A FREQUENCY RESPONSE NEURON MODEL: INTERACTIVE IMPLEMENTATION AND FURTHER SIMULATION EXPERIMENTS

Spiking behavior and epileptiform oscillations in a discrete model of cortical neural networks

The intrinsic electrophysiological properties of single neurons can be described by a broad spectrum of models, from realistic Hodgkin-Huxley-type models with numerous detailed mechanisms to the phenomenological models. The adaptive exponential integrate-and-fire (AdEx) model has emerged as a convenient middle-ground model. With a low computational cost but keeping biophysical interpretation of the parameters, it has been extensively used for simulations of large neural networks. However, because of its current-based adaptation, it can generate unrealistic behaviors. We show the limitations of the AdEx model, and to avoid them, we introduce the conductance-based adaptive exponential integrate-and-fire model (CAdEx). We give an analysis of the dynamics of the CAdEx model and show the variety of firing patterns it can produce. We propose the CAdEx model as a richer alternative to perform network simulations with simplified models reproducing neuronal intrinsic properties.

Simulation of spiking neural networks — architectures and implementations

Describes a scalable parallel architecture for real-time, large scale neural network simulations. Currently the SIMD architecture is largely adopted for implementing digital neurocomputers. However, it is not efficient for simulating large scale neural networks in real-time because of its limited scalability and flexibility. The authors investigate, as a solution, a wavefront array processing (WAP) architecture based on asynchronous communications. The authors compare both architectures in scalability, performance, and flexibility for simulating multi-layer perceptrons. The authors also briefly discuss implementing a high performance digital neurocomputer based on the WAP.

This paper presents a parallel algorithm for the simulation of large complex interconnect networks with frequency-dependent parameters. The equivalent transmission line model, which is suitable for time-domain simulation, is introduced for the modeling of dispersive interconnects. By means of the equivalent multi-port model, the interconnects are separated from other circuit elements, and thus makes the parallel simulation feasible. Based on the proposed technique, a program named FdSPICE is developed for parallel simulation of large interconnect networks. The accuracy and efficiency of FdSPICE are demonstrated by numerical examples.

We here describe SNNS, a neural network simulator for Unix workstations that we have been developing at the Universitat Stuttgart, Germany. Our network simulation environment is a tool to generate, train, test and visualize artificial neural networks. The simulator consists of three major components: a simulator kernel that operates on the internal representation ofthe neural networks, a graphical user interface based on X-Windows to interactively construct and change small neural nets,:ind a compiler to generate the internal representation of large neural networks from a high level network description language.Keywords: connectionism, neural networks, network simulators, network description language 1. INTRODUCTION AND MOTIVATION SNNS (Stuttgart Neural Network Simulator) is an efficient and portable neural network simulation environment for Unixworkstations that we have been developing at the Institute for Parallel and Distributed High Performance Systems, Universitht Stuttgart, Germany. It is an efficient software tool to generate, train, test and visualize artificial neural networks. The

To understand the mechanism of information processing by a biological neural network, computer simulation of a large-scale spiking neural network is an important method. However, because of a high computation cost of the simulation of a large-scale spiking neural network, the simulation requires high performance computing implemented by a supercomputer or a computer cluster. Recently, hardware for parallel computing such as a multi-core CPU and a graphics card with a graphics processing unit (GPU) is built in a gaming computer and a workstation. Thus, parallel computing using this hardware is becoming widespread, allowing us to obtain powerful computing power for simulation of a large-scale spiking neural network. However, it is not clear how much increased performance the parallel computing method using a new GPU yields in the simulation of a large-scale spiking neural network. In this study, we compared computation time between the computing methods with CPUs and GPUs in a simulation of neuronal models. We developed computer programs of neuronal simulations for the computing systems that consist of a gaming graphics card with new architecture (the NVIDIA GTX 1080) and an accelerator board using a GPU (the NVIDIA Tesla K20C). Our results show that the computing systems can perform a simulation of a large number of neurons faster than CPU-based systems. Furthermore, we investigated the accuracy of a simulation using single precision floating point. We show that the simulation results of single precision were accurate enough compared with those of double precision, but chaotic neuronal response calculated by a GPU using single precision is prominently different from that calculated by a CPU using double precision. Furthermore, the difference in chaotic dynamics appeared even if we used double precision of a GPU. In conclusion, the GPU-based computing system exhibits a higher computing performance than the CPU-based system, even if the GPU system includes data transfer from a graphics card to host memory.

