Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): Comparing multi-electrode recordings from simulated and biological mammalian cortical tissue

Local field potentials (LFPs) sampled with extracellular electrodes are frequently used as a measure of population neuronal activity. However, relating such measurements to underlying neuronal behaviour and connectivity is non-trivial. To help study this link, we developed the Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX). We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons. We then developed VERTEX as an easy-to-use Matlab tool for simulating LFPs from large populations (>100 000 neurons). A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue. Our model, with virtual electrodes placed anywhere in 3D, allows direct comparisons with the in vitro recording setup. We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.Comment: appears in Brain Struct Funct 201

Brain fame:From FPGA to heterogeneous acceleration of brain simulations

Among the various methods in neuroscience for understanding brain function, in-silico simulations have been gaining popularity. Advances in neuroscience and engineering led to the creation of mathematical models of networks that do not simply mimic biological behaviour in an abstract fashion but emulate its in significant detail, even to the level of its biophysical properties. Such an example is the Spiking Neural Network (SNN) that can model a variety of additional behavioural features, like encoding data and adapting according to a spike train`s amplitude, frequency and general precise pattern of arrival of spiking events on a neuron. As a result, SNNs have higher explanatory power than their predecessors, thus brain simulations based on SNNs become an attractive topic to explore. In-silico simulations of SNNs can have beneficial results not only for neuroscience research but breakthroughs can also potentially benefit medical, computing and A.I. research. SNNs, though, computationally depending workloads that traditional computing might not be able to cover. Thus, the use of High Performance Computing (HPC) platforms in this application domain becomes desirable. This dissertation explores the topic of HPC-based in-silico brain simulations. Initially, the effort focuses on custom hardware accelerators, due to their potential in providing real-time performance alongside support for large-scale non-real-time experiments and specifically Field Programmable Gate Arrays (FPGAs). The nature of FPGA-based accelerators provides specific benefits against other similar paradigms like Application Specific Integrated Circuit (ASIC) designs.Firstly, we explore the general characteristics of typical SNNs model types to identify their computational requirements in relation to their explanatory strength. We also identify major design characteristics in model development that can directly affect its performance and behaviour when ported to an HPC platform. Subsequently, a detailed literature review is made on FPGA-based SNN implementations. The HPC porting effort begins with the implementation of an extended-Hodgkin-Huxley model of the Inferior-olivary nucleus featuring advanced connectivity. The model is quite demanding and complex enough to act as a realistic benchmark for HPC implementations, while also being scientifically relevant in its own right. FPGA development shows promising performance results not only when doing custom designs but also using High-level synthesis (HLS) toolflows that significantly reduce development time. FPGAs have proven suitable for small-scale embedded-HPC uses as well. The various efforts, though, reveal a very specific weakness of FPGA development that has less to do with the silicon itself and more with its programming environment. The FPGA tools are very inaccessible to non-experts, thus any acceleration effort would require the engineer (and the FPGA development time) to be in the critical path of the research process. An important question to be answered is how the FPGA platform would compare to other popular software-based HPC solutions such as GPU- and CPU-based platforms. A detailed comparison of the best FPGA implementation with GPU and manycore-CPU ports of the same benchmark is conducted. The comparison and evaluation shows that, when it comes to real-time performance, FPGAs have a clear advantage. But for non-real-time, large scale simulations, there is no single platform that can optimally support the complete range of experiments that could be conducted with the inferior olive model. The comparison makes a clear case for BrainFrame, a platform that supports heterogeneous HPC substrates. This dissertation, thus, concludes with the proposal of the BrainFrame system. The proof-of-concept design supports standard and extended Hodgkin-Huxley models, , such as the original inferior-olive model. The system integrates a GPU-, CPU- and FPGA-based HPC back-end while also using a standard neuroscientific language front-end (PyNN) that can score best-in-class performance, alleviate some of the development hurdles and make it far more user-friendly for the typical model developer. Additionally, the multi-node potential of the platform is being explored. BrainFrame provides both a powerful heterogeneous platform for acceleration and also a front-end familiar to the neuroscientist

An efficient automated parameter tuning framework for spiking neural networks

As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier

Frontiers of Membrane Computing: Open Problems and Research Topics

This is a list of open problems and research topics collected after the Twelfth Conference on Membrane Computing, CMC 2012 (Fontainebleau, France (23 - 26 August 2011), meant initially to be a working material for Tenth Brainstorming Week on Membrane Computing, Sevilla, Spain (January 30 - February 3, 2012). The result was circulated in several versions before the brainstorming and then modified according to the discussions held in Sevilla and according to the progresses made during the meeting. In the present form, the list gives an image about key research directions currently active in membrane computing

ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations

IntroductionIn-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2) flexibility, and (3) ease of use for non-technical users. However, most existing platforms and libraries do not meet all three criteria, particularly for complex models such as the Hodgkin-Huxley (HH) model or for complex neuron-connectivity modeling such as gap junctions.MethodsThis work introduces ExaFlexHH, an exascale-ready, flexible library for simulating HH models on multi-FPGA platforms. Utilizing FPGA-based Data-Flow Engines (DFEs) and the dataflow programming paradigm, ExaFlexHH addresses all three requirements. The library is also parameterizable and compliant with NeuroML, a prominent brain-description language in computational neuroscience. We demonstrate the performance scalability of the platform by implementing a highly demanding extended-Hodgkin-Huxley (eHH) model of the Inferior Olive using ExaFlexHH.ResultsModel simulation results show linear scalability for unconnected networks and near-linear scalability for networks with complex synaptic plasticity, with a 1.99 × performance increase using two FPGAs compared to a single FPGA simulation, and 7.96 × when using eight FPGAs in a scalable ring topology. Notably, our results also reveal consistent performance efficiency in GFLOPS per watt, further facilitating exascale-ready computing speeds and pushing the boundaries of future brain-simulation platforms.DiscussionThe ExaFlexHH library shows superior resource efficiency, quantified in FLOPS per hardware resources, benchmarked against other competitive FPGA-based brain simulation implementations

Efficient Numerical Population Density Techniques with an Application in Spinal Cord Modelling

MIIND is a neural simulator which uses an innovative numerical population density technique to simulate the behaviour of multiple interacting populations of neurons under the influence of noise. Recent efforts have produced similar techniques but they are often limited to a single neuron model or type of behaviour. Extensions to these require a great deal of further work and specialist knowledge. The technique used in MIIND overcomes this limitation by being agnostic to the underlying neuron model of each population. However, earlier versions of MIIND still required a high level of technical knowledge to set up the software and involved an often time-consuming manual pre-simulation process. It was also limited to only two-dimensional neuron models. This thesis presents the development of an alternative population density technique, based on that already in MIIND, which reduces the pre-simulation step to an automated process. The new technique is much more flexible and has no limit on the number of time-dependent variables in the underlying neuron model. For the first time, the population density over the state space of the Hodgkin-Huxley neuron model can be observed in an efficient manner on a single PC. The technique allows simulation time to be significantly reduced by gracefully degrading the accuracy without losing important behavioural features. The MIIND software itself has also been simplified, reducing technical barriers to entry, so that it can now be run from a Python script and installed as a Python module. With the improved usability, a model of neural populations in the spinal cord was simulated in MIIND. It showed how afferent signals can be integrated into common reflex circuits to produce observed patterns of muscle activation during an isometric knee extension task. The influence of proprioception in motor control is not fully understood as it can be both task and subject-specific. The results of this study show that afferent signals have a significant effect on sub-maximal muscle contractions even when the limb remains static. Such signals should be considered when developing methods to improve motor control in activities of daily living via therapeutic or mechanical means

A Practical Investigation into Achieving Bio-Plausibility in Evo-Devo Neural Microcircuits Feasible in an FPGA

Many researchers has conjectured, argued, or in some cases demonstrated, that bio-plausibility can bring about emergent properties such as adaptability, scalability, fault-tolerance, self-repair, reliability, and autonomy to bio-inspired intelligent systems. Evolutionary-developmental (evo-devo) spiking neural networks are a very bio-plausible mixture of such bio-inspired intelligent systems that have been proposed and studied by a few researchers. However, the general trend is that the complexity and thus the computational cost grow with the bio-plausibility of the system. FPGAs (Field- Programmable Gate Arrays) have been used and proved to be one of the flexible and cost efficient hardware platforms for research' and development of such evo-devo systems. However, mapping a bio-plausible evo-devo spiking neural network to an FPGA is a daunting task full of different constraints and trade-offs that makes it, if not infeasible, very challenging. This thesis explores the challenges, trade-offs, constraints, practical issues, and some possible approaches in achieving bio-plausibility in creating evolutionary developmental spiking neural microcircuits in an FPGA through a practical investigation along with a series of case studies. In this study, the system performance, cost, reliability, scalability, availability, and design and testing time and complexity are defined as measures for feasibility of a system and structural accuracy and consistency with the current knowledge in biology as measures for bio-plausibility. Investigation of the challenges starts with the hardware platform selection and then neuron, cortex, and evo-devo models and integration of these models into a whole bio-inspired intelligent system are examined one by one. For further practical investigation, a new PLAQIF Digital Neuron model, a novel Cortex model, and a new multicellular LGRN evo-devo model are designed, implemented and tested as case studies. Results and their implications for the researchers, designers of such systems, and FPGA manufacturers are discussed and concluded in form of general trends, trade-offs, suggestions, and recommendations

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance