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

Significance of the Gradient Vector01:27

Significance of the Gradient Vector

A surface defined by a function of two variables can be understood by examining how it changes along specific directions. When one variable is held constant, the surface reduces to a curve that reflects variation in the other variable. For example, fixing one variable and moving parallel to a coordinate axis produces a cross-sectional curve. The slope of this curve at a given point represents how the function changes in that particular direction, providing a measure of local steepness.By...
Gradient and Del Operator01:14

Gradient and Del Operator

In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
Graded Potential01:19

Graded Potential

Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or calcium...
Gradient Fields01:27

Gradient Fields

A gradient field is a vector field derived from a scalar field. A scalar field assigns a single numerical value to every point in space, such as temperature, pressure, or electric potential. The gradient field describes how that value changes from point to point. It gives both the direction of the fastest increase and the rate of change in that direction.For a scalar field f(x, y), the gradient is written as\begin{equation*}\nabla f=\left\langle \jfrac{\partial f}{\partial x},\jfrac{\partial...
The Product Rule01:24

The Product Rule

In calculus, the Product Rule provides a method for differentiating expressions that are the product of two functions. It states that the derivative of the product of two differentiable functions equals the first function times the rate of change of the second, plus the second function times the rate of change of the first.This rule ensures that the rate of change of the product accounts for the simultaneous variation of both functions.A compelling way to understand the Product Rule is through...
Gradient Vectors and Their Applications01:19

Gradient Vectors and Their Applications

Every point on a topographical map corresponds to a particular elevation, so the landscape can be modeled as a surface whose height depends on horizontal position. From any given location, a hiker may face infinitely many directions, but only one direction produces the fastest possible increase in elevation. This unique route is called the direction of steepest ascent, and in multivariable calculus, it is represented by the gradient vector of the elevation function.The gradient vector points...

You might also read

Related Articles

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

Sort by
Same author

Temporal stimulus segmentation by reinforcement learning in populations of spiking neurons.

Physical review. E·2026
Same author

Increased Perceptual Reliability Reduces Membrane Potential Variability in Cortical Neurons.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2025
Same author

Differential modulation of positive and negative prediction errors by stimulus variability in the mouse posterior parietal cortex.

Communications biology·2025
Same author

The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

<i>DreamOn:</i> a data augmentation strategy to narrow the robustness gap between expert radiologists and deep learning classifiers.

Frontiers in radiology·2025
Same author

A neuronal least-action principle for real-time learning in cortical circuits.

eLife·2024
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2026

Measurement &amp; Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

A gradient learning rule for the tempotron.

Robert Urbanczik1, Walter Senn

  • 1Department of Physiology, University of Bern, 3012-Bern, Switzerland. urbanczik@pyl.unibe.ch

Neural Computation
|May 12, 2009
PubMed
Summary
This summary is machine-generated.

We developed a novel supervised learning rule for classifying neural spike trains using an integrate-and-fire neuron. This new method improves performance without needing a specific neuron reset mechanism.

Related Experiment Videos

Last Updated: Jun 23, 2026

Measurement &amp; Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

Area of Science:

  • Computational Neuroscience
  • Machine Learning

Background:

  • The tempotron task involves binary classification of neural input.
  • Integrate-and-fire neurons are common models for neuronal behavior.

Purpose of the Study:

  • Introduce a new supervised learning rule for the tempotron task.
  • Enhance the performance of binary classification for spike trains.

Main Methods:

  • Developed a supervised learning rule based on cost function gradients.
  • Applied the rule to an integrate-and-fire neuron model.

Main Results:

  • The new learning rule demonstrated enhanced performance in classification.
  • The rule is independent of the neuron's specific reset mechanism.

Conclusions:

  • The proposed gradient-based learning rule offers an effective approach for spike train classification.
  • This method provides a flexible and high-performing solution for neural data analysis.