Workpackage description

Two core aspects govern the project. The first addresses the technological design and assembly of the Si elegans hardware architecture accompanied by the development of the virtual arena and of the neural response model design and FPGA configuration interface followed by their integration into a user-friendly web-accessible platform. The second addresses its deployment to the scientific community for its independent peer-validation as a free-access tool and testbed for neurocomputational studies.

WP2 Development of FPGA representations of C. elegans neurons and muscles

Leader: Martin McGinnity (UU.lSRC)

The inherent signal processing and response logic of each neuron shall be (re)programmable. We therefore decided to base the hardware design on dynamically reconfigurable field programmable gate arrays (drFPGAs). The need for dynamically reprogrammable somato-dendritic circuitry has several advantages: to stay flexible in defining the type and thus the response behaviour of a neuron, to implement any kind of synaptic or dendritic pre-processing algorithms, to freely adjust or upgrade the algorithms for emulating neural development (changes in neural response) or implementing upcoming neuroscience knowledge, and to emulate disease states (e.g., Parkinson's, epileptic seizures) by temporarily modulating the response behaviour at run-time. drFPGAs thus offer the best ratio between hardware costs and performance, accuracy, and parameterization space. Since FPGA technology currently experiences fast technological advances, it will be easy to exchange individual modules/components for more powerful or smaller ones at any time.

Unlike most strategies pursued to date, individual FPGAs will not host an entire neural network simulation but perform the arithmetic of individual neurons. Their programmable grid shall be sufficiently large to place even intricately detailed models and neural processing mechanisms on a single FPGA. Furthermore, FPGAs feature static memory that will be used to temporarily or permanently store state variables. This will allow us to mimic biological memory by modulating information flow through these state variables, in that, any physical memory may be interpreted as a reference to individual parameters that define memory in biological systems (e.g., protein concentration, number of ion channels in a synapse, DNA methylation, ...). Any graded signal type will be considered as part of a post-synaptic model building block described in WP 5, and in particular Task 5.2.

WP 2 also focuses on the design of the FPGA C. elegans neural and muscle emulation platform. It will consist of 302 individual FPGA boards representing the individual neurons of the C. elegans nervous system and of pooled FPGA representations of the 95 C. elegans muscles. We will create a design for the complete neural modelling and muscle flex system, taking into account electronic, physical, heat, communication, optical and power constraints.

WP3 3D interconnection of FPGA modules to replicate the connectome of C. elegans

Leader: Enzo di Fabrizio & Carlo Liberale (IIT.NAST)

In almost all hardware implementations of neural networks, the issue of inter-neuron connectivity is a major problem. If implemented on-chip e.g., ASIC, typically 90% of the chip is composed of interconnect and scaling networks becomes a major problem. In this project we are proposing to solve this problem by using off-chip interconnects. Four complementary interconnection strategies will be pursued and compared for their ease of implementation, reliability, functionality and scalability. They will be implemented by adding two elements to each FPGA neuron module: i) a synaptic/gap junction input field and an axonal output line with distribution elements that communicate the neural response simultaneously to individual synapses of one or many target neurons.

WP4 Development of a virtual arena for behavioural studies

Leader: Peter Leskovsky (VT.eHBA)

Several C. elegans-specific descriptors of its physiology, morphology and body mechanics exist, including a realistic representation of the body (Virtual Worm) and aspects of locomotion (Niebur & Erdos, 1993; Wakabayashi, 2006; Bryden & Cohen, 2008; Boyle, 2009; Haspel et al., 2010; Mailler et al., 2010; Stephens et al., 2010). Based on these data, we will embody the Si elegans hardware nervous system of C. elegans created in WP 2 & 3 through a biophysically realistic virtual representation of the nematode in a virtual environment. The virtual body will share the shape, body-physics (e.g., elasticity, friction) and cellular organization of C. elegans (including realistic spatio-functional representations of sensory cells). The interplay between active actuation through sensory-driven control circuits of its nervous system and passive actuation by environmental factors (material-properties, arena topology, gravity, air- or fluid-flow etc.) will be considered. The simulation of this virtual body being situated in a virtual arena will be running on standard PC hardware with powerful GPUs. The virtual arena can be freely configured to copy the 3D geometries and biophysical features of an experimental environment used in in vivo studies. It will display the native behaviour of the Si elegans, provide simulated environmental stimuli to its sensory neurons and show stimuli-induced responses (e.g., muscle actuation, secretory events). Information flow will be channelled in real-time through a bi-directional interface between the computer and the Si elegans nervous system. Sensory neurons of the Si elegans nervous system will receive their input from artificial sensor elements. Si elegans' motor neuron activity will actuate associated muscles of the virtual body. Through real-time closed-loop feedback, any resulting sensory experience (e.g., change in posture, touch, change of chemical concentration gradients) will be coded and transmitted to the Si elegans as new sensory input.

WP5 Neural model definition GUI, HDL translation, FPGA bitstream generation and network state analysis

Leader: Fearghal Morgan (NUI.BIRC)

WP 5 will deliver a user-friendly neuron model submission and configuration facility for defining arbitrarily detailed neural stimulus-response models for individual C. elegans FPGA-based neurons together with a selection of pre-defined neuron models. The platform will provide automated tools to translate users' high level neuron model descriptions to parameterisable Hardware Description Language (HDL) format for automatic logic synthesis, automatic FPGA unit allocation, FPGA place and route (using third party tools), and FPGA hardware configuration. The Si elegans software framework will be supported by a GUI.

The "modelling space" shall allow for the definition of relevant neural processing parameters in a pictorial object-oriented flow diagram (graphical drag-and-drop manner) or script. The desired network structure shall be created by simply selecting various neuron, synapse or gap junction models from a library of available components and connecting them together. An assembly can be stored as a neuron-specific model to become a high-level, properly documented building block for other researchers. The modelling toolset will provide all required design elements to address and freely combine all known features and events of neural signal transmission down to the synaptic level, possibly including even abstracted models of signalling cascades. These design elements can be altered, thus personalized and versioned by community members for experimental purposes. E.g., the function of an individual synapse may include cable properties of the pre-synaptic axon and synapse to account for physiological and morphological boundary conditions that shape/affect signal properties such as signal transmission delays and attenuation. We will furthermore implement a standard set of amplitude-invariant, self-terminating action potentials with stereotyped waveforms as well as graded regenerative potentials as the predominant signal type in C. elegans with amplitudes and waveforms that are highly sensitive to the size, duration and waveform of the stimulus. We will finally provide readout and storage tools for reverse-engineering nervous-system function.

WP6 - Pictorial representation of overall WP and individual tasks
WP6 - Pictorial representation of overall WP and individual tasks

WP6 Assembly, validation and deployment of Si elegans platform for public access and peer-contribution

Leader: Gorka Epelde Unanue (VT.eHBA & all partners)

The assembly and deployment workpackage will devise a strategy for the optimal integration of individual hardware (WP 2 & 3) and software (WP 4 & 5) elements to result in a functional and autonomous Si elegans prototype platform, which will be deployed for public access and use. We will integrate the technologies researched and developed in previous workpackages in an autonomous device that can be programmed, controlled and used through a web interface. We will internally validate overall platform functionality. The Si elegans platform will be accessible by the scientific community (e.g., in a shared-time, fair-use model) through a client/server-based remote access environment for its independent validation and pursuit of scientific studies. Feedback from researchers will be shared through community outreach tools and be implemented into the public platform.

WP7 Dissemination, collaboration and exploitation

Leader: Axel Blau (IIT.NBT supported by IIT's Projects Office and all partners)

The spirit and central mission of Si elegans as an 'open-science, peer-contribution' project is the early involvement of the scientific community, particularly groups interested in behavioural and modelling aspects of C. elegans, but also the neurocomputation community at large. The consortium therefore agreed on adopting an 'open access' approach to new knowledge generated by the project. This choice has been taken in order to maximize the medium and long-term impact of the project results in various research fields with substantial innovation potential. This dissemination, collaboration and exploitation workpackage will ensure the effective execution of all outreach and dissemination measures.

Peter Leskovsky