8,884 research outputs found
SIMPEL: Circuit model for photonic spike processing laser neurons
We propose an equivalent circuit model for photonic spike processing laser
neurons with an embedded saturable absorber---a simulation model for photonic
excitable lasers (SIMPEL). We show that by mapping the laser neuron rate
equations into a circuit model, SPICE analysis can be used as an efficient and
accurate engine for numerical calculations, capable of generalization to a
variety of different laser neuron types found in literature. The development of
this model parallels the Hodgkin--Huxley model of neuron biophysics, a circuit
framework which brought efficiency, modularity, and generalizability to the
study of neural dynamics. We employ the model to study various
signal-processing effects such as excitability with excitatory and inhibitory
pulses, binary all-or-nothing response, and bistable dynamics.Comment: 16 pages, 7 figure
SBML models and MathSBML
MathSBML is an open-source, freely-downloadable Mathematica package that facilitates working with Systems Biology Markup Language (SBML) models. SBML is a toolneutral,computer-readable format for representing models of biochemical reaction networks, applicable to metabolic networks, cell-signaling pathways, genomic regulatory networks, and other modeling problems in systems biology that is widely supported by the systems biology community. SBML is based on XML, a standard medium for representing and transporting data that is widely supported on the internet as well as in computational biology and bioinformatics. Because SBML is tool-independent, it enables model transportability, reuse, publication and survival. In addition to MathSBML, a number of other tools that support SBML model examination and manipulation are provided on the sbml.org website, including libSBML, a C/C++ library for reading SBML models; an SBML Toolbox for MatLab; file conversion programs; an SBML model validator and visualizer; and SBML specifications and schemas. MathSBML enables SBML file import to and export from Mathematica as well as providing an API for model manipulation and simulation
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models
This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant
2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
Paradoxical signaling regulates structural plasticity in dendritic spines
Transient spine enlargement (3-5 min timescale) is an important event
associated with the structural plasticity of dendritic spines. Many of the
molecular mechanisms associated with transient spine enlargement have been
identified experimentally. Here, we use a systems biology approach to construct
a mathematical model of biochemical signaling and actin-mediated transient
spine expansion in response to calcium-influx due to NMDA receptor activation.
We have identified that a key feature of this signaling network is the
paradoxical signaling loop. Paradoxical components act bifunctionally in
signaling networks and their role is to control both the activation and
inhibition of a desired response function (protein activity or spine volume).
Using ordinary differential equation (ODE)-based modeling, we show that the
dynamics of different regulators of transient spine expansion including CaMKII,
RhoA, and Cdc42 and the spine volume can be described using paradoxical
signaling loops. Our model is able to capture the experimentally observed
dynamics of transient spine volume. Furthermore, we show that actin remodeling
events provide a robustness to spine volume dynamics. We also generate
experimentally testable predictions about the role of different components and
parameters of the network on spine dynamics
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Spiking neural networks (SNN) are artificial computational models that have
been inspired by the brain's ability to naturally encode and process
information in the time domain. The added temporal dimension is believed to
render them more computationally efficient than the conventional artificial
neural networks, though their full computational capabilities are yet to be
explored. Recently, computational memory architectures based on non-volatile
memory crossbar arrays have shown great promise to implement parallel
computations in artificial and spiking neural networks. In this work, we
experimentally demonstrate for the first time, the feasibility to realize
high-performance event-driven in-situ supervised learning systems using
nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize
audio signals of alphabets encoded using spikes in the time domain and to
generate spike trains at precise time instances to represent the pixel
intensities of their corresponding images. Moreover, with a statistical model
capturing the experimental behavior of the devices, we investigate
architectural and systems-level solutions for improving the training and
inference performance of our computational memory-based system. Combining the
computational potential of supervised SNNs with the parallel compute power of
computational memory, the work paves the way for next-generation of efficient
brain-inspired systems
When kinases meet mathematics: the systems biology of MAPK signalling
The mitogen activated protein kinase/extracellular signal regulated kinase pathway regulates fundamental cellular function such as cell proliferation, survival, differentiation and motility, raising the question how these diverse functions are specified and coordinated. They are encoded through the activation kinetics of the pathway, a multitude of feedback loops, scaffold proteins, subcellular compartmentalisation, and crosstalk with other pathways. These regulatory motifs alone or in combination can generate a multitude of complex behaviour. Systems biology tries to decode this complexity through mathematical modelling and prediction in order to gain a deeper insight into the inner works of signalling networks
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
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