Application Plugins for Distributed Simulations on the Grid

Computing grids are today still underexploited by scientific computing communities. The main reasons for this are, on the one hand, the complexity and variety of tools and services existent in the grid middleware ecosystem, and, on the other hand, the complexity of the development of applications capable to exploit the grids. We address in this work the challenge of developing grid applications that keep pace with the rapid evolution of grid middleware. For that, we propose an approach based on plugins for grid applications that encapsulate a set of commonly used type of grid operations. We further propose more complex high-level functionalities, such as the plugins for remote exploration of simulation scenarios and for monitoring of the behavior of end-user applications in grids. We provide an example of a grid application constructed with these software components and evaluate based on it the performance of our approach in the context of the simulation of biological neurons. The results obtained on test and production grids demonstrate the usefulness of the proposed plugins, with a small performance overhead compared to traditional grid tools

Neocortical Layer 4 to Layer 2/3 Sensory Information Processing Investigated with Digital-Light-Projection Neuronal Photostimulation

The mammalian brain forms neuronal networks and microcircuits with cell-type- and anatomical-specific synaptic connections. Despite great advances in elucidating the cellular physiology of the nervous system, little is known about the computational processes occurring at the level of neuronal microcircuits. Much success has been reported in describing the synaptic input patterns of many brain regions and cell types using photostimulation systems; however, these systems are severely limited in their ability to study the integration of synaptic input from multiple synchronous or temporally correlated presynaptic locations. Here we describe a system that allows the generation of arbitrary 2-D stimulus patterns with thousands of independently controlled sites to manipulate the activity of populations of neurons with high spatial and temporal precision. The PC-controlled Digital-Light-Processing (DLP) based system updates the 780,000 parallel photostimulation beams, or pixels, at a maximum rate of 13 kHz. With the currently used projection objective, the pixel sizes at the plane of focus are 7.3 µm2 . The high-power UV laser source used in this system provides a light flux density sufficient for bins of 8x8 pixels (21.6 µm x 21.6 µm) with dwell times as low 3 ms to reliably induce action potentials in 2.5 mM MNI-caged glutamate. At these settings the effective diameter of a glutamate uncaging site is \u3c 86 µm, which is equivalent to most other UV photostimulation rigs. With DLP photostimulation, sub-threshold responses and action potentials can be synchronously induced at thousands of sites over a 2.76 mm x 2.07 mm area, a capability unmatched by any other current system. This DLP-based system has the unique capability to investigate normal and diseased circuit properties by investigating neuronal responses to spatiotemporally complex activity patterns. This technique was used to investigate the temporal integration of synaptic input in the whisker barrel cortex of mice. The neocortex is organized into layers, with neuronal networks and circuits formed by layer-specific connections. While the anatomical organization of these circuits has been well characterized, the information processing and coding performed by these ensembles is poorly understood. A key component of this investigation concerns the transmission and transformation of the neuronal representation from one neuronal pool to the next. In the rodent somatosensory barrel cortex, histologically-distinguishable “barrels” in layer 4 (L4) receive principal input from a single whisker. L4 projects to layer II/III (L2/3), where the circuit diverges to multiple postsynaptic targets. Using the DLP-photostimulation system, we modulated the synchronicity of action potentials in L4 cells while recording from L2/3 in an acute slice preparation. This data shows that synchronous activity in L4 neurons is highly effective at eliciting strong spiking responses in L2/3 pyramidal cells, while asynchronous L4 activity fails to drive L2/3 to action-potential threshold. Pharmacological manipulation of the slice-bathing solution has suggested that this phenomenon is AMPA-receptor dependent and modulated by NMDA receptor activity. Intracellular pharmacological manipulations suggest that postsynaptic conductances also play a role in the nonlinear L2/3 synaptic integration of L4 activity

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

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

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.Center for Excellence for Learning Education, Science, and Technology (SBE-0354378); Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

Large-Scale simulations of plastic neural networks on neuromorphic hardware

SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models

An unsupervised neuromorphic clustering algorithm

Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, highly parallel operation of brains. However, to use these systems in applications, we need “neuromorphic algorithms” that can run on them. Here we develop a spiking neural network model for neuromorphic hardware that uses spike timing-dependent plasticity and lateral inhibition to perform unsupervised clustering. With this model, time-invariant, rate-coded datasets can be mapped into a feature space with a specified resolution, i.e., number of clusters, using exclusively neuromorphic hardware. We developed and tested implementations on the SpiNNaker neuromorphic system and on GPUs using the GeNN framework. We show that our neuromorphic clustering algorithm achieves results comparable to those of conventional clustering algorithms such as self-organizing maps, neural gas or k-means clustering. We then combine it with a previously reported supervised neuromorphic classifier network to demonstrate its practical use as a neuromorphic preprocessing module