Evidence map›Paper›PMID 27536234›Full record

ArticleFrontiers in neuroinformatics2016

Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS.

Philipp Weidel, Mikael Djurfeldt, Renato C Duarte, Abigail Morrison

Abstract read
In one paragraph

Article in Frontiers in neuroinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
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  4. Tutorial: using NEURON for neuromechanical simulations.Frontiers in computational neuroscience · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Philipp WeidelInstitute for Advanced Simulation, Theoretical Neuroscience and Institute of Neuroscience and Medicine, Computational and Systems Neuroscience and Jülich Aachen Research Alliance BRAIN Institute I, Jülich Research Center and Jülich Aachen Research Alliance Jülich, Germany.
Mikael DjurfeldtInstitute for Advanced Simulation, Theoretical Neuroscience and Institute of Neuroscience and Medicine, Computational and Systems Neuroscience and Jülich Aachen Research Alliance BRAIN Institute I, Jülich Research Center and Jülich Aachen Research AllianceJülich, Germany; PDC Center for High Performance Computing, KTH Royal Institute of TechnologyStockholm, Sweden.
Renato C DuarteInstitute for Advanced Simulation, Theoretical Neuroscience and Institute of Neuroscience and Medicine, Computational and Systems Neuroscience and Jülich Aachen Research Alliance BRAIN Institute I, Jülich Research Center and Jülich Aachen Research AllianceJülich, Germany; Faculty of Biology, Albert-Ludwig University of FreiburgFreiburg im Breisgau, Germany; Bernstein Center Freiburg, Albert-Ludwig University of FreiburgFreiburg im Breisgau, Germany.
Abigail MorrisonInstitute for Advanced Simulation, Theoretical Neuroscience and Institute of Neuroscience and Medicine, Computational and Systems Neuroscience and Jülich Aachen Research Alliance BRAIN Institute I, Jülich Research Center and Jülich Aachen Research AllianceJülich, Germany; Bernstein Center Freiburg, Albert-Ludwig University of FreiburgFreiburg im Breisgau, Germany; Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research CenterJülich, Germany; Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University BochumBochum, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC). This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning.

Indexed as

closed-loopneural network simulationsneuroroboticsreal-timerobotic simulations

Identifiers

PMID27536234
PMCPMC4971076

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.