Simulation of Large Scale Neural Models With Event-Driven Connectivity Generation

Accurate simulations of brain structures is a major problem in neuroscience. Many works are dedicated to design better models or to develop more efficient simulation schemes. In this paper, we propose a hybrid simulation scheme that combines time-stepping second-order integration of Hodgkin-Huxley (HH) type neurons with event-driven updating of the synaptic currents. As the HH model is a continuous model, there is no explicit spike events. Thus, in order to preserve the accuracy of the integration method, a spike detection algorithm is developed that accurately determines spike times. This approach allows us to regenerate the outgoing connections at each event, thereby avoiding the storage of the connectivity. Consequently, memory consumption is significantly reduced while preserving execution time and accuracy of the simulations, especially the spike times of detailed point neuron models. The efficiency of the method, implemented in the SiReNe software¹, is demonstrated by the simulation of a striatum model which consists of more than 10⁶ neurons and 10⁸ synapses (each neuron has a fan-out of 504 post-synaptic neurons), under normal and Parkinson's conditions.

Three-dimensional frequency-domain optical anisotropy imaging of biological tissues with near-infrared light

PURPOSE

Near-infrared optical imaging aims to reconstruct the absorption μ a and scattering μ s coefficients in order to detect tumors at early stage. However, the reconstructions have only been limited to μ a and μ s due to theoretical and computational limitations. The authors propose an efficient method of the reconstruction, in three-dimensional geometries, of the anisotropy factor g of the Henyey-Greenstein phase function as a new optical imaging biomarker.

METHODS

The light propagation in biological tissues is accurately modeled by the radiative transfer equation (RTE) in the frequency-domain. The reconstruction algorithm is based on a gradient-based updating scheme. The adjoint method is used to efficiently compute the gradient of the objective function which represents the discrepancy between simulated and measured boundary data. A parallel implementation is carried out to reduce the computational time.

RESULTS

We show that by illuminating only one surface of a tissue-like phantom, the algorithm is able to accurately reconstruct optical values and different shapes (spherical and cylindrical) that characterize small tumor-like inclusions. Numerical simulations show the robustness of the algorithm to reconstruct the anisotropy factor with different contrast levels, inclusion depths, initial guesses, heterogeneous background, noise levels, and two-layered medium. The crosstalk problem when reconstructing simultaneously μ s and g has been reported and achieved with a reasonable quality.

CONCLUSIONS

The proposed RTE-based reconstruction algorithm is robust to spatially retrieve and localize small tumoral inclusions. Heterogeneities in g-factor have been accurately reconstructed which makes the new algorithm a candidate of choice to image this factor as new intrinsic contrast biomarker for optical imaging.

Radiative transfer equation for predicting light propagation in biological media: comparison of a modified finite volume method, the Monte Carlo technique, and an exact analytical solution

We examine the accuracy of a modified finite volume method compared to analytical and Monte Carlo solutions for solving the radiative transfer equation. The model is used for predicting light propagation within a two-dimensional absorbing and highly forward-scattering medium such as biological tissue subjected to a collimated light beam. Numerical simulations for the spatially resolved reflectance and transmittance are presented considering refractive index mismatch with Fresnel reflection at the interface, homogeneous and two-layered media. Time-dependent as well as steady-state cases are considered. In the steady state, it is found that the modified finite volume method is in good agreement with the other two methods. The relative differences between the solutions are found to decrease with spatial mesh refinement applied for the modified finite volume method obtaining <2.4%. In the time domain, the fourth-order Runge-Kutta method is used for the time semi-discretization of the radiative transfer equation. An agreement among the modified finite volume method, Runge-Kutta method, and Monte Carlo solutions are shown, but with relative differences higher than in the steady state.

A Load Balanced Parallel Ground Visualization Tool

We propose in this paper, a parallel implementation of a ground visualization algorithm. Our input data consist in a Digital Elevation Model (DEM) covering a rectangular region, together with a raster image of the same area (the texture). The goal of the algorithm is to compute in parallel, images of the DEM from any point of view while mapping the texture onto the surface. The main originality of our approach concerns the distribution of the data, leading to a load-balanced and scalable parallel algorithm. We use a workload estimation to partition the output image, and then redistribute the input data according to this division. Special attention is paid on the data structures used for minimizing the cost of communications.

Corrections to "basins of attraction in fully asynchronous discrete-time discrete-state dynamic networks"

This paper brings a correction to the formulation of the basins of fixed-point states of fully asynchronous discrete-time discrete-state dynamic networks presented in our paper that appeared in the IEEE Transactions on Neural Networks, vol. 17, no. 2, pp. 397-408, March 2006. In our subsequent works on totally asynchronous systems, we have discovered that the formulation given in that previous paper lacks an additional condition. We present in this paper why the previous formulation is incomplete and give the correct formulation.

Basins of attraction in fully asynchronous discrete-time discrete-state dynamic networks

This paper gives a formulation of the basins of fixed point states of fully asynchronous discrete-time discrete-state dynamic networks. That formulation provides two advantages. The first one is to point out the different behaviors between synchronous and asynchronous modes and the second one is to allow us to easily deduce an algorithm which determines the behavior of a network for a given initialization. In the context of this study, we consider networks of a large number of neurons (or units, processors, etc.), whose dynamic is fully asynchronous with overlapping updates. We suppose that the neurons take a finite number of discrete states and that the updating scheme is discrete in time. We make no hypothesis on the activation functions of the nodes, so that the dynamic of the network may have multiple cycles and/or basins. Our results are illustrated on a simple example of a fully asynchronous Hopfield neural network.

Calculations of dose distributions using a neural network model

The main goal of external beam radiotherapy is the treatment of tumours, while sparing, as much as possible, surrounding healthy tissues. In order to master and optimize the dose distribution within the patient, dosimetric planning has to be carried out. Thus, for determining the most accurate dose distribution during treatment planning, a compromise must be found between the precision and the speed of calculation. Current techniques, using analytic methods, models and databases, are rapid but lack precision. Enhanced precision can be achieved by using calculation codes based, for example, on Monte Carlo methods. However, in spite of all efforts to optimize speed (methods and computer improvements), Monte Carlo based methods remain painfully slow. A newer way to handle all of these problems is to use a new approach in dosimetric calculation by employing neural networks. Neural networks (Wu and Zhu 2000 Phys. Med. Biol. 45 913-22) provide the advantages of those various approaches while avoiding their main inconveniences, i.e., time-consumption calculations. This permits us to obtain quick and accurate results during clinical treatment planning. Currently, results obtained for a single depth-dose calculation using a Monte Carlo based code (such as BEAM (Rogers et al 2003 NRCC Report PIRS-0509(A) rev G)) require hours of computing. By contrast, the practical use of neural networks (Mathieu et al 2003 Proceedings Journees Scientifiques Francophones, SFRP) provides almost instant results and quite low errors (less than 2%) for a two-dimensional dosimetric map.