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

Long-term Depression

Long-term Depression

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

Long-term Depression

Long-term Depression

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

Integration of Synaptic Events

Integration of Synaptic Events

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...

Postsynaptic Potential (PSP)

Postsynaptic Potential (PSP)

Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...

Propagation of Action Potentials

Propagation of Action Potentials

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...

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.
Hebbian LTP
LTP can occur when presynaptic neurons...

You might also read

Related Articles

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

Sort by

Same author

A unified neurocomputational model of prospective and retrospective timing.

Psychological review·2025

Same author

Modelling neural probabilistic computation using vector symbolic architectures.

Cognitive neurodynamics·2024

Same author

A spiking neural model of decision making and the speed-accuracy trade-off.

Psychological review·2024

Same author

A scalable spiking amygdala model that explains fear conditioning, extinction, renewal and generalization.

The European journal of neuroscience·2024

Same author

Exploiting semantic information in a spiking neural SLAM system.

Frontiers in neuroscience·2023

Same author

Biologically-Based Computation: How Neural Details and Dynamics Are Suited for Implementing a Variety of Algorithms.

Brain sciences·2023

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

Solving the problem of negative synaptic weights in cortical models.

Christopher Parisien¹, Charles H Anderson, Chris Eliasmith

¹Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada. chris@cs.toronto.edu

Neural Computation

|February 8, 2008

Summary

This summary is machine-generated.

Researchers developed a method to convert unrealistic neural network models into biologically plausible ones. This approach ensures neurons are either inhibitory or excitatory, maintaining network function and dynamics.

More Related Videos

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Related Experiment Videos

Last Updated: Jul 7, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Area of Science:

Computational Neuroscience
Systems Neuroscience
Theoretical Neuroscience

Background:

Cortical neural networks feature neurons that are exclusively inhibitory or excitatory.
Existing theoretical models often violate this biological constraint, limiting their plausibility.
A general solution for converting unrealistic models to biologically constrained ones is lacking.

Purpose of the Study:

To develop a general method for transforming computational neural network models.
To ensure transformed models adhere to the biological constraint of unidirectional neuronal signaling (excitatory or inhibitory).
To maintain the functional and dynamic properties of the original network models.

Main Methods:

A constructive transformation technique was developed for network models.
The method applies to both feedforward and dynamic recurrent neural network architectures.
The transformation identifies a general mathematical form for the solution.

Main Results:

The developed transformation successfully converts unrealistic models into biologically plausible ones.
The transformed models closely approximate the original network functions and temporal dynamics.
A general form for the solution to this model conversion problem was identified.

Conclusions:

A biologically plausible transformation method for cortical neural network models is now available.
This approach addresses a significant gap in computational neuroscience model development.
The study also outlines empirical methods for determining precise solutions for specific cortical networks.