Parallel processing is a promising technique for reducing the execution time of a large and complex wireless network simulation. In this article, a parallel processing technique is presented for time-driven simulations of large and complex wireless networks. The technique explicitly considers the physical-layer details of wireless network simulators such as shadowing and co-channel interference. The technique uses multiple processors interconnected with highspeed links. Principles and issues are elaborated, including workload partitioning, synchronization, and database design. Numerical results from two different wireless network simulators employing the technique are presented to demonstrate the performance of the technique. The results indicate that the technique can significantly improve the run-time performance of time-driven simulations of large and complicated wireless networks of different types.

Analysis of Japanese logistics development mode based on neural network and computer simulation is conducted in this research. The Japanese government provides strong financial support for green logistics and implements various green environmental protection tax preferential policies. For then referring this advanced feature, the listed aspects are analyzed. (1) During the training and learning process, the state of the neuron nodes in each layer only affects the signals of the neurons in the lower layer, which are gradually transmitted through multiple layers. (2) The simulation with the clustering model is analyzed. The simulation results are combined with efficient information analysis and mining. The experimental results compared with the other have proven the suggestions are reasonable.

Some applications of simulation require that the model state be advanced in simulation time than the wall-clock time advances as the simulation executes. This faster than requirement is crucial, for instance, when a simulation is used as part of a real-time control system, working through the consequences of contemplated control actions, in order to identify feasible (or even optimal) decisions. This paper considers the issue of than real-time simulation of very large communication networks, and how this is accomplished using our implementation (in C++) of the scalable simulation framework (SSF). Our tool (called iSSF) uses hierarchical levels of abstraction, and parallelism, to achieve speedups of nearly four orders of magnitude, enabling real-time execution rates on large network models. We quantify the effects that choice of hierarchical abstraction has on the simulation time advance rate, and show analytically and empirically how changing the abstraction mix affects performance.

Computational requirements in signal processing may cover a wide spectrum, from binary operations to floating point. A task-oriented approach to solving such complex problems may prove costly if the application domain is not specifically restricted. The present approach consists in combining performance speed-up through parallelism with enough flexibility in an attempt to cover a wide field of application. The paper provides a brief overview of a reconfigurable multiprocessor system, called Shiva, and an example of how a computationally intensive problem such as large neural network simulation can be mapped onto it. This is illustrated by a speech synthesis application. >

Slow update stochastic simulation algorithms for modeling complex biochemical networks

SummaryThe paper deals with the modeling of memristors operating in extremely large memristive networks such as crossbar structures for memory and computational circuits, memristor‐based neural networks or circuits for massively parallel analog computations. Because the non‐convergence and other numerical problems increase with increasing complexity of the simulated circuit, suitable models of the individual memristors need to be choicely developed and optimized. Three different models are considered, each representing a specific trade‐off between speed and accuracy. Benchmark circuits for testing the applications of various complexities are used for the transient analysis in HSPICE. It is shown how the models can be modified to minimize the simulation time and improve the convergence. Copyright © 2017 John Wiley & Sons, Ltd.

The rejection-based simulation technique has been applying to improve the computational efficiency of the stochastic simulation algorithm (SSA) in simulating large reaction networks, which are required for a thorough understanding of biological systems. We compare two recently proposed simulation methods, namely the composition-rejection algorithm (SSA-CR) and the rejection-based SSA (RSSA), aiming for this purpose. We discuss the right interpretation of the rejection-based technique used in these algorithms in order to make an informed choice when dealing with different aspects of biochemical networks. We provide the theoretical analysis as well as the detailed runtime comparison of these algorithms on concrete biological models. We highlight important factors that are omitted in previous analysis of these algorithms. The numerical comparison shows that for reaction networks where the search cost is expensive then SSA-CR is more efficient, and for reaction networks where the update cost is dominant, often the case in practice, then RSSA should be the choice.

Substantial evidence indicates that the time structure of neuronal spike trains is relevant in neuronal signal processing. Bio-inspired spiking neural networks are taking these results into account. Applications of these networks to low vision problems, e.g. segmentation, requires that the simulation of large-scale networks must be performed in a reasonable time. On this basis, we investigated the achievable performance of existing hardware platforms for the simulation of spiking neural networks with sizes from 8k neurons up to 512k neurons/50M synapses. We present results for workstations (Sparc-Ultra), digital signal processors (TMS-C8x), neurocomputers (CNAPS, SYNAPSE), small- and large-scale parallel-computers (4xPentium, CM-2, SP2) and discuss the specific implementation issues. According to our investigation, only supercomputers like CM-2 can match the performance requirements for the simulation of very large-scale spiking neural networks. Therefore, there is still. the need for low-cost hardware accelerators.