Integrating Visualizations into Modeling NEST Simulations

Nowke, Christian; Zielasko, Daniel; Weyers, Benjamin; Peyser, Alexander; Hentschel, Bernd; Kuhlen, Torsten

doi:10.3389/fninf.2015.00029

Cited by 12 publications

(11 citation statements)

References 28 publications

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“…In this work, we demonstrate the advantages of neuromorphic computation by showing how an agent controlled by a spiking neural network (SNN) learns to solve a smooth pursuit task via reinforcement learning in a fully embedded perception-action loop that simulates the classic Pong video game on the BSS2 prototype. Measurements of time-to-convergence, power consumption, and sensitivity to parameter noise demonstrate the advantages of our neuromorphic solution compared to classical simulation on a modern CPU that runs the NEST simulator (Peyser et al, 2017). The on-chip learning converges within seconds, which is equivalent to hours in biological terms, while the software simulation is at least an order of magnitude slower and three orders of magnitude less energy-efficient.…”

Section: Introductionmentioning

confidence: 90%

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

et al. 2019

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Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play a simplified version of the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57 mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.

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Section: Introductionmentioning

confidence: 90%

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

et al. 2019

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“…Intrusive methods require changes to the underlying model equations and are often challenging to implement. Models in neuroscience are often created with the use of advanced simulators such as NEST (Peyser et al, 2017 ) and NEURON (Hines and Carnevale, 1997 ). Modifying the underlying equations of models using such simulators is a complicated task best avoided.…”

Section: Theory On Uncertainty Quantification and Sensitivity Analmentioning

confidence: 99%

“…NEST (Peyser et al, 2017 ) is a simulator for large networks of spiking neurons. NEST models are supported through the class, another subclass of :…”

Section: User Guide For Uncertainpymentioning

confidence: 99%

Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience

Tennøe

Halnes

Einevoll

2018

Front. Neuroinform.

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Computational models in neuroscience typically contain many parameters that are poorly constrained by experimental data. Uncertainty quantification and sensitivity analysis provide rigorous procedures to quantify how the model output depends on this parameter uncertainty. Unfortunately, the application of such methods is not yet standard within the field of neuroscience. Here we present Uncertainpy, an open-source Python toolbox, tailored to perform uncertainty quantification and sensitivity analysis of neuroscience models. Uncertainpy aims to make it quick and easy to get started with uncertainty analysis, without any need for detailed prior knowledge. The toolbox allows uncertainty quantification and sensitivity analysis to be performed on already existing models without needing to modify the model equations or model implementation. Uncertainpy bases its analysis on polynomial chaos expansions, which are more efficient than the more standard Monte-Carlo based approaches. Uncertainpy is tailored for neuroscience applications by its built-in capability for calculating characteristic features in the model output. The toolbox does not merely perform a point-to-point comparison of the “raw” model output (e.g., membrane voltage traces), but can also calculate the uncertainty and sensitivity of salient model response features such as spike timing, action potential width, average interspike interval, and other features relevant for various neural and neural network models. Uncertainpy comes with several common models and features built in, and including custom models and new features is easy. The aim of the current paper is to present Uncertainpy to the neuroscience community in a user-oriented manner. To demonstrate its broad applicability, we perform an uncertainty quantification and sensitivity analysis of three case studies relevant for neuroscience: the original Hodgkin-Huxley point-neuron model for action potential generation, a multi-compartmental model of a thalamic interneuron implemented in the NEURON simulator, and a sparsely connected recurrent network model implemented in the NEST simulator.

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“…The developed tool realizes a CMV system by applying principles of event-driven architectures as presented in Abram and Treinish ( 1995 ), Michelson ( 2006 ), and Nowke et al ( 2015 ). The development of the tool was organized into four stages: first, the simulation script was modified to retrieve electrical activity and connectivity values; second, the visualization components and user interfaces were developed; third, processing of parameter changes from the user interface was added; and finally, the simulation script was optimized to run on supercomputers.…”

Section: In Situ Visualization and Steering Of Connectivity mentioning

confidence: 99%

Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation

Nowke

Diaz-Pier

Weyers

et al. 2018

Front. Neuroinform.

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Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases—the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed.

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Integrating Visualizations into Modeling NEST Simulations

Cited by 12 publications

References 28 publications

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience

Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation

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