Simulation Of Large Neural Networks Research Articles

Scalability of Large Neural Network Simulations via Activity Tracking With Time Asynchrony and Procedural Connectivity.

We present a new algorithm to efficiently simulate random models of large neural networks satisfying the property of time asynchrony. The model parameters (average firing rate, number of neurons, synaptic connection probability, and postsynaptic duration) are of the order of magnitude of a small mammalian brain or of human brain areas. Through the use of activity tracking and procedural connectivity (dynamical regeneration of synapses), computational and memory complexities of this algorithm are proved to be theoretically linear with the number of neurons. These results are experimentally validated by sequential simulations of millions of neurons and billions of synapses running in a few minutes using a single thread of an equivalent desktop computer.

Conductance-Based Adaptive Exponential Integrate-and-Fire Model.

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.

Two dimensional model of learning in spiking neural networks with homeostasis and reward

The huge complexity of molecular mechanisms that support memory formation makes it difficult to build simple, but precise and sufficient models for an efficient simulation of large neural networks. In this paper, we propose the phenomenological model of a learning rule that describes the synaptic strength via slow and fast variables. Two variables interact with each other in a bidirectional manner that allows to combine the reward and unsupervised learning. Results show the stability of synaptic strength due to coupling of two variables and fast homeostatic plasticity. The multiplicative approach of synaptic scaling preserves memory patterns of statistically more frequent input signals. Similar to the eligibility traces approach, the model tracks recent synaptic changes and allows to reinforce these changes. Also, we speculate on a possible biophysical interpretation of such a model that includes the fast movement of receptors to the membrane and their stabilization into clusters.

Image-based configuration and interaction for large neural network simulations

Background Large neural network simulations are becoming more complex to set up. They require modeling at multiple scales, include the effects of many interacting physical processes, encompass greater detail, and consume greater computational resources. The drive to solve problems that rely on increasingly complex codes will soon land us in the realm of petascale computing. How will we manage such simulations, configure them, and accurately aim them at the problems we're trying to solve? Simulation is an increasingly expensive process, with each run providing data to inform configuration and targeting of subsefrom Sixteenth Annual Computational Neuroscience Meeting: CNS*2007 Toronto, Canada. 7–12 July 2007

LARGE NEURAL NETWORK: OBJECT MODELING AND PARALLEL SIMULATION

We propose an object oriented model for the simulation of large neural networks using the OMT technique. This modeling has been implemented on a client-server parallel simulator (called NeuSim-NNLIB), the server being a "beowulf" cluster, getting up to 18 MCPS with a cluster of 6 Pentium processors. We also present an estimation for simulator performance and optimal processors number.

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

We investigate the phenomenon of epileptiform activity using a discrete model of cortical neural networks. Our model is reduced to the elementary features of neurons and assumes simplified dynamics of action potentials and postsynaptic potentials. The discrete model provides a comparably high simulation speed which allows the rendering of phase diagrams and simulations of large neural networks in reasonable time. Further the reduction to the basic features of neurons provides insight into the essentials of a possible mechanism of epilepsy. Our computer simulations suggest that the detailed dynamics of postsynaptic and action potentials are not indispensable for obtaining epileptiform behavior on the system level. The simulation results of autonomously evolving networks exhibit a regime in which the network dynamics spontaneously switch between fluctuating and oscillating behavior and produce isolated network spikes without external stimulation. Inhibitory neurons have been found to play an important part in the synchronization of neural firing: an increased number of synapses established by inhibitory neurons onto other neurons induces a transition to the spiking regime. A decreased frequency accompanying the hypersynchronous population activity has only occurred with slow inhibitory postsynaptic potentials.

A parallel algorithm for simulation of large neural networks

The simulation of biologically realistic neural networks requires the numerical solution of very large systems of differential equations. Variables within the system can be changing at rates that vary by orders of magnitude, not only at different times of the solution, but at the same time in different parts of the network. Therefore, an efficient implementation must be able to vary the solution step size, and do so independently in different subsystems. A single processor algorithm is presented in which each neuron can be solved with its own step size by using a priority queue to integrate them in the correct order. But this leaves the problem of how communication and synchronisation between neurons should be managed when executing in parallel. The proposed solution uses an algorithm based on waveform relaxation, which allows groups of neurons on different processors to be solved independently and hence in parallel, for substantial parts of the computation. Realistic test problems were run on a distributed memory parallel computer and results show that speedups of 10 using 16 processors are achievable, and indicate that further speedups may be possible.

Large neural network simulations on multiple hardware platforms.

To efficiently simulate very large networks of interconnected neurons, particular consideration has to be given to the computer architecture being used. This article presents techniques for implementing simulators for large neural networks on a number of different computer architectures. The neuronal simulation task and the computer architectures of interest are first characterized, and the potential bottlenecks are highlighted. Then we describe the experience gained from adapting an existing simulator, SWIM, to two very different architectures-vector computers and multiprocessor workstations. This work lead to the implementation of a new simulation library, SPLIT, designed to allow efficient simulation of large networks on several architectures. Different computer architectures put different demands on the organization of both data structures and computations. Strict separation of such architecture considerations from the neuronal models and other simulation aspects makes it possible to construct both portable and extendible code.

The neural shell: A neural network simulation tool

This paper presents a neural network simulator, the Neural Shell, which was developed in order to simplify the design and use of neural networks. The Neural Shell is a prototyping and simulation environment which runs on Unix workstations. Additionally, the Neural Shell supports the execution of large neural-network simulations on Cray Supercomputers.

Design of low-cost, real-time simulation systems for large neural networks

Systems with large amounts of computing power and storage are required to simulate very large neural networks capable of tackling complex control problems and real-time emulation of the human sensory, language, and reasoning systems. General-purpose parallel computers do not have communications, processor, and memory architectures optimized for neural computation and so cannot perform such simulations at reasonable cost. This paper analyzes several software and hardware strategies to make feasible the simulation of large neural networks in real-time and presents a particular multicomputer design able to implement these strategies. An important design goal is that the system must not sacrifice computational flexibility for speed as new information about the workings of the brain and new artificial neural network architectures and learning algorithms are continually emerging.

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

This paper reports further development and applications of a computational model permitting analysis of frequency response characteristics of simulated neurons. The original single‐neuron model, as described by Peddicord and Sampson in 1974, is briefly reviewed here. There follows a thorough description of a new interactive implementation of the model on a PDP‐11/45. The implementation permits high‐speed simulation of large neural networks, and is designed for easy use by investigators with no special knowledge of computers or programming. Experiments with the new system have included the design of improved bandpass filters, pacemaker neurons and networks, and a cerebellar circuit. Simulation results are presented and, in the last case, compared with available physiological data. The paper concludes with a critique of the system, suggestions for further experiments, and a comparison with the simulation system developed by Perkel. Two appendices document the system data structures and show a sample simulation run.