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

3.3K
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....
3.3K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

752
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
752
Neural Circuits01:25

Neural Circuits

1.4K
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...
1.4K
Propagation of Action Potentials01:23

Propagation of Action Potentials

6.1K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
6.1K
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.4K
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...
8.4K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

573
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
573

You might also read

Related Articles

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

Sort by
Same author

Crop rotations with legumes, cover crops and adjusted N management have better environmental performance than maize-wheat double cropping, but lower productivity.

Journal of environmental management·2026
Same author

Rewiring the immune response in lung cancer: current progress in bispecific antibodies, CAR-T therapy, and the rise of <i>in vivo</i> CAR-T platforms.

Frontiers in immunology·2026
Same author

The role of astrocytic GSDME in Sepsis-associated cognitive dysfunction.

International immunopharmacology·2026
Same author

Band Topology and Dynamic Multiferroicity Induced from Dynamical Dzyaloshinskii-Moriya Interactions in Centrosymmetric Lattices.

Physical review letters·2026
Same author

Dual-Proximity Labeling Strategy Identifies TrxR1 as a Cuproprotein that Regulates Apoptosis via Caspase-3 S-Nitrosylation.

Angewandte Chemie (International ed. in English)·2026
Same author

Meta-TIP: An Unsupervised End-to-End Fusion Network for Multi-Dataset Style-Adaptive Threat Image Projection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025

Related Experiment Video

Updated: Aug 6, 2025

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

11.9K

Stochastic photonic spiking neuron for Bayesian inference with unsupervised learning.

Bowen Ma, Junfeng Zhang, Xing Li

    Optics Letters
    |March 22, 2023
    PubMed
    Summary

    We developed a high-speed photonic spiking neuron using noise injection for reliable Bayesian inference. This stochastic approach significantly reduces misdiagnosis rates in critical applications like breast cancer detection.

    More Related Videos

    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

    10.0K
    Imaging Membrane Potential with Two Types of Genetically Encoded Fluorescent Voltage Sensors
    09:57

    Imaging Membrane Potential with Two Types of Genetically Encoded Fluorescent Voltage Sensors

    Published on: February 4, 2016

    10.8K

    Related Experiment Videos

    Last Updated: Aug 6, 2025

    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

    11.9K
    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

    10.0K
    Imaging Membrane Potential with Two Types of Genetically Encoded Fluorescent Voltage Sensors
    09:57

    Imaging Membrane Potential with Two Types of Genetically Encoded Fluorescent Voltage Sensors

    Published on: February 4, 2016

    10.8K

    Area of Science:

    • Neuromorphic photonics
    • Artificial intelligence
    • Computational neuroscience

    Background:

    • Biological neural networks exhibit inherent stochasticity.
    • Conventional deterministic photonic spiking neural networks (D-PSNNs) lack uncertainty quantification.
    • High-speed, reliable information processing is crucial for error-critical applications.

    Purpose of the Study:

    • To propose and demonstrate a noise-injection scheme for a GHz-rate stochastic photonic spiking neuron (S-PSN).
    • To leverage firing-probability encoding for Bayesian inference with unsupervised learning.
    • To enhance diagnostic accuracy and uncertainty evaluation in a breast cancer detection task.

    Main Methods:

    • Implementation of a stochastic photonic spiking neuron (S-PSN) using a noise-injection scheme.
    • Experimental demonstration of firing-probability encoding for Bayesian inference.
    • Development of a stochastic photonic spiking neural network (S-PSNN) for a breast diagnosis task.

    Main Results:

    • Achieved 96.6% classification accuracy in breast cancer diagnosis using the S-PSNN.
    • Successfully evaluated diagnostic uncertainty through prediction entropies.
    • Reduced misdiagnosis rate by 80% compared to deterministic photonic spiking neural networks (D-PSNNs).

    Conclusions:

    • The GHz-rate S-PSN enables high-speed Bayesian inference for neuromorphic photonics.
    • Stochastic photonic spiking neural networks offer reliable information processing in error-critical scenarios.
    • This technology enhances diagnostic reliability and reduces misdiagnosis in medical applications.