ReviewFrontiers in neurorobotics2018
A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks.
Review in Frontiers in neurorobotics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed.
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- A Synchronization-Driven Learning Rule for Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.Research square · 2026Article
- Systematic Evaluation of Biologically Inspired Motion Detection Models: From LGMD and EMD to Hybrid Spiking Neural Networks.Biomimetics (Basel, Switzerland) · 2026Article
- Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- Learning-efficient spiking neural networks with multi-compartment spatio-temporal backpropagation.iScience · 2025Article
- Brain-inspired learning rules for spiking neural network-based control: a tutorial.Biomedical engineering letters · 2025Review
- A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns.Frontiers in computational neuroscience · 2024Article
- From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain sciences · 2023Review
- CBMC: A Biomimetic Approach for Control of a 7-Degree of Freedom Robotic Arm.Biomimetics (Basel, Switzerland) · 2023Article
- EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing.Sensors (Basel, Switzerland) · 2023Article
- Lessons from natural flight for aviation: then, now and tomorrow.The Journal of experimental biology · 2023Review
- A directionally selective collision-sensing visual neural network based on fractional-order differential operator.Frontiers in neurorobotics · 2023Article
- Model-free control for autonomous prevention of adverse events in robotics.Frontiers in robotics and AI · 2023Article
- An Overview of In Vitro Biological Neural Networks for Robot Intelligence.Cyborg and bionic systems (Washington, D.C.) · 2023Review
- Implementation of Kalman Filtering with Spiking Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Neuromorphic Tactile Edge Orientation Classification in an Unsupervised Spiking Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics.Patterns (New York, N.Y.) · 2022Article
- BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons.Frontiers in neuroscience · 2022Article
- A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods.Frontiers in neurorobotics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.
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.