4 research outputs found
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Analysis of iteration schemes for deterministic transport in binary Markovian mixtures
The Adams-Larsen-Pomraning coupled transport model has been used to describe neutral particle transport in binary stochastic mixtures. Here, the mixing statistics are considered to be homogeneous Markovian processes. While the model is robust, the convergence behavior and efficiency of this coupled model have not been addressed. Countless iterative methods could be employed to solve the coupled model. In this study, three candidate iterative schemes are analyzed with the Fourier analysis technique. The schemes are tested and implemented with a variety of material data to observe the convergence behaviors. While two schemes appear to be stable and convergent, both can converge slowly in the presence of scattering. We develop a two-grid acceleration scheme to improve the convergence rate of the fully-implicit iteration scheme. A shape function from the high-order coupled transport equations (fine-grid) is used to collapse cross-sections for an effectively-mixed one-material transport approximation (coarse-grid). In turn, diffusion synthetic acceleration is applied to the coarse-grid transport operator in the event that the effective scattering ratio is near unity. Theoretical and computational results indicate that this two-grid acceleration technique is highly efficient and effective for improving the convergence rate
Profiling the tyrosine phosphoproteome of different mouse mammary tumour models reveals distinct, model-specific signalling networks and conserved oncogenic pathways
25th annual computational neuroscience meeting: CNS-2016
The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong