study other neural data.

Suggested reading:
Gruen S and Rotter S (2010) Analysis of parallel spike trains. Springer.
Cunningham JP and Yu B (2014) Dimensionality reduction for large-scale neural recordings. Nature Neuroscience, 17, 1500–1509. doi:10.1038/nn.3776 Averbeck B, Latham PE, Pouget A (2006) Neural correlations, population coding and computation. Nature Reviews Neurosci. 7:358-366 (2006).
 T6: Neuroinformatics resources (half day)

Padraig Gleeson (University College London, UK)
Andrew P. Davison (CNRS, Gif-sur-Yvette, France)
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.