Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.1K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.1K
Neural Circuits01:25

Neural Circuits

1.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.0K
Integration of Synaptic Events01:28

Integration of Synaptic Events

1.4K
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability...
1.4K
Neuronal Communication01:28

Neuronal Communication

777
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
777
Neuroplasticity01:01

Neuroplasticity

284
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
284
Long-term Potentiation01:25

Long-term Potentiation

2.7K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
2.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Fall-from-Bed Risk Prediction Using Physics-Based Bed Simulation.

Sensors (Basel, Switzerland)·2026
Same author

Nanoscale Dynamics of Buried Charge Trap in Oxide-Nitride-Oxide Stacks Investigated Using Kelvin Probe Force Microscopy.

Nano letters·2025
Same author

Fatigue life predictor: predicting fatigue life of metallic material using LSTM with a contextual attention model.

RSC advances·2025
Same author

Deep Neural Network-Based Empirical Mode Decomposition for Motor Imagery EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024
Same author

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.

Sensors (Basel, Switzerland)·2024
Same author

Regulated Phase Separation in Al-Ti-Cu-Co Alloys through Spark Plasma Sintering Process.

Materials (Basel, Switzerland)·2024
Same journal

Multi-Scale convolutional neural networks integrated with self-attention for motor imagery EEG decoding.

Biomedical engineering letters·2026
Same journal

Low-power analog and mixed-signal circuit techniques for next-generation miniature implantable neural interface systems.

Biomedical engineering letters·2026
Same journal

Advances in semiconductor materials and device architectures for biomedical systems: a mini review.

Biomedical engineering letters·2026
Same journal

A Multi-perception fusion using shared-control method for brain-mobile robot.

Biomedical engineering letters·2026
Same journal

SSA-DCNet: a cross-session MI-EEG classification network based on deformable convolution and spatial-shift attention.

Biomedical engineering letters·2026
Same journal

Advanced silicon nanomembrane based bioelectronics for flexible and stretchable implantable systems.

Biomedical engineering letters·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.0K

Brain-inspired learning rules for spiking neural network-based control: a tutorial.

Choongseop Lee1, Yuntae Park1, Sungmin Yoon1

  • 1Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Republic of Korea.

Biomedical Engineering Letters
|January 9, 2025
PubMed
Summary
This summary is machine-generated.

Spiking neural networks offer an energy-efficient alternative to deep neural networks for robotic control. This review explores brain-inspired learning rules for spiking neural networks to enhance real-time spatio-temporal processing in robots.

Keywords:
Control problemNeuromorphic computingR-STDPReinforcement learningSpike-timing-dependent plasticitySpiking neural networks

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.0K

Related Experiment Videos

Last Updated: Jun 3, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.0K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.0K

Area of Science:

  • Robotics
  • Neuroscience
  • Computer Science

Background:

  • Deep neural networks (DNNs) have improved robotic control but suffer from high energy consumption and latency due to their complexity.
  • Real-time data processing in robotic systems is hindered by the computational demands of complex DNNs.
  • Spiking neural networks (SNNs), inspired by biological brains, process information via spikes, offering a potential solution for efficient, real-time control.

Purpose of the Study:

  • To review brain-inspired learning rules for SNNs.
  • To examine the application of SNNs in solving robotic control tasks.
  • To explore advancements in learning rules, including spike-timing-dependent plasticity (STDP) and third-factor learning.

Main Methods:

  • Review of biologically plausible learning rules, focusing on STDP.
  • Investigation of global and local third-factor learning mechanisms integrated with STDP.
  • Analysis of weight-based backpropagation for synaptic weight modification in SNNs.

Main Results:

  • SNNs, particularly with advanced learning rules, demonstrate potential for efficient spatio-temporal information processing.
  • Third-factor learning, both global and local, enhances STDP's effectiveness in SNNs.
  • These learning rules enable SNNs to tackle complex control tasks in robotic systems.

Conclusions:

  • SNNs and their brain-inspired learning rules present a viable, energy-efficient alternative to DNNs for robotic control.
  • Further research into STDP and third-factor learning can unlock greater potential for SNNs in real-time applications.
  • The reviewed methods provide a foundation for developing sophisticated SNN-based control systems for robotics.