668 research outputs found
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
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
Mean Field description of and propagation of chaos in recurrent multipopulation networks of Hodgkin-Huxley and Fitzhugh-Nagumo neurons
We derive the mean-field equations arising as the limit of a network of
interacting spiking neurons, as the number of neurons goes to infinity. The
neurons belong to a fixed number of populations and are represented either by
the Hodgkin-Huxley model or by one of its simplified version, the
Fitzhugh-Nagumo model. The synapses between neurons are either electrical or
chemical. The network is assumed to be fully connected. The maximum
conductances vary randomly. Under the condition that all neurons initial
conditions are drawn independently from the same law that depends only on the
population they belong to, we prove that a propagation of chaos phenomenon
takes places, namely that in the mean-field limit, any finite number of neurons
become independent and, within each population, have the same probability
distribution. This probability distribution is solution of a set of implicit
equations, either nonlinear stochastic differential equations resembling the
McKean-Vlasov equations, or non-local partial differential equations resembling
the McKean-Vlasov-Fokker- Planck equations. We prove the well-posedness of
these equations, i.e. the existence and uniqueness of a solution. We also show
the results of some preliminary numerical experiments that indicate that the
mean-field equations are a good representation of the mean activity of a finite
size network, even for modest sizes. These experiment also indicate that the
McKean-Vlasov-Fokker- Planck equations may be a good way to understand the
mean-field dynamics through, e.g., a bifurcation analysis.Comment: 55 pages, 9 figure
Optimization and parallelization of tensor and ODE/PDE computations on GPU
We propose a multi-level GPU-based parallelization algorithm to solve the multi-compartment
Hodgkin Huxley (HH) model equation that requires solving the Hines matrix. We use
a ‘parallel-in-time’ algorithm (like the Parareal strategy) for obtaining outer level parallelism,
and an Exact Domain Decomposition (EDD) algorithm with fine-decomposition for
inner-level parallelism. We show that our technique can also be applied to any differential
equation like the heat equations which induce tridiagonal systems.
Typically, a solution to the HH equation runs for hundreds to tens of thousands of time-steps
while solving a Hines matrix at each time step. Previous solutions by Michael Mascagni
et al. (1991) and Hines et al. (2008) to this problem have tackled only solving the Hines
matrix in parallel.
Our approach uses the dynamic parallelism of CUDA to achieve multi-level parallelism
on GPUs. Our solution outperforms the sequential time method on standard neuron morphologies
upto 2.5x. We also show that iterative part of parareal method converges in 5-7
iterations on average with an accuracy of 10−6.
We also propose a GPU optimization for the Higher Order Tensor Renormalization Group
problem, where the tensor contraction operations inside HOTRG is optimized by a multi-
GPU implementation using cuBLAS xt API
Entrainment and chaos in a pulse-driven Hodgkin-Huxley oscillator
The Hodgkin-Huxley model describes action potential generation in certain
types of neurons and is a standard model for conductance-based, excitable
cells. Following the early work of Winfree and Best, this paper explores the
response of a spontaneously spiking Hodgkin-Huxley neuron model to a periodic
pulsatile drive. The response as a function of drive period and amplitude is
systematically characterized. A wide range of qualitatively distinct responses
are found, including entrainment to the input pulse train and persistent chaos.
These observations are consistent with a theory of kicked oscillators developed
by Qiudong Wang and Lai-Sang Young. In addition to general features predicted
by Wang-Young theory, it is found that most combinations of drive period and
amplitude lead to entrainment instead of chaos. This preference for entrainment
over chaos is explained by the structure of the Hodgkin-Huxley phase resetting
curve.Comment: Minor revisions; modified Fig. 3; added reference
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