Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Role of Ion Channels in Neuronal Computation

The Role of Ion Channels in Neuronal Computation

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.

Action Potential: Phases of Stimulation

Action Potential: Phases of Stimulation

The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
Resting Phase:
In this phase, the cell's membrane is at its resting potential, typically around -70 millivolts (mV) for neurons. Inside the cell, there is a higher concentration of potassium ions (K+) and a lower concentration of sodium ions (Na+). Voltage-gated sodium channels are closed, and...

Neural Circuits

Neural Circuits

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...

You might also read

Related Articles

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

Sort by

Same author

Progress and Prospects of Nanodelivery Systems in Metabolism-Regulated mRNA Vaccines.

Biotechnology journal·2026

Same author

Coordinated sub-cycle modulation atomic layer deposition of atomically homogeneous GeTe<sub>9</sub> thin films for high-performance OTSs.

Materials horizons·2026

Same author

A body roundness index (BRI)-based predictive model for metabolic syndrome in perimenopausal and postmenopausal women-from a cross-sectional machine learning study to a longitudinal dynamic assessment.

Annals of medicine·2026

Same author

Associations of cumulative exposure and dynamic trajectories of the combined atherogenic and frailty index with incident cardiometabolic multimorbidity: a longitudinal analysis based on the China Health and Retirement Longitudinal Study (CHARLS).

Cardiovascular diabetology·2026

Same author

Circular RNAs in hepatocellular carcinoma: a systematic qualitative review of regulatory mechanisms, therapeutic resistance, and clinical translation.

BMC gastroenterology·2026

Same author

Effects of dietary fiber from different particle sizes of wheat bran on myofibrillar protein structure.

Food research international (Ottawa, Ont.)·2026

Search research articles

Related Experiment Video

Updated: May 20, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Compact photonic spiking neuron with inherent stochasticity based on phase-change material for probabilistic

Yunxiao Dong¹, Tianci Wang¹, Jian Xia^1,2

¹School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.

Nature Communications

|May 18, 2026

Summary

Researchers developed a novel photonic spiking neuron with inherent stochasticity using a phase-change material. This breakthrough enables efficient Bayesian inference and robust on-chip photonic neuromorphic computing systems.

More Related Videos

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

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

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

Published on: February 4, 2016

Related Experiment Videos

Last Updated: May 20, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

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

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

Published on: February 4, 2016

Area of Science:

Photonics
Neuromorphic Computing
Materials Science

Background:

Probabilistic models in photonic neural networks are promising for Bayesian inference.
Existing photonic neurons lack intrinsic stochasticity, complicating designs.
On-chip integration of probabilistic computing requires efficient stochastic neurons.

Purpose of the Study:

To report the first compact on-chip photonic spiking neuron with inherent stochasticity.
To utilize a novel phase-change material for probabilistic photonic computing.
To demonstrate the neuron's capability for Bayesian inference and its robustness.

Main Methods:

Developed a photonic spiking neuron using a novel phase-change material (SbTe9).
Exploited intrinsic fluctuations in the material's melting point for stochasticity.
Integrated the neuron into a system for Bayesian inference and tested its tolerance to variations and noise.

Main Results:

Achieved stable and tunable probabilistic firing behaviors.
Demonstrated 98.67% accuracy in breast cell diagnosis using Bayesian inference with uncertainty quantification.
Showcased remarkable tolerance to hardware synaptic variations and input noise compared to deterministic neurons.

Conclusions:

The novel stochastic photonic neuron enables low-complexity, high-performance on-chip photonic neuromorphic computing.
Phase-change materials offer a transformative pathway for advanced neuromorphic systems.
This work paves the way for large-scale, efficient probabilistic photonic computing architectures.