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Publications of Computational Neurophysics

Selected publications of Computational Neurophysics

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. Read the full Paper:: Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator …

Constructing Neuronal Network Models in Massively Parallel Environments

Constructing Neuronal Network Models in Massively Parallel Environments

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers. Read the full Paper:: Constructing Neuronal Network Models in Massively Parallel Environments …

Paper JS 20170219

A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC

Workflows for the acquisition and analysis of data in the natural sciences exhibit a growing degree of complexity and heterogeneity, are increasingly performed in large collaborative efforts, and often require the use of high-performance computing (HPC). Here, we explore the reasons for these new challenges and demands and discuss their impact with a focus on the scientific domain of computational neuroscience. Read the full Paper:: A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC …

Fundamental Activity...

Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome

The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Read the full Paper:: Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome …

Paper MD 20161211

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Read the full Paper:: Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks …

Paper MD 20161211

Including Gap Junctions into Distributed Neuronal Network Simulations. In: Amunts K, Grandinetti L, Lippert T, Petkov N. (eds) Brain Inspired Computing, Brain Comp 2015

Contemporary simulation technology for neuronal networks enables the simulation of brain-scale networks using neuron models with a single or a few compartments. However, distributed simulations at full cell density are still lacking the electrical coupling between cells via so called gap junctions. This is due to the absence of efficient algorithms to simulate gap junctions on large parallel computers. The difficulty is that gap junctions require an instantaneous interaction between the coupled neurons, whereas the efficiency of simulation codes for spiking neurons relies on delayed communication. Read the full Paper:: Including Gap Junctions into Distributed Neuronal Network Simulations. In: Amunts K, Grandinetti L, Lippert T, Petkov N. (eds) Brain Inspired Computing, Brain Comp 2015 …

High Performance

High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination

Opportunities offered by new neuro-technologies are threatened by lack of coherent plans to analyze, manage, and understand the data. High-performance computing will allow exploratory analysis of massive datasets stored in standardized formats, hosted in open repositories, and integrated with simulations. Read the full Paper:: High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination …

Diesmann, Albada, Maksimov [Rescience]

[Re] Cellular and Network Mechanisms of Slow Oscillatory Activity (<1 Hz) and Wave Propagations in a Cortical Network Model

We provide an implementation of the model of [1], which reproduces single-neuron and collective network behaviors during slow-wave oscillations in vitro in control conditions and under pharmacological manipulations. In particular, we focus onthe authors’ model results that include: (a) neuronal membrane potentials oscillating between Up and Down states at <1 Hz; (b) characteristic membrane resistance behavior and activation of neuronal ion channels with proportional excitation andinhibition during Up states; (c) spontaneous and stimulus-evoked initiation and further wave-like propagation of population spiking activity. The original implementation is in C++, but the source code is not publicly available. The implementation wepropose is coded in the NEST [5] framework, one of the modern actively developed simulation platforms that is publicly available. The code uses the Python interface [4] for legibility. The model and analysis scripts are implemented using Python3.5.2, and also tested with Python 2.7.6. Read the full Paper:: [Re] Cellular and Network Mechanisms of Slow Oscillatory Activity (<1 Hz) and Wave Propagations in a Cortical Network Model …

Identifiying Anatomical...

Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit

Oscillations are omnipresent in neural population signals, like multi-unit recordings, EEG/MEG, and the local field potential. They have been linked to the population firing rate of neurons, with individual neurons firing in a close-to-irregular fashion at low rates. Using a combination of mean-field and linear response theory we predict the spectra generated in a layered microcircuit model of V1, composed of leaky integrate-and-fire neurons and based on connectivity compiled from anatomical and electrophysiological studies. The model exhibits low- and high-γ oscillations visible in all populations. Since locally generated frequencies are imposed onto other populations, the origin of the oscillations cannot be deduced from the spectra. We develop an universally applicable systematic approach that identifies the anatomical circuits underlying the generation of oscillations in a given network. Read the full Paper:: Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit …

Paper MD 20160606

Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality

Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. Read the full paper: Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality …

Paper MD 20160518

Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study

High-level brain function, such as memory, classification, or reasoning, can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy-efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. Read the full Paper:: Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study …

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