NeBICA 2015 Abstracts


Short Papers
Paper Nr: 1
Title:

Input Encoding Proposal for Behavioral Experiments with a Virtual C. elegans Representation

Authors:

Gorka Epelde, Andoni Mujika, Roberto Alvarez, Peter Leškovský, Alessandro de Mauro, Finn Krewer and Axel Blau

Abstract: This paper discusses a Caenorhabditis elegans (C. elegans) nematode behavioral experiment input encoding. It proposes a common digital representation for behavioral studies with C. elegans. This work is a step forward towards the reproducibility and comparability of in silico simulations of the nematode with real-world experiments. The digital representation is divided into environmental and experimental configurations. The behavioral input is structured by duration-based behavioral experiment types at the top level (i.e. interaction at a specific time, interaction from t0 – t1 and overall duration) and by interaction type (i.e. mechanotaxis, chemotaxis, thermotaxis, galvanotaxis and phototaxis) for each duration-based type. The environment configuration is composed of the identification of the worm’s mutation type, worm crowding, initial location, configuration of the assay plate, and obstacle settings. Parameters are defined by an XML schema to ensure the interoperability with other simulation solutions. It is being implemented and tested in the context of the Si elegans project

Paper Nr: 2
Title:

Si elegans: A Computational Model of C. elegans Muscle Response to Light

Authors:

Alicia Costalago Meruelo, Pedro Machado, Kofi Appiah and T. Martin McGinnity

Abstract: It has long been the goal of computational neuroscientists to understand animal nervous systems, but their vast complexity has made it very difficult to fully understand even basic functions such as movement. The C. elegans nematode offers the opportunity to study a fully described connectome and link neural network to behaviour. In this paper a model of the responses of the body wall muscle in C. elegans to a random light stimulus is presented. An algorithm has been developed that tracks synapses in the nematode nervous system from the stimulus in the phototaxis sensory neurons to the muscles cells. A linear second order model was used to calculate the isometric force in each of the C. elegans body wall muscle cells. The isometric force calculated resembles that of previous investigations in muscle modelling.

Paper Nr: 3
Title:

An Electro-optical Connectome Prototype for Eight Neuron Representations in FPGA Technology

Authors:

Lorenzo Ferrara, Alexey Petrushin and Axel Blau

Abstract: In nature, interneural signaling is highly parallel and temporally precisely structured. It would require equal parallelism and temporal accuracy to faithfully mimic neural communication in hardware representations. Light-based communication schemes fulfil this prerequisite. We report on a prototype of an optical connectome implementation for a neuromorphic system eventually consisting of eight neurons. The platform is based on field-programmable gate arrays (FPGAs) that run neuron-specific response models. Their axons are represented by light-emitting diodes (LEDs) with axonal arbors in the form of micro-patterned transparencies. They distribute membrane voltage threshold crossings, which are represented by light pulses, onto synapse-specific photodiodes of postsynaptic neurons. This contribution sketches out the overall system design and discusses its prospective application in replicating the connectome of the nematode C. elegans in the framework of the Si elegans project.

Paper Nr: 4
Title:

Past and Recent Endeavours to Simulate Caenorhabditis elegans

Authors:

Alexey Petrushin, Lorenzo Ferrara and Axel Blau

Abstract: Biological nervous systems are robust and highly adaptive information processing entities that excel current computer architectures in almost all aspects of sensory-motor integration. While they are slow and inefficient in the serial processing of stimuli or data chains, they outperform artificial computational systems in seemingly ordinary pattern recognition, orientation or navigation tasks. Even one of the simplest nervous systems in nature, that of the hermaphroditic nematode Caenorhabditis elegans with just 302 neurons and less than 8,000 synaptic connections, gives rise to a rich behavioural repertoire that – among controlling vital functions - encodes different locomotion modalities (crawling, swimming and jumping). It becomes evident that both robotics and information and computation technology (ICT) would strongly benefit if the working principles of nervous systems could be extracted and applied to the engineering of brain-mimetic computational architectures. C. elegans, being one of the five best-characterized animal model systems, promises to serve as the most manageable organism to elucidate the information coding and control mechanisms that give rise to complex behaviour. This short paper reviews past and present endeavours to reveal and harvest the potential of nervous system function in C. elegans.

Paper Nr: 5
Title:

Web-enabled Neuron Model Hardware Implementation and Testing

Authors:

Fearghal Morgan, Finn Krewer, Frank Callaly, Aedan Coffey and Brian Mc Ginley

Abstract: This paper presents a prototype web-based Graphical User Interface (GUI) platform for integrating and testing a system that can perform Low-Entropy Model Specification (LEMS) neural network description to Hardware Description Language (VHDL) conversion, and automatic synthesis and neuron implementation and testing on Field Programmable Gate Array (FPGA) testbed hardware. This system enables hardware implementation of neuron components and their connection in a small neural network testbed. This system incorporates functionality for automatic LEMS to synthesisable VHDL translation, automatic VHDL integration with FPGA logic to enable data I/O, automatic FPGA bitfile generation using Xilinx PlanAhead, automated multi- FPGA testbed configuration, neural network parameter configuration and flexible testing of FPGA based neuron models. The prototype UI supports clock step control and real-time monitoring of internal signals. References are provided to video demonstrations.