Si elegans EU project information is available in this link
Despite its seeming simplicity, the nervous system of the hermaphroditic nematode Caenorhabditis elegans with just 302 neurons gives rise to a rich behavioral repertoire. Besides controlling vital functions (feeding, defecation, reproduction), it encodes different stimuli-induced as well as autonomous locomotion modalities (crawling, swimming and jumping). For this dichotomy between system simplicity and behavioral complexity, C. elegans has challenged neurobiologists and computational scientists alike. Understanding the underlying mechanisms that lead to a context-modulated functionality of individual neurons would not only advance our knowledge on nervous system function and its failure in pathological states, but have directly exploitable benefits for robotics and the engineering of brain-mimetic computational architectures that are orthogonal to current von-Neumann-type machines. To provide the tools for unravelling the mysteries of nervous system function in C. elegans is the vision of the Si elegans community.
Lessons learned from past and current computational approaches to deciphering and reconstructing information flow in the C. elegans nervous system corroborate the need of refining neural response models and linking them to intra- and extra-environmental interactions to better reflect and understand the actual biological, biochemical and biophysical events that lead to behavior. The Si elegans initiative was motivated by the lack of a holistic closed-loop simulation environment, where neural events can be linked to as well as altered by their behavioral outcome. The Si elegans project team therefore designed a unique closed-loop hard- and software environment that allows you to do so. The result is this Si elegans platform. This framework is based on brain-mimetic principles for the emulation and reverse-engineering of C. elegans nervous system function in a behavioral context – free to use and to extend by everyone!
If you are interested in the challenge of deciphering how nervous system function in C. elegans encodes behavior, be it through the neurocomputational modelling of neurons and networks or the study of behavioral paradigms, you are cordially invited to test-drive Si elegans. No matter whether you are just curious or pursue concrete scientific questions, Si elegans may provide you with the insights and tools to leap forward and advance your field of study. In any case, we would be delighted to hear back from you (feedback form). Any constructive feedback that will lead to an improvement of Si elegans is most welcome. If you are a real C. elegans enthusiast, a modelling wizard or simply a genius, you are moreover invited to extend any of the Si elegans functionalities. Most of the open-access peer-contribution platform is in the process of being documented, and the code of the individual modules will be made available through open-source repositories (link(s) will be provided once source releasing strategy is agreed). We furthermore welcome the adaptation and testing of models and modules that have been generated in other contexts and communities such as ‘Nemasys’, the ‘Perfect C. elegans Project’, the ‘Virtual C. elegans Project’, the NEMALOAD project, DevoWorm and the OpenWorm project.
Si elegans aims at providing the required tools on a unique hardware architecture to advance our understanding of how nervous system function in C. elegans encodes behavior. Its main features are the following:
The Si elegans nervous system consists of a dedicated hardware infrastructure that, unlike software implementations, permits true parallelism in the intra-neural as well as inter-neural signal processing. It is based on 329 field-programmable gate arrays (FPGAs), a parallel circuit definition architecture by design. Unlike functionally pre-defined neuromorphic computing systems, FPGAs are freely reconfigurable circuit fabrics that can accommodate distinct neural response models, one for each C. elegans neuron. Similarly, FPGAs can carry one or several other models that interact with neurons, such as models of downstream muscle response (e.g., 27 FPGAs share up to 6 muscle models each to emulate the 95 C. elegans striated body wall muscles and 60 nonstriated muscles (a future functionality)) and algorithms of subsequent body physics. These circuit-embedded response models may be dynamic and context-aware and thus evolve over time. This adaptation is not restricted to simply adjusting e.g., synaptic weights, but may allow the model to respond differently as a function of the (sensory) signal type and origin, environmental conditions (e.g., T) and their history, or of the local level of ‘neuromodulatory biochemical background’ at a given time. In view of the high number (359,200) of adaptive logic modules (ALM) of the chosen FPGAs (Altera Stratix V GX), models are thus allowed to include aspects that are often ignored in computational neuroscience. For instance, the complexity of the dendritic tree suggests its involvement in the computational pre-processing of incoming signals such as their temporal filtering and amplitude modulation and its effect on altering synaptic properties.
279 neurons exchange synaptic information through an Ethernet backbone. To nevertheless warrant the temporal parallelism inherent to biological networks and events, the hardware-based network will operate on a central clock (50 MHz). Some neural operations will require more FPGA ‘hardware clock’ cycles than others. At the cost of real-time operation, the supervising FPGA-based controller will thus ensure that all model operations of all neurons including the inter-neural signal transmission within a ‘biological clock’ cycle are completed before a new one starts. Any delays related to different lengths at the axonal arbor or synaptic properties can be incorporated in the respective neural models on the individual FPGAs.
The platform furthermore features a dynamic version of an opto-electrical connectome based on digital light processing (DLP) technology. The reconfigurable digital micromirror devices (DMDs) allow for exploring the impact of changes in neural interconnectivity on neural information processing. It is currently restricted to the synaptic signal transmission between the 20 neurons of the pharyngeal sub-network [ADD LINK!!] as a proof-of-concept implementation.
To make the Si elegans framework user-friendly for novice and expert users alike, several model generation (e.g., drag-and-drop) and import functionality (e.g., from existing simulation engines) are provided (link(s) to model design and network definition GUI(s) Login Required). The current model design is based on the low entropy modelling specification (LEMS) language. In a neural network configuration graphical user interface (GUI), the user places neuron and synapse models in a graphically represented C. elegans connectome and can parametrize specific neuron models.
This biomimetic Si elegans hardware nervous system emulation is controlling a virtually embodied and physically realistic representation of the nematode (via soft-body physics) in an equally realistic three-dimensional virtual behavioral arena (e.g., an agar Petri dish) (link to behavioral definition GUI Login Required). In there, the virtual C. elegans will encounter commonly tested stimuli (e.g., touch, chemicals, electric fields, light and/or temperature gradients) at any pre-defined time. These, together with characteristics of the environment (e.g., the shape of the plate, substrate properties) and the initial position and orientation of the nematode, can be batch-defined in a dedicated behavioral experiment configuration interface. The definition parameters are translated into an editable extensible markup language (XML) schema (link). During an experiment, the sensory experience is transmitted to the sensory neurons in the FPGA network. Based on published knowledge on network-internal circuitry and signal processing pathways, the sensory input (and proprioceptive information) will generate a motor output to instruct the muscles of the virtual worm on what to do next. In this closed-loop scenario, it will furthermore be possible to read out any network state (e.g., synaptic weights) at any given time for the reverse-engineering of network function (link to results read-out module/GUI). The simulation results, both the neuron variable traces as well as the body motion, can be visualized and downloaded after the simulation is over (link to results viewer).
The early implementation and functionality of Si elegans may be compared with personal computers in the 70’s of the last century: just like the PC hardware and its basic operating system at that time, the recently launched Si elegans platform provides a basic computational framework to model C. elegans nervous system function and observe the generated behavioral output. Its usefulness in predicting neural function to reproduce a certain behavior will therefore strongly depend on its adoption and on (model) contributions by both the biological and neurocomputational communities. But imagine this: Once the chosen models generate a behavioral output that is comparable to observations in real laboratory experiments, the platform will allow the neuroscience community to better understand, if not anticipate, the neural mechanisms that underlie behavior. Be the first to get there!
For more detail go to link (Login Required)