ArticleFrontiers in neuroinformatics2015
ANNarchy: a code generation approach to neural simulations on parallel hardware.
Article in Frontiers in neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed.
- Article
- In silico model of basal ganglia deep brain stimulation in Parkinson's disease captures range of effective parameters for pathological beta power suppression.PLoS computational biology · 2026Article
- Evolving spiking neural networks: the role of neuron models and encoding schemes in neuromorphic learning.Frontiers in neuroscience · 2026Article
- Perisaccadic Attentional Updating in Area V4: A Neurocomputational Approach.The European journal of neuroscience · 2025Article
- Bridging Conflicting Views on Eye Position Signals: A Neurocomputational Approach to Perisaccadic Perception: Eye Position Information in Brain and Model.The European journal of neuroscience · 2025Article
- Climbing fibers selectively recruit disinhibitory interneurons to enhance dendritic calcium signaling in cerebellar Purkinje cells.bioRxiv : the preprint server for biology · 2025Article
- Pallidal Deep Brain Stimulation Enhances Habitual Behavior in a Neuro-Computational Basal Ganglia Model During a Reward Reversal Learning Task.The European journal of neuroscience · 2025Article
- Neuromorphic computing at scale.Nature · 2025Review
- Multiscale co-simulation design pattern for neuroscience applications.Frontiers in neuroinformatics · 2024Article
- Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.Frontiers in neuroinformatics · 2024Article
- Article
- PyRates-A code-generation tool for modeling dynamical systems in biology and beyond.PLoS computational biology · 2023Article
- From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain sciences · 2023Review
- SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber-Physical Systems.Biomimetics (Basel, Switzerland) · 2023Article
- Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model.iScience · 2023Article
- The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.PLoS computational biology · 2023Article
- SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.Frontiers in neuroscience · 2023Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
- Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons.eLife · 2022Article
- Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster.Frontiers in neuroinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.