NeBICA 2014 Abstracts


Short Papers
Paper Nr: 1
Title:

VirtualEnaction - A Platform for Systemic Neuroscience Simulation

Authors:

Nicolas Denoyelle, Florian Pouget, Thierry Vieville and Frédéric Alexandre

Abstract: Considering the experimental study of systemic models of the brain as a whole (in contrast to models of one brain area or aspect), there is a real need for tools designed to realistically simulate these models and to experiment them. We explain here why a robotic setup is not necessarily the best choice, and what are the general requirements for such a bench-marking platform. A step further, we describe an effective solution, freely available on line and already in use to validate functional models of the brain. This solution is a digital platform where the brainy-bot implementing the model to study is embedded in a simplified but realistic controlled environment. From visual, tactile and olfactory input, to body, arm and eye motor command, in addition to vital somesthetic cues, complex survival behaviors can be experimented. The platform is also complemented with algorithmic high-level cognitive modules, making the job of building biologically plausible bots easier.

Paper Nr: 2
Title:

Visualization of a Virtual Caenorhabditis elegans in WebGL

Authors:

Andoni Mujika, Gorka Epelde, Alessandro De Mauro and David Oyarzun

Abstract: This paper presents the work that has been done in Si Elegans project in order to visualize the locomotion and the behaviour of a virtual reproduction of the nematode Caenorhabditis elegans, one of the most studied animal in neuroscience. The project aims to develop the first hardware-based computing framework that will accurately mimic this worm. It will enable complex and realistic behaviour to emerge through interaction with a rich and dynamic simulation of a natural or laboratory environment. In order to visualize the physical behaviours that emerge from the neuronal system that has been constructed in the project, a web environment has been designed where the user will be able to define an assay and to run it in a WebGL-based 3D virtual arena. For that a relation has been defined from the physics based simulation (run on the server side) and the simplified web rendering of it.

Paper Nr: 3
Title:

Si elegans - Computational Modelling of C. elegans Nematode Nervous System using FPGAs

Authors:

Pedro Machado, John Wade and T. M. Mcginnity

Abstract: It has long been the goal of computational neuroscientists to understand and harness the parallel computational power of the mammalian nervous system. However, the vast complexity of such a nervous system has made it very difficult to fully understand even the most basic of functions such as movement and learning and accordingly there has been increasing attention paid to the development of emulations of simpler systems. One such system is the C. elegans nematode, which has been widely studied in recent years and there now exists a vast wealth of biological knowledge about its nervous structure, function and connectivity. The Si elegans EU FP7 project aims to develop a Hardware Neural Network (HNN) to accurately replicate the C. elegans nervous system behaviour to enable neuroscientists to better understand these basic functions. To fully replicate the C. elegans biological system requires powerful computing technologies, based on parallel processing, for real-time computation and therefore will use Field Programmable Gate Arrays (FPGAs) to achieve this. In this paper an overview of the complete hardware system required to fully realise Si elegans is presented along with an early small scale implementation of the hardware system.

Paper Nr: 4
Title:

Neuron Models in FPGA Hardware - A Route from High Level Descriptions to Hardware Implementations

Authors:

Finn Krewer, Aedan Coffey, Frank Callaly and Fearghal Morgan

Abstract: This paper presents the LEMS2HDL toolsuite which converts Low Entropy Model Specification (LEMS) neuron/neural network models to synthesisable Hardware Description Language (HDL) hardware descriptions. The LEMS2HDL process will provide a route for the neuroscience community to perform accelerated Field-Programmable Gate Array (FPGA) hardware implementations of the growing library of LEMS neuron/neural network models. The paper describes the LEMS to HDL conversion process and references the previously reported vicilogic platform. The paper compares the resulting FPGA hardware simulation of three LEMS neuron models with the LEMS model simulation.

Paper Nr: 5
Title:

Towards an Electro-optical Emulation of the C. elegans Connectome

Authors:

Alexey Petrushin, Lorenzo Ferrara, Carlo Liberale and Axel Blau

Abstract: The tiny worm Caenorhabditis elegans features one of the simplest nervous systems in nature. The hermaphrodite contains exactly 302 neurons and about 8000 connections. The Si elegans project aims at providing a reverse-engineerable model of this nematode by emulating its nervous system in hardware and embodying it in a virtual world. The hardware will consist of 302 individual FPGAs, each carrying a neuron-specific neural response model. The FPGA neurons will be interconnected by an electro-optical connectome to distribute the signal at the axonal output or gap-junction pin of an FPGA neuron onto the respective synaptic input or gap-junction pins of those target FPGA neurons that a neuron interconnects with. This technology will replicate the known connectome of the nematode to allow for an as biologically meaningful as possible and truly parallel information flow between neurons. This article focuses on the concepts and first implementation steps of such optical connectome.

Paper Nr: 6
Title:

Exploring Neural Principles with Si elegans, a Neuromimetic Representation of the Nematode Caenorhabditis elegans

Authors:

Axel Blau, Frank Callaly, Seamus Cawley, Aedan Coffey, Alessandro de Mauro, Gorka Epelde, Lorenzo Ferrara, Finn Krewer, Carlo Liberale, Pedro Machado, Gregory Maclair, Thomas Martin McGinnity, Fearghal Morgan, Andoni Mujika, Alexey Petrushin, Gautier Robin and John Wade

Abstract: Biological neural systems are powerful, robust and highly adaptive computational entities that outperform conventional computers in almost all aspects of sensory-motor integration. Despite dramatic progress in information technology, there is a big performance discrepancy between artificial computational systems and brains in seemingly simple orientation and navigation tasks. In fact, no system exists that can faithfully reproduce the rich behavioural repertoire of the tiny worm Caenorhabditis elegans which features one of the simplest nervous systems in nature made of 302 neurons and about 8000 connections. The Si elegans project aims at providing this missing link. This article is sketching out the main platform components.