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

Neural Circuits01:25

Neural Circuits

3.2K
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...
3.2K
Neuroplasticity01:01

Neuroplasticity

2.4K
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.
2.4K
Propagation of Action Potentials01:23

Propagation of Action Potentials

13.5K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
13.5K
Integration of Synaptic Events01:28

Integration of Synaptic Events

5.7K
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 to...
5.7K
Long-term Potentiation01:35

Long-term Potentiation

59.4K
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.
59.4K

You might also read

Related Articles

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

Sort by
Same author

Catecholamine precursor modulation of human exploration: Evidence from a large gender-balanced sample.

PLoS computational biology·2026
Same author

The earlier you know, the smoother you act: anticipatory control in solo and dyadic juggling.

Experimental brain research·2026
Same author

Exploration Strategies and Feature Prioritisation in Contour-based Haptic Perception of 2D Shape.

IEEE transactions on haptics·2026
Same author

Open science practices in behavioral addictions: An exploratory survey.

Journal of behavioral addictions·2026
Same author

[Use of continuous passive motion in inpatient rehabilitation after shoulder replacement-a retrospective study].

Orthopadie (Heidelberg, Germany)·2026
Same author

Hoffa-Kastert Syndrome: A Rare Cause of Acute Knee Blockade.

Indian journal of orthopaedics·2025
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Mar 25, 2026

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

11.0K

Recurrent Spiking Networks Solve Planning Tasks.

Elmar Rueckert1, David Kappel2, Daniel Tanneberg1

  • 1Intelligent Autonomous Systems Lab, Technische Universität Darmstadt, 64289, Germany.

Scientific Reports
|February 19, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel spiking neural network model for planning tasks, framing it as probabilistic inference. The model demonstrates efficient learning and potential for neuromorphic hardware, enabling robots to plan and avoid obstacles.

More Related Videos

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

7.7K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.3K

Related Experiment Videos

Last Updated: Mar 25, 2026

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

11.0K
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

7.7K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.3K

Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Robotics

Background:

  • Planning is crucial for intelligent behavior, but computationally demanding.
  • Existing models often struggle with complex, long-term, or uncertain tasks.
  • Spiking neural networks offer a biologically plausible and potentially efficient alternative.

Purpose of the Study:

  • To develop a recurrent spiking neural network (RSNN) for planning as probabilistic inference.
  • To investigate reward-modulated plasticity rules for optimizing planning strategies.
  • To explore the model's applicability in robotic tasks and compare its dynamics to biological systems.

Main Methods:

  • Proposed an RSNN architecture separating task dynamics and reward optimization.
  • Introduced a general class of reward-modulated plasticity rules based on Expectation Maximization.
  • Tested the model on a simulated robot arm reaching and obstacle avoidance task.
  • Analyzed network dynamics and compared them to hippocampal activity in rats.

Main Results:

  • The RSNN successfully implemented planning as probabilistic inference for finite and infinite horizon tasks.
  • Learning converged to a local maximum, optimizing reward likelihood.
  • Network dynamics showed qualitative similarities to hippocampal firing patterns during planning and foraging.
  • The model demonstrated the ability to represent multiple task solutions in a robotic manipulation task.

Conclusions:

  • The proposed RSNN model offers a novel approach to planning, integrating probabilistic inference with biologically plausible learning rules.
  • The model provides a foundation for neuromorphic hardware implementations, with potential for efficient, adaptive robotic control.
  • Future work could explore advanced applications in human-robot interaction and complex decision-making scenarios.