ArticleFrontiers in neuroscience2020
Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A Synchronization-Driven Learning Rule for Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.Research square · 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
- Exploring spiking neural networks for deep reinforcement learning in robotic tasks.Scientific reports · 2024Article
- Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns.Frontiers in computational neuroscience · 2024Article
- Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.Cognitive computation · 2023Article
- Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing.Discover nano · 2023Review
- EXODUS: Stable and efficient training of spiking neural networks.Frontiers in neuroscience · 2023Article
- Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot.Frontiers in neurorobotics · 2023Article
- Direct learning-based deep spiking neural networks: a review.Frontiers in neuroscience · 2023Review
- TiN/Ti/HfOFrontiers in neuroscience · 2023Article
- Toward Reflective Spiking Neural Networks Exploiting Memristive Devices.Frontiers in computational neuroscience · 2022Review
- Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance.Frontiers in neuroscience · 2022Article
- Adaptive SNN for Anthropomorphic Finger Control.Sensors (Basel, Switzerland) · 2021Article
- Spatial Memory in a Spiking Neural Network with Robot Embodiment.Sensors (Basel, Switzerland) · 2021Article
- A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware.Frontiers in neuroscience · 2021Article
- Integrated Information in the Spiking-Bursting Stochastic Model.Entropy (Basel, Switzerland) · 2020Article
- Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application.Nanomaterials (Basel, Switzerland) · 2020Review
- Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics.Frontiers in neuroscience · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence. Such networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The critical problem is in the design of robust learning algorithms aimed at building a "living computer" based on SNNs. Here, we propose a simple SNN equipped with a Hebbian rule in the form of spike-timing-dependent plasticity (STDP). The SNN implements associative learning by exploiting the spatial properties of STDP. We show that a LEGO robot controlled by the SNN can exhibit classical and operant conditioning. Competition of spike-conducting pathways in the SNN plays a fundamental role in establishing associations of neural connections. It replaces the irrelevant associations by new ones in response to a change in stimuli. Thus, the robot gets the ability to relearn when the environment changes. The proposed SNN and the stimulation protocol can be further enhanced and tested in developing neuronal cultures, and also admit the use of memristive devices for hardware implementation.
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