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

Integration of Synaptic Events01:28

Integration of Synaptic Events

5.9K
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...
5.9K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

4.2K
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....
4.2K
Parallel Processing01:20

Parallel Processing

889
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
889
Antigen Processing Pathways01:31

Antigen Processing Pathways

3.0K
MHC molecules are key players in the immune response, enabling T cells to recognize and respond to specific antigens. They are present on the surface of all nucleated cells in the body and are instrumental in presenting antigens to T cells and activating them. T cells recognize the MHC-antigen complex and initiate an immune response. MHC class I and MHC class II are two main types of MHC molecules, each associated with a distinct antigen processing pathway.
MHC Class I: Presenting Endogenous...
3.0K
Neural Circuits01:25

Neural Circuits

3.3K
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...
3.3K
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

10.2K
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...
10.2K

You might also read

Related Articles

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

Sort by
Same author

Synaptic high-frequency jumping synchronises vision to high-speed behaviour.

Nature communications·2026
Same author

Modeling and characterization of pure and odorant mixture processing in the <i>Drosophila</i> mushroom body calyx.

Frontiers in physiology·2024
Same author

Divisive normalization processors in the early visual system of the Drosophila brain.

Biological cybernetics·2023
Same author

The functional logic of odor information processing in the Drosophila antennal lobe.

PLoS computational biology·2023
Same author

A Programmable Ontology Encompassing the Functional Logic of the <i>Drosophila</i> Brain.

Frontiers in neuroinformatics·2022
Same author

Connectomic analysis of the <i>Drosophila</i> lateral neuron clock cells reveals the synaptic basis of functional pacemaker classes.

eLife·2022
Same journal

Analysis of Variance of Multiple Causal Networks.

Advances in neural information processing systems·2026
Same journal

Long-term Intracortical Neural activity and Kinematics (LINK): An intracortical neural dataset for chronic brain-machine interfaces, neuroscience, and machine learning.

Advances in neural information processing systems·2026
Same journal

Distributionally Robust Feature Selection.

Advances in neural information processing systems·2026
Same journal

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution.

Advances in neural information processing systems·2026
Same journal

Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.

Advances in neural information processing systems·2026
Same journal

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

Advances in neural information processing systems·2026
See all related articles

Related Experiment Video

Updated: Apr 3, 2026

Patterned Photostimulation with Digital Micromirror Devices to Investigate Dendritic Integration Across Branch Points
09:30

Patterned Photostimulation with Digital Micromirror Devices to Investigate Dendritic Integration Across Branch Points

Published on: March 2, 2011

16.3K

Identifying Dendritic Processing.

Aurel A Lazar1, Yevgeniy B Slutskiy2

  • 1Department of Electrical Engineering Columbia University New York, NY 10027 aurel@ee.columbia.edu.

Advances in Neural Information Processing Systems
|September 22, 2015
PubMed
Summary
This summary is machine-generated.

We developed a new method to identify dendritic processing in neural circuits. This algorithm precisely reconstructs the dendritic filter

More Related Videos

Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice
10:55

Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice

Published on: March 31, 2015

10.9K
Assessment of Hippocampal Dendritic Complexity in Aged Mice Using the Golgi-Cox Method
09:44

Assessment of Hippocampal Dendritic Complexity in Aged Mice Using the Golgi-Cox Method

Published on: June 22, 2017

15.8K

Related Experiment Videos

Last Updated: Apr 3, 2026

Patterned Photostimulation with Digital Micromirror Devices to Investigate Dendritic Integration Across Branch Points
09:30

Patterned Photostimulation with Digital Micromirror Devices to Investigate Dendritic Integration Across Branch Points

Published on: March 2, 2011

16.3K
Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice
10:55

Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice

Published on: March 31, 2015

10.9K
Assessment of Hippocampal Dendritic Complexity in Aged Mice Using the Golgi-Cox Method
09:44

Assessment of Hippocampal Dendritic Complexity in Aged Mice Using the Golgi-Cox Method

Published on: June 22, 2017

15.8K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • System Identification

Background:

  • System identification aims to determine model parameters from system inputs and outputs.
  • Understanding dendritic processing is crucial for comprehending neural circuit function.
  • Existing methods may not fully capture the complexities of dendritic computations.

Purpose of the Study:

  • To present a novel formal methodology for identifying dendritic processing.
  • To develop an algorithm for parameter identification in a neural circuit model.
  • To reconstruct the kernel of a linear dendritic processing filter with high precision.

Main Methods:

  • The study models a neural circuit with a linear dendritic filter in cascade with a spiking neuron.
  • The input signal is an analog, bandlimited function.
  • An algorithm is derived to identify the dendritic filter's parameters from the spike train output.

Main Results:

  • A formal methodology for identifying dendritic processing was successfully derived.
  • The algorithm enables precise reconstruction of the dendritic filter's kernel.
  • The approach is applicable to neural circuits with linear dendritic filtering and spiking neuron output.

Conclusions:

  • The developed methodology offers a powerful tool for analyzing neural circuit dynamics.
  • Precise identification of dendritic filters advances our understanding of neural computation.
  • This work contributes to the field of system identification in neuroscience.