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.
Integration of Synaptic Events01:28

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...

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
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 21, 2026

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

Maximum likelihood decoding of neuronal inputs from an interspike interval distribution.

Xuejuan Zhang1, Gongqiang You, Tianping Chen

  • 1Mathematical Department, Zhejiang Normal University, Jinhua, PR China. xuejuanzhang@gmail.com

Neural Computation
|July 29, 2009
PubMed
Summary
This summary is machine-generated.

Researchers derived a probability distribution for leaky integrate-and-fire (LIF) neuron models, enabling maximum likelihood estimates (MLE) of neural input from spike trains. This method efficiently decodes dynamic inputs in neural networks.

More Related Videos

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

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Related Experiment Videos

Last Updated: Jun 21, 2026

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

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

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Area of Science:

  • Computational Neuroscience
  • Theoretical Neuroscience
  • Stochastic Processes

Background:

  • The leaky integrate-and-fire (LIF) model is fundamental in computational neuroscience for simulating neuronal firing.
  • Estimating input parameters from recorded spike trains is crucial for understanding neural coding.
  • Existing methods often lack rigorous theoretical grounding or efficiency for dynamic inputs.

Purpose of the Study:

  • To derive a rigorous expression for the interspike interval probability distribution of the LIF neuron.
  • To develop a novel maximum likelihood estimation (MLE) method for inferring neural input parameters from spike train data.
  • To demonstrate the efficiency and reliability of the MLE method for decoding dynamic inputs in neural networks.

Main Methods:

  • Utilized recent theoretical advancements in stochastic process theory.
  • Derived the probability distribution of the interspike interval for the LIF model.
  • Applied the derived distribution to develop a maximum likelihood estimation framework.
  • Tested the MLE method on simulated spike trains with dynamic inputs, including interacting neuron pools.

Main Results:

  • Successfully derived a closed-form expression for the LIF neuron's interspike interval distribution.
  • Developed and validated a novel maximum likelihood estimation (MLE) technique for inferring input rates and variances.
  • Demonstrated efficient and reliable decoding of dynamic inputs using MLE, even with short time windows (25 ms).
  • Showcased the method's effectiveness for both isolated and interacting LIF neurons.

Conclusions:

  • The derived probability distribution provides a rigorous foundation for LIF neuron analysis.
  • The novel MLE method offers a powerful tool for estimating neural input from spike train recordings.
  • This approach significantly advances the ability to decode neural activity in response to dynamic stimuli.