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

Random Variables01:09

Random Variables

15.1K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
15.1K
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
Neural Circuits01:25

Neural Circuits

1.9K
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...
1.9K

You might also read

Related Articles

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

Sort by
Same author

GFAP and NfL as predictors of disease progression and relapse activity in fingolimod-treated multiple sclerosis.

Brain : a journal of neurology·2025
Same author

Comparison of the properties of two-dimensional and three-dimensional percolating networks of nanoparticles.

Physical review. E·2025
Same author

Barracuda: a dynamic, Turing-complete GPU virtual machine for high-performance simulations.

Medical & biological engineering & computing·2025
Same author

Complexity of brain-like signals in self-organised nanoscale networks.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Reservoir computing with networks of nanoscale memristors: optimisation of memristor responses for maximal computational performance.

Nanoscale·2025
Same author

Learning and spiking dynamics in brain-like nanoscale networks.

Nanoscale horizons·2025

Related Experiment Video

Updated: Oct 15, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.2K

Stochastic Spiking Behavior in Neuromorphic Networks Enables True Random Number Generation.

Susant K Acharya1, Edoardo Galli1, Joshua B Mallinson1

  • 1The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.

ACS Applied Materials & Interfaces
|November 1, 2021
PubMed
Summary

Nanoparticle networks mimic neuron spiking behavior, controlled by stimuli. This stochasticity enables high-quality random number generation for secure neuromorphic computing applications.

Keywords:
neuromorphicpercolationspiking neuronsstochasticitytrue random number generation

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K
Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

1.8K

Related Experiment Videos

Last Updated: Oct 15, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.2K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K
Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

1.8K

Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Science

Background:

  • Growing interest in nanoscale devices for emulating neuronal and synaptic functions.
  • Need for brain-inspired computation models.

Purpose of the Study:

  • To demonstrate stochastic spiking behavior in nanoparticle networks.
  • To explore the use of this behavior for random number generation.
  • To investigate potential applications in neuromorphic computing and secure information processing.

Main Methods:

  • Fabrication and characterization of percolating nanoparticle networks.
  • Analysis of stochastic spiking behavior and event timing distributions.
  • Evaluation of random number generation quality from network stochasticity.

Main Results:

  • Percolating nanoparticle networks exhibit stochastic spiking behavior similar to biological neurons.
  • Spiking rate is controllable by input stimulus, akin to biological 'rate coding'.
  • Log-normal distributions of inter-event times provide insights into the spiking mechanism.
  • Demonstrated high-quality random bit-stream generation using network stochasticity.

Conclusions:

  • Nanoparticle networks offer a viable platform for emulating neuronal dynamics.
  • Stochastic spiking in these networks can be harnessed for efficient random number generation.
  • This research opens avenues for integrating neuromorphic computing with secure data processing.