167 research outputs found
Environmental assessment of bioethanol from onshore grown Ulva
PosterBesides biofuels from microalgae, an emerging interest in using macroalgae as feedstock for biofuel production is observable. Macroalgae have the advantage that they are much easier to harvest than microalgae so that the problem of low feedstock concentration does not arise. The environmental performance of bioethanol from onshore grown green algae is assessed using literature data and initial laboratory scale data. The optimized system model allows for producing an environmentally efficient biofuel in comparison to fossil fuel and bioethanol from sugar cane. Handling the co-product by substitution instead of energy allocation significantly reduced the environmental impacts of the system and resulted in environmental bonuses in several impact categories. Thus, the management of the co-product in the LCA model (energy allocation vs. substitution) is a key step in the LCA, as it highly influences the impact assessment results
Non-equilibrium dynamics of stochastic point processes with refractoriness
Stochastic point processes with refractoriness appear frequently in the
quantitative analysis of physical and biological systems, such as the
generation of action potentials by nerve cells, the release and reuptake of
vesicles at a synapse, and the counting of particles by detector devices. Here
we present an extension of renewal theory to describe ensembles of point
processes with time varying input. This is made possible by a representation in
terms of occupation numbers of two states: Active and refractory. The dynamics
of these occupation numbers follows a distributed delay differential equation.
In particular, our theory enables us to uncover the effect of refractoriness on
the time-dependent rate of an ensemble of encoding point processes in response
to modulation of the input. We present exact solutions that demonstrate generic
features, such as stochastic transients and oscillations in the step response
as well as resonances, phase jumps and frequency doubling in the transfer of
periodic signals. We show that a large class of renewal processes can indeed be
regarded as special cases of the model we analyze. Hence our approach
represents a widely applicable framework to define and analyze non-stationary
renewal processes.Comment: 8 pages, 4 figure
A Fokker-Planck formalism for diffusion with finite increments and absorbing boundaries
Gaussian white noise is frequently used to model fluctuations in physical
systems. In Fokker-Planck theory, this leads to a vanishing probability density
near the absorbing boundary of threshold models. Here we derive the boundary
condition for the stationary density of a first-order stochastic differential
equation for additive finite-grained Poisson noise and show that the response
properties of threshold units are qualitatively altered. Applied to the
integrate-and-fire neuron model, the response turns out to be instantaneous
rather than exhibiting low-pass characteristics, highly non-linear, and
asymmetric for excitation and inhibition. The novel mechanism is exhibited on
the network level and is a generic property of pulse-coupled systems of
threshold units.Comment: Consists of two parts: main article (3 figures) plus supplementary
text (3 extra figures
PyNEST: A Convenient Interface to the NEST Simulator
The neural simulation tool NEST (http://www.nest-initiative.org) is a simulator for heterogeneous networks of point neurons or neurons with a small number of compartments. It aims at simulations of large neural systems with more than 104 neurons and 107 to 109 synapses. NEST is implemented in C++ and can be used on a large range of architectures from single-core laptops over multi-core desktop computers to super-computers with thousands of processor cores. Python (http://www.python.org) is a modern programming language that has recently received considerable attention in Computational Neuroscience. Python is easy to learn and has many extension modules for scientific computing (e.g. http://www.scipy.org). In this contribution we describe PyNEST, the new user interface to NEST. PyNEST combines NEST's efficient simulation kernel with the simplicity and flexibility of Python. Compared to NEST's native simulation language SLI, PyNEST makes it easier to set up simulations, generate stimuli, and analyze simulation results. We describe how PyNEST connects NEST and Python and how it is implemented. With a number of examples, we illustrate how it is used
Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework
MUSIC is a standard API allowing large scale neuron simulators to exchange data within a parallel computer during runtime. A pilot implementation of this API has been released as open source. We provide experiences from the implementation of MUSIC interfaces for two neuronal network simulators of different kinds, NEST and MOOSE. A multi-simulation of a cortico-striatal network model involving both simulators is performed, demonstrating how MUSIC can promote inter-operability between models written for different simulators and how these can be re-used to build a larger model system. Benchmarks show that the MUSIC pilot implementation provides efficient data transfer in a cluster computer with good scaling. We conclude that MUSIC fulfills the design goal that it should be simple to adapt existing simulators to use MUSIC. In addition, since the MUSIC API enforces independence of the applications, the multi-simulation could be built from pluggable component modules without adaptation of the components to each other in terms of simulation time-step or topology of connections between the modules
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