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

Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

3.9K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
3.9K
Poisson Probability Distribution01:09

Poisson Probability Distribution

11.3K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
11.3K
Poisson's Ratio01:23

Poisson's Ratio

789
Poisson's ratio is a material property that indicates their stress response. It explains the connection between the elongation or compression a material undergoes in the direction of an applied force and the contraction or expansion it experiences perpendicular to that force. When a slender bar is loaded axially, it stretches in the direction of the force and contracts laterally. Poisson's ratio is the negative ratio of this lateral contraction to the axial elongation. The negative sign...
789
Neural Circuits01:25

Neural Circuits

2.3K
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...
2.3K
The Resting Membrane Potential01:21

The Resting Membrane Potential

139.9K
Overview
139.9K
Resting Membrane Potential01:24

Resting Membrane Potential

20.7K
The relative difference in electrical charge, or voltage, between the inside and the outside of a cell membrane, is called the membrane potential. It is generated by differences in permeability of the membrane to various ions and the concentrations of these ions across the membrane.
The Inside of a Neuron is More Negative
The membrane potential of a cell can be measured by inserting a microelectrode into a cell and comparing the charge to a reference electrode in the extracellular fluid. The...
20.7K

You might also read

Related Articles

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

Sort by
Same author

State-switching navigation strategies in <i>Caenorhabditis elegans</i> are beneficial for chemotaxis.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Compact deep neural network models of the visual cortex.

Nature·2026
Same author

Identifying the factors governing internal state switches during nonstationary sensory decision-making.

Nature communications·2025
Same author

Fast Optimization of Robust Transcriptomics Embeddings using Probabilistic Inference Autoencoder Networks for multi-Omics.

bioRxiv : the preprint server for biology·2025
Same author

Improved inference of latent neural states from calcium imaging data.

bioRxiv : the preprint server for biology·2025
Same author

Inferring learning rules during de novo task learning.

bioRxiv : the preprint server for biology·2025
Same journal

Combinatorial multiomic analysis from a pedigree of Sox10Dom Hirschsprung mice identifies multiple high confidence candidate modifiers of Enteric Nervous System development.

PLoS computational biology·2026
Same journal

Extracting host-specific developmental signatures from longitudinal microbiome data.

PLoS computational biology·2026
Same journal

Population sparseness determines strength of Hebbian plasticity for maximal memory lifetime in associative networks.

PLoS computational biology·2026
Same journal

Predictive coding explains asymmetric connectivity in the brain: A neural network study.

PLoS computational biology·2026
Same journal

Zooplankton feeding behavioral signatures in the morphology of macroscale prey spatial distribution.

PLoS computational biology·2026
Same journal

A brief overview of 20 years of neuroscience in PLoS Computational Biology.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Nov 29, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

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

Published on: June 24, 2015

11.8K

Poisson balanced spiking networks.

Camille E Rullán Buxó1, Jonathan W Pillow2

  • 1Center for Neural Science, New York University, New York City, New York, USA.

Plos Computational Biology
|November 20, 2020
PubMed
Summary
This summary is machine-generated.

Balanced spiking networks (BSNs) with realistic synaptic delays are stabilized using conditionally Poisson firing. This novel approach maintains accuracy and robustness in computational neuroscience models.

More Related Videos

Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish
10:14

Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish

Published on: November 26, 2019

9.1K
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

10.1K

Related Experiment Videos

Last Updated: Nov 29, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

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

Published on: June 24, 2015

11.8K
Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish
10:14

Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish

Published on: November 26, 2019

9.1K
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

10.1K

Area of Science:

  • Computational Neuroscience
  • Neural Network Modeling
  • Spiking Neuron Dynamics

Background:

  • Balanced spiking networks (BSNs) model computations in spiking neurons.
  • Classic BSNs lack biological realism due to instantaneous spike transmission.
  • Synaptic delays in BSNs cause instability ('ping-ponging') and inaccurate dynamics.

Purpose of the Study:

  • To address the instability caused by synaptic delays in BSNs.
  • To develop a biologically plausible BSN model with accurate dynamics.
  • To investigate the effects of conditionally Poisson firing on network stability and function.

Main Methods:

  • Introduced conditionally Poisson firing into the BSN framework.
  • Developed two formulations: a 'local' framework with a soft threshold and a 'population' framework using expected spike counts.
  • Analyzed network stability and accuracy with realistic synaptic delays.

Main Results:

  • Conditionally Poisson firing resolves the 'ping-ponging' instability caused by synaptic delays.
  • Both proposed Poisson BSN frameworks maintain coding accuracy and robustness to neuron loss.
  • The models exhibit positive correlations between similarly tuned neurons, a feature of biological neural populations.

Conclusions:

  • Conditionally Poisson firing provides a solution for incorporating realistic synaptic delays into BSNs.
  • The proposed Poisson BSNs are accurate, stable, and more biologically realistic.
  • This work unifies BSNs with Poisson generalized linear models, opening new research avenues.