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

Discrete Fourier Transform01:15

Discrete Fourier Transform

901
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
901
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

1.1K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
1.1K
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

720
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
720
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

684
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
684
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

947
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
947
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

928
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
928

You might also read

Related Articles

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

Sort by
Same author

Connectomic Analysis of Mitochondria in the Central Brain of <i>Drosophila</i>.

bioRxiv : the preprint server for biology·2026
Same author

Integrated clinical and postmortem profiling in schizophrenia reveals a cognitive subtype linked to cerebrovascular disease.

Translational psychiatry·2026
Same author

Sexual dimorphism in the complete connectome of the <i>Drosophila</i> male central nervous system.

bioRxiv : the preprint server for biology·2025
Same author

Imaging cellular activity simultaneously across all organs of a vertebrate reveals body-wide circuits.

bioRxiv : the preprint server for biology·2025
Same author

PEELing: an integrated and user-centric platform for spatially resolved proteomics data analysis.

Bioinformatics (Oxford, England)·2025
Same author

DELTA: a method for brain-wide measurement of synaptic protein turnover reveals localized plasticity during learning.

Nature neuroscience·2025

Related Experiment Video

Updated: Feb 3, 2026

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion
09:09

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Published on: April 12, 2020

7.5K

The subiculum is a patchwork of discrete subregions.

Mark S Cembrowski1, Lihua Wang1, Andrew L Lemire1

  • 1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.

Elife
|October 31, 2018
PubMed
Summary

Researchers discovered eight distinct subclasses of subiculum pyramidal cells in the mouse hippocampus. These subclasses exhibit a complex spatial organization and connect to different brain regions, offering new insights into hippocampal circuitry.

Keywords:
RNA-seqcell typehippocampusmouseneurosciencepyramidal cellsubiculumtranscriptome

More Related Videos

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
09:22

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

15.0K
Reconfigurable Microfluidic Channel with Pin-discretized Sidewalls
10:39

Reconfigurable Microfluidic Channel with Pin-discretized Sidewalls

Published on: April 12, 2018

7.8K

Related Experiment Videos

Last Updated: Feb 3, 2026

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion
09:09

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Published on: April 12, 2020

7.5K
A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
09:22

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

15.0K
Reconfigurable Microfluidic Channel with Pin-discretized Sidewalls
10:39

Reconfigurable Microfluidic Channel with Pin-discretized Sidewalls

Published on: April 12, 2018

7.8K

Area of Science:

  • Neuroscience
  • Molecular Biology
  • Computational Biology

Background:

  • The subiculum, a key hippocampal region, acts as a primary output pathway for conveying information to downstream brain areas.
  • Emerging evidence suggests the subiculum pyramidal cell population is not uniform but comprises discrete subclasses.

Purpose of the Study:

  • To investigate the heterogeneity of pyramidal cells within the mouse subiculum.
  • To identify the organizational principles governing pyramidal cell subclasses.

Main Methods:

  • Single-cell RNA sequencing (scRNA-seq) was employed to analyze gene expression profiles of individual subiculum pyramidal cells.
  • Spatial mapping techniques were used to determine the anatomical distribution of identified subclasses.

Main Results:

  • Eight distinct pyramidal cell subclasses were identified within the subiculum.
  • These subclasses displayed a complex laminar and columnar organization along dorsal-ventral, proximal-distal, and superficial-deep axes.
  • Transcriptomic subclasses corresponded to differential protein expression and specific projection targets.

Conclusions:

  • The subiculum pyramidal cell population is comprised of spatially segregated subclasses.
  • This detailed deconstruction provides a framework for future studies on subiculum function and regulation.