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

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.
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Net Change Theorem01:22

Net Change Theorem

The Net Change Theorem is a fundamental principle in calculus that establishes a direct relationship between a function’s rate of change and its accumulated change over an interval. Mathematically, it states that the definite integral of a function's derivative over a given interval [a,b] yields the net change in the original function:This theorem has significant applications in various real-world scenarios, including physics, economics, and engineering. A particularly useful application is in...
Binomial Probability Distribution01:15

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
The Maximum Power Transfer Theorem01:20

The Maximum Power Transfer Theorem

Consider a linear AC Thevenin equivalent circuit connected to a load impedance.
The load connected draws the current, and the circuit delivers the power to the load. The alternating current flowing through the load is determined using the rectangular form of voltages, currents, network impedance, and load impedance. The average power delivered to the load is obtained from the product of the square of current and load resistance.
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

You might also read

Related Articles

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

Sort by
Same author

Temporally-Varying Stimulations for Cortical Visual Neuroprosthetics using Spiking Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Investigation of Audio Controlled Edge Semantic Segmentation System for Visually Impaired People.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

A Behavioral Study of Event-based Depth-Filtered Prosthetic Vision in Simulated Dynamic Environments<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Deep Learning-Based Control of Electrically Evoked Activity in Human Visual Cortex.

bioRxiv : the preprint server for biology·2025
Same author

Exploiting neuro-inspired dynamic sparsity for energy-efficient intelligent perception.

Nature communications·2025
Same author

Real-time control of a hearing instrument with EEG-based attention decoding.

Journal of neural engineering·2025

Related Experiment Video

Updated: Jun 22, 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

Computation with spikes in a winner-take-all network.

Matthias Oster1, Rodney Douglas, Shih-Chii Liu

  • 1Institute of Neuroinformatics, Uni-ETH Zurich, CH-8057 Zurich, Switzerland. mao@ini.phys.ethz.ch

Neural Computation
|June 25, 2009
PubMed
Summary

This study analyzes spiking neural networks performing winner-take-all (WTA) computations. Spiking WTA networks effectively discriminate both steady and non-stationary inputs, similar to rate-based models.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 22, 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

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

Area of Science:

  • Computational neuroscience
  • Neural networks
  • Neuromorphic engineering

Background:

  • Winner-take-all (WTA) computation is crucial for decision-making in cortical processing models.
  • Most analytical studies focus on rate-based models, with limited analysis of spiking neuron networks.
  • Spiking networks are vital for understanding biological neural systems and developing neuromorphic hardware.

Purpose of the Study:

  • To analytically examine the input discrimination capabilities of spike-based WTA networks.
  • To extend previous theoretical work on WTA performance in recurrent networks to spiking neuron models.
  • To investigate the impact of non-stationary inputs on spiking WTA network performance.

Main Methods:

  • Utilized a simplified Markov model to represent spiking neural networks.
  • Analyzed the discrimination of stationary regular and non-stationary Poisson input statistics.
  • Extended analysis to include time-varying spike rates mimicking sensory stimuli.

Main Results:

  • Spiking WTA networks show input discrimination consistent with rate-based models for spike rate inputs.
  • Self-excitation and inhibition effects on discrimination align with traditional WTA models.
  • Spiking WTAs demonstrate high discrimination performance even with non-stationary, time-varying inputs.

Conclusions:

  • Spiking WTA networks are functionally consistent with continuous rate-based models for steady-state inputs.
  • These networks exhibit robust discrimination capabilities for non-stationary inputs.
  • The findings support the utility of spiking WTA networks in biological and artificial neural systems.