Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neuroplasticity

Neuroplasticity

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.

Long-term Potentiation

Long-term Potentiation

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.

Neural Circuits

Neural Circuits

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...

You might also read

Related Articles

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

Sort by

Same author

Cross-subject fMRI-to-Image with Visual-cortex 2D Representation and Pre-Training.

IEEE journal of biomedical and health informatics·2026

Same author

Shifts in the brain sex continuum in major depressive disorder: Evidence for a persistent neurobiological marker.

Journal of affective disorders·2026

Same author

The complement C3-microglial axis in depression of Parkinson's disease: from mechanism to therapeutic intervention.

EBioMedicine·2026

Same author

Sex differences in activations to the sight of faces, scenes, body parts and tools in visual and non-visual cortical regions leading to the human hippocampus.

Biology of sex differences·2026

Same author

A hierarchical multi-scale framework for schizophrenia: integrating symptom networks, functional circuits, and molecular pathways.

Molecular psychiatry·2026

Same author

Latent neural architecture organising shared aesthetic evaluations of visual artworks.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Jul 1, 2026

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

Training spiking neuronal networks with applications in engineering tasks.

Phill Rowcliffe¹, Jianfeng Feng

¹Department of Informatics, School of Science and Technology, University of Sussex, Brighton, East Sussex BN1 9QH, UK. phillipr@sussex.ac.uk

IEEE Transactions on Neural Networks

|September 10, 2008

Summary

This study introduces novel spiking neuronal models for computation using statistical properties. These models enable robust learning rules for complex networks and demonstrate success in engineering tasks like function approximation and robot control.

More Related Videos

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

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

Published on: March 2, 2015

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

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

Published on: November 12, 2019

Related Experiment Videos

Last Updated: Jul 1, 2026

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

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

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

Published on: March 2, 2015

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

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

Published on: November 12, 2019

Area of Science:

Computational Neuroscience
Artificial Intelligence
Machine Learning

Background:

Spiking neuronal models are crucial for understanding brain function and developing efficient AI.
Current models often lack robust mathematical frameworks for learning and complex computations.

Purpose of the Study:

To introduce novel spiking neuronal models utilizing statistical properties (mean, variance, correlation) for computation.
To present two design approaches for spiking neuronal networks applicable to engineering tasks.
To define mathematically robust learning rules for these networks.

Main Methods:

Developed spiking neuronal models based on statistical properties.
Explored the input-output relationship of integrate-and-fire (IF) neurons with Poisson inputs.
Defined learning rules applicable to multilayer and time-series networks.
Applied models to function approximation and robot arm control tasks.

Main Results:

Mathematically robust learning rules were defined for spiking neuronal networks.
Demonstrated successful training of spike-rate networks on function approximation.
Showcased the effectiveness of the models in dynamic tasks like robot arm control.

Conclusions:

Spiking neuronal models employing statistical properties offer a robust framework for computation and learning.
These models are effective for various engineering applications, including complex control tasks.
The defined learning rules advance the capabilities of artificial neural networks.