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

What is a Sensory System?01:31

What is a Sensory System?

89.5K
Sensory systems detect stimuli—such as light and sound waves—and transduce them into neural signals that can be interpreted by the nervous system. In addition to external stimuli detected by the senses, some sensory systems detect internal stimuli—such as the proprioceptors in muscles and tendons that send feedback about limb position.
89.5K
Sensory Perception: Organization of the Somatosensory System01:11

Sensory Perception: Organization of the Somatosensory System

8.4K
The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
The receptor level:
The receptor level is the first stage of sensation. It involves the detection of a stimulus by specialized sensory receptors. The stimulus must arrive within the receptor's receptive field. Next, the receptor converts the energy of the...
8.4K
Signal and System01:26

Signal and System

1.7K
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
1.7K
Downsampling01:20

Downsampling

868
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
868
Introduction to Special Senses01:26

Introduction to Special Senses

6.6K
Sensory receptors play an integral part in comprehending our external and internal environments. They receive diverse stimuli, converting them into the nervous system's electrochemical signals. This conversion occurs as the stimulus alters the sensory neuron's cell membrane potential, instigating the generation of an action potential. This action potential is subsequently transmitted to the central nervous system (CNS), which integrates with other sensory data or higher cognitive...
6.6K
Upsampling01:22

Upsampling

743
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
743

You might also read

Related Articles

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

Sort by
Same author

Redefining spiking neural networks through the lens of dynamical superspace.

Cognitive neurodynamics·2026
Same author

A novel image-based neuronal network model framework for understanding visual multistability and neurological disorders.

Frontiers in computational neuroscience·2026
Same author

Anatomical connectivity reconstruction of biological neuronal networks using Granger causality.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Distance-dependent connectivity in the brain facilitates high dynamical and structural complexity.

Cognitive neurodynamics·2025
Same author

Overcoming the space clamp effect: Reliable recovery of local and effective synaptic conductances of neurons.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Origin, Impact, and Solutions for Lifestyle-Related Diseases in Japan.

Cureus·2025
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Apr 25, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K

Sparsity and compressed coding in sensory systems.

Victor J Barranca1, Gregor Kovačič2, Douglas Zhou3

  • 1Courant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, New York, United States of America; NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.

Plos Computational Biology
|August 22, 2014
PubMed
Summary
This summary is machine-generated.

Sensory systems may evolve to exploit sparse stimuli, reducing neuron numbers in early pathways. This research proposes a compressed-sensing (CS) network mechanism for efficient sparse signal transmission.

More Related Videos

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

11.3K
Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

12.4K

Related Experiment Videos

Last Updated: Apr 25, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K
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

11.3K
Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

12.4K

Area of Science:

  • Computational neuroscience
  • Systems neuroscience
  • Information theory

Background:

  • Natural stimuli often exhibit sparsity.
  • Early sensory pathways show significant downstream neuronal reductions.
  • The evolutionary advantage of processing sparse stimuli is not fully understood.

Purpose of the Study:

  • To investigate if sensory systems evolve to leverage stimulus sparsity.
  • To identify potential mechanisms for transmitting sparse stimuli efficiently.
  • To explore the relationship between neuronal network structure and sparsity encoding.

Main Methods:

  • Modeling an early sensory pathway with an idealized neuronal network (receptors and downstream neurons).
  • Analyzing the linear structure within neuronal network dynamics.
  • Simulating networks to examine optimal sparsity encoding and receptive field characteristics.
  • Relating findings to compressed-sensing (CS) principles.

Main Results:

  • A linear structure in neuronal dynamics suggests a mechanism for transmitting sparse stimuli, akin to CS.
  • Identified characteristics of networks optimal for sparsity encoding.
  • Demonstrated the impact of localized receptive fields on sparsity encoding, extending beyond conventional CS theory.

Conclusions:

  • Downstream neuronal reductions in sensory pathways may be a consequence of stimulus sparsity.
  • Proposed a novel network framework for signal sparsity, independent of specific representations.
  • The CS network mechanism offers guidance for studying sparse stimulus transmission and engineering sparse-encoding networks.