Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions.

Previous computational model-based approaches for understanding the dynamic changes related to Parkinson's disease made particular assumptions about Parkinson's disease-related activity changes or specified dopamine-dependent activation or learning rules. Inspired by recent model-based analysis of resting-state fMRI, we have taken a data-driven approach. We fit the free parameters of a spiking neurocomputational model to match correlations of blood oxygen level-dependent signals between different basal ganglia nuclei and obtain subject-specific neuro-computational models of two subject groups: Parkinson patients and matched controls. When comparing mean firing rates at rest and connectivity strengths between the control and Parkinsonian model groups, several significant differences were found that are consistent with previous experimental observations. We discuss the implications of our approach and compare its results also with the popular "rate model" of the basal ganglia. Our study suggests that a model-based analysis of imaging data from healthy and Parkinsonian subjects is a promising approach for the future to better understand Parkinson-related changes in the basal ganglia and corresponding treatments. K E Y W O R D S BOLD correlations, data fitting, firing rate, spiking neuron model How to cite this article: Maith O, Villagrasa Escudero F, Dinkelbach HÜ, et al. A computational model-based analysis of basal ganglia pathway changes in Parkinson's disease inferred from resting-state fMRI.

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Modern parallel hardware such as multi-core processors (CPUs) and graphics processing units (GPUs) have a high computational power which can be greatly beneficial to the simulation of large-scale neural networks. Over the past years, a number of efforts have focused on developing parallel algorithms and simulators best suited for the simulation of spiking neural models. In this article, we aim at investigating the advantages and drawbacks of the CPU and GPU parallelization of mean-firing rate neurons, widely used in systems-level computational neuroscience. By comparing OpenMP, CUDA and OpenCL implementations towards a serial CPU implementation, we show that GPUs are better suited than CPUs for the simulation of very large networks, but that smaller networks would benefit more from an OpenMP implementation. As this performance strongly depends on data organization, we analyze the impact of various factors such as data structure, memory alignment and floating precision. We then discuss the suitability of the different hardware depending on the networks' size and connectivity, as random or sparse connectivities in mean-firing rate networks tend to break parallel performance on GPUs due to the violation of coalescence.

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