Pedro Machado

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

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

The Si elegans Project – The Challenges and Prospects of Emulating Caenorhabditis elegans

Caenorhabditis elegans features one of the simplest nervous systems in nature, yet its biological information processing still evades our complete understanding. The position of its 302 neurons and almost its entire connectome has been mapped. However, there is only sparse knowledge on how its nervous system codes for its rich behavioral repertoire. The EU-funded Si elegans project aims at reverse-engineering C. elegans‘ nervous system function by its emulation. 302 in parallel interconnected field-programmable gate array (FPGA) neurons will interact through their sensory and motor neurons with a biophysically accurate soft-body representation of the nematode in a virtual behavioral arena. Each FPGA will feature its own reprogrammable neural response model that researchers world-wide will be able to modify to test their neuroscientific hypotheses. In a closed-feedback loop, any sensory experience of the virtual nematode in its virtual environment will be processed by sensory and subsequently interconnected neurons to result in motor commands at neuromuscular junctions at the hardware-software interface to actuate virtual muscles of the virtual nematode. Postural changes in the virtual world will lead to a new sensory experience and thus close the loop. In this contribution we present the overall concepts with special focus on the virtual embodiment of the nematode. For further information and recent news please visit http://www.si-elegans.eu.

Si elegans: Hardware architecture and communications protocol

The hardware layer of the Si elegans EU FP7 project is a massively parallel architecture designed to accurately emulate the C. elegans nematode in biological real-time. The C. elegans nematode is one of the simplest and well characterized Biological Nervous Systems (BNS) yet many questions related to basic functions such as movement and learning remain unanswered. The hardware layer includes a Hardware Neural Network (HNN) composed of 302 FPGAs (one per neuron), a Hardware Muscle Network (HMN) composed of 27 FPGAs (one per 5 muscles) and one Interface Manager FPGA, which is physically connected through 2 Local Area Networks (LANs) and through an innovative 3D optical connectome. Neuron structures (gap junctions and synapses) and muscles are modelled in the design environment of the software layer and their simulation data (spikes, variable values and parameters) generate data packets sent across the Local Area Networks (LAN). Furthermore, a software layer gives the user a set of design tools giving the required flexibility and high level hardware abstraction to design custom neuronal models. In this paper the authors present an overview of the hardware layer, connections infrastructure and communication protocol.

Si elegans: FPGA hardware emulation of C. elegans nematode nervous system

For many decades neuroscience researchers have been interested in harnessing the computational power of the mammalian nervous system. However, the vast complexity of such a nervous system has made it very difficult to fully understand basic functions such as movement, touch and learning. More recently the nervous system of the C. elegans nematode has been widely studied and there now exists a vast wealth of biological knowledge about its nervous structure, function and connectivity. The Si elegans project aims to develop a Hardware Neural Network (HNN) to accurately replicate the C. elegans nervous system behavior to enable neuroscientists to better understand these basic functions. Replication of the C. elegans biological system requires powerful computing technologies, based on parallel processing, for real-time computation. The Si elegans project will use FPGAs due to their advanced programmable features that allow reconfigurability, high performance parallel processing and relatively low price per programmable logic element. Furthermore, the project will deliver an open-access framework that will be available via a Web Portal to neuroscientists, biologists, clinicians and engineers. In this paper an overview of the complete hardware system required to fully realize Si elegans is presented along with an early small scale implementation of the hardware system.

C. elegans behavioural response germane to Hardware modelling

The nematode C. elegans (Caenorhabditis elegans) has for many years been instrumental as a model organism for fundamental research into biological neural networks, mainly to understand the behaviour and physiology of nervous systems. The Si elegans EU FP7 project aims to develop a Hardware Neural Network (HNN) to accurately replicate the C. elegans nervous system, behaviour and response to environmental changes that will enable neuroscientists to better understand these basic functions. In this paper, we focus on consideration of some specific behaviours of the worm for modelling in the hardware based Si elegans system. Environmental response to oxygen, food, light, temperature and other chemicals are reviewed to establish specific neurons responsible for the worm behaviours. Neuron interconnects mechanisms and suitable models simulating the biological worm are also discussed.

Si elegans: Modeling the C. elegans Nematode Nervous System Using High Performance FPGAS

The mammalian nervous system is very efficient at processing, integrating and making sense of different sensory information from the outside world. When compared to the processing speed of modern computers the mammalian nervous system is very slow but is compensated for by the dense parallel nature of the brain. Understanding and harnessing the computational power of such systems has long been the goal of computational neuroscientists. However, elucidating the most basic cognitive behaviour has been difficult due to the vast complexity of such a system. Through understanding and emulating simpler nervous systems, such as the C. elegans nematode, it is hoped that new insights into nervous system behaviour can be achieved. The Si elegans EU FP7 project aims to develop a Hardware Neural Network (HNN) to accurately replicate the C. elegans nervous system which has been widely studied in recent years and there now exists a vast wealth of knowledge about its nervous function and connectivity. To fully replicate the C. elegans nervous 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. The project will also deliver an open-access framework via a Web Portal to neuroscientists, biologists, clinicians and engineers and will enable a global network of scientists to gain a better understanding of neural function. 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.

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

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

Si elegans: a computational model of C. elegans muscle response to light

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.

Online Object Trajectory Classification Using FPGA-SoC Devices

Real time classification of objects using computer vision techniques are becoming relevant with emergence of advanced perceptions systems required by, surveillance systems, industry 4.0 robotics and agricultural robots. Conventional video surveillance basically detects and tracks moving object whereas there is no indication of whether the object is approaching or receding the camera (looming). Looming detection and classification of object movements aids in knowing the position of the object and plays a crucial role in military, vehicle traffic management, robotics, etc. To accomplish real-time object trajectory classification, a contour tracking algorithm is necessary. In this paper, an application is made to perform looming detection and to detect imminent collision on a system-on-chip field-programmable gate array (SoC- FPGA) hardware. The work presented in this paper was designed for running in Robotic platforms, Unmanned Aerial Vehicles, Advanced Driver Assistance System, etc. Due to several advantages of SoC-FPGA the proposed work is performed on the hardware. The proposed work focusses on capturing images, processing, classifying the movements of the object and issues an imminent collision warning on-the-fly. This paper details the proposed software algorithm used for the classification of the movement of the object, simulation of the results and future work.

Emulation of chemical stimulus triggered head movement in the C. elegans nematode

For a considerable time, it has been the goal of computational neuroscientists to understand biological nervous systems. However, the vast complexity of such systems has made it very difficult to fully understand even basic functions such as movement. Because of its small neuron count, the C. elegans nematode offers the opportunity to study a fully described connectome and attempt to link neural network activity to behaviour. In this paper a simulation of the neural network in C. elegans that responds to chemical stimulus is presented and a consequent realistic head movement demonstrated. An evolutionary algorithm (EA) has been utilised to search for estimates of the values of the synaptic conductances and also to determine whether each synapse is excitatory or inhibitory in nature. The chemotaxis neural network was designed and implemented, using the parameterisation obtained with the EA, on the Si elegans platform a state-of-the-art hardware emulation platform specially designed to emulate the C. elegans nematode.