International Joint Conference on Neural Networks (IJCNN 2015)

Written by Axel Blau on 23 April 2015

International Joint Conference on Neural Networks (IJCNN 2015)



Simulating an entire nervous system? An exemplary Caenorhabditis elegans emulation case study.


Sunday, 12th July 2015

08.30 – 10.30


What computational mechanisms and feedback loops need to be considered to faithfully mimic nervous system function? And what processes allow one of the most minimalistic nervous systems in nature - that of the nematode Caenorhabditis elegans - to not only sustain vital body function, but to give rise to a rich behavioral repertoire and basic forms of learning? Although there is no doubt that neuroscience will ultimately provide the answers, we ask whether and what neurocomputational approaches are suited to simulate neural processing events in an organism and thereby confirm or even anticipate some of the underlying principles. This tutorial will introduce the required elements to mimic the nervous system function of a real-world organism in a virtual behavioural context. Software and hardware-based representations of neural function will be compared. Currently available toolsets to define neural response models and brain-mimetic parallel information transfer techniques will be discussed. Means of virtually embodying a neural network in a meaningful behavioural context will be explored. We will finally illustrate how a holistic simulation/emulation platform could be accessed by users with diverse expertise and be exploited for neurocomputational studies. The tutorial is based on efforts, developments and findings in the context of the Si elegans project with further in-depth information @

Topics to be covered

I   Introduction to the C. elegans nervous system. What do we already know about nervous system function in general and what can we learn from one of the simplest nervous systems in nature? Hear about the basic features of the nervous system of C. elegans with its exactly 302 neurons and about 8000 synaptic connections. We will show how its function is tightly coupled to sensory and motor modalities. We will furthermore focus on the similarities and particularities of this nervous system with respect to those of higher organisms. The introduction will end with rehearsing current and past approaches to simulating C. elegans. (15 min, Axel Blau)

II   Neuronal response models in hardware. The second session will explore various technologies of emulating neural function in hardware with a special focus on field-programmable gate arrays (FPGAs) as one of the most promising emerging technologies for defining neural representations. After a short introduction to FPGA tools and technologies, we will guide you through a step-by-step design of a neuron/synapse model for implementation on an FPGA and the translation of that model to executable code to run on the FPGA. (30 min, Martin McGinnity/ Pedro Machado)

III   Neuron2FPGA: Graphical neuron model capture and automated FPGA hardware implementation. The third session will demonstrate

the browser-based Graphical User Interface (GUI) toolsuite (Neuron2FPGA) for the automated implementation and verification of neuron and synapse models on Field Programmable Gate Array (FPGA) hardware. We will guide you through the GUI-to-FPGA bitstream file creation process with its testbed-based verification (30 min, Brian Mc Ginley/Fearghal Morgan).

  • GUI browser-based capture of a Hodgkin-Huxley (HH) neuron model and generation/simulation of a LEMS model description
  • GUI-based automation of FPGA Electronic Design Automation (EDA) tools to create an FPGA hardware configuration file for the neuron
  • Practical simulation of a  HH neuron model  on an FPGA testbed
  • Comparison of hardware simulation results with software jLEMS simulation results

IV   Body physics modelling; definition and visualization of behavioural experiments.  The fourth session reviews current approaches to the modelling of C. elegans’ locomotion. We will discuss the challenges of creating physically faithful representations and of their coherent interaction with a driving neuronal network. We will then introduce a web-based user environment for defining and visualizing behavioural experiments to let attendants create their own assays. (30 min, Andoni Mujika)

V  Approaches to establishing hardware-based neural connectivity. Because nervous systems process information in a highly parallel fashion, we will explore past and current strategies and their limitations for their mimicry in computational substrates. We will then exemplarily compare two light-mediated neural communication schemes based on bitmap and holographic projection in the final session. (15 min, Axel Blau, Lorenzo Ferrara, Alexey Petrushin)


Axel Blau, Martin McGinnity, Fearghal Morgan, Andoni Mujika.


 Martin McGinnity Prof. Martin McGinnity is the Dean of Science and Technology at  Nottingham Trent University, United Kingdom, and is responsible for the strategic direction and management of the School's staff, academic provision and research activities. Formerly, he was Director of the Intelligent Systems Research Centre, Acting Associate Dean of the Faculty of Computing and Engineering and Head of the School of Computing and Intelligent Systems at the University of Ulster. He is a Fellow of the IET, SMIEEE and a Chartered Engineer.
 Brian McGinley NUI

Dr. Brian Mc Ginley holds a B.E. (Hons) in Electronic and Computer Engineering and a Ph.D. from the National University of Ireland (NUI), Galway. Since 2010, he is a member of both the Signal Processing and the Bio-Inspired and Reconfigurable Computing research groups at NUI Galway. Brian has worked as a postdoctoral researcher in the areas of Spiking Neural Networks, Genetic Algorithms as well as Biomedical Signal and Image Processing. He is currently a contributor to the Si elegans consortium, investigating the implementation of computational neuron models in reconfigurable hardware.

 Andoni Mujika 193

Mr. Andoni Mujika holds a degree in Mathematics from the University of the Basque Country (2002-2007). He spent his last study year at the Aarhus University (Denmark). In 2010, he graduated (MSc) in Mathematical Modeling, Statistics and Computation at the University of Basque Country (Spain). Since 2007, he has been working as a researcher in the Interactive Computer Graphics Department of the Technology Center Vicomtech-ik4 (Visual Interaction & Communication Technologies). During these years, he has attended several international conferences and published several papers. He currently pursues his PhD in Computer Science at the Rey Juan Carlos University in Madrid (Spain).

 Axel Blau Dr. Axel Blau is team leader at the Dept. for Neuroscience and Brain Technologies (NBT) of the Italian Institute of Technology (IIT), Italy. He holds a diploma and a PhD degree in physical chemistry both from the University of Tübingen, Germany. After being a postdoc in neurobiology at the California Institute of Technology, he held an assistant professorship in physics at the University of Kaiserslautern, Germany. His current research activities are centred on the development of enabling technologies for the sampling, elicitation, and manipulation of neural activity and target at the hardware-embedded simulation and implementation of neural coding schemes.

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Disclaimer: The opinions expressed on this web page and the presentation slides are that of the organizers, not of the IJCNN conference or IEEE, or any other entity.