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

Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
Neural Circuits01:25

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...
Integration of Synaptic Events01:28

Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
The Role of Ion Channels in Neuronal Computation01:19

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.
Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
Graded Potential01:19

Graded Potential

Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or calcium...

You might also read

Related Articles

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

Sort by
Same author

Evaluating upper airway in orthodontics via 3D UX-Net model on CBCT scans.

Clinical oral investigations·2026
Same author

Nanofiber-based protection of DNA for archival data storage via coaxial electrospinning and chitosan integration.

Nanotechnology·2026
Same author

Self-replication of DNA cross-tile patterns from temperature-selected species.

Nanoscale·2026
Same author

Hierarchical access to encoded data on DNA nanostructures using administrator and user keys.

Nucleic acids research·2025
Same author

AEPMA: peptide-microbe association prediction based on autoevolutionary heterogeneous graph learning.

Briefings in bioinformatics·2025
Same author

A Chemo-Mechanically Coupled DNA Origami Clamp Capable of Generating Robust Compression Forces.

Small (Weinheim an der Bergstrasse, Germany)·2024
Same journal

Investigating Effect of Dimensional Variance on Separation of Glomerular Ultrafiltrate in a Microfluidic Environment.

IEEE transactions on nanobioscience·2026
Same journal

Green synthesis of multifunctional ZnFe<sub>2</sub>O<sub>4</sub>-MWCNT-Cellulose acetate nanocomposite for peroxidase enzyme immobilization.

IEEE transactions on nanobioscience·2026
Same journal

IoT-Enabled SnO₂-Based Humidity Sensor for Real-Time Monitoring in Neonatal Incubators.

IEEE transactions on nanobioscience·2026
Same journal

Electrokinetic and Antibiofilm Effects of Silver Nanoparticles Combined with Imipenem Against multidrug-resistant of Klebsiella pneumoniae.

IEEE transactions on nanobioscience·2026
Same journal

Bio-inspired Optofluidic Molecular Communication with Photothermally Actuated Microrobot Swarms.

IEEE transactions on nanobioscience·2026
Same journal

Nanostructured ZnO Thin Film-Based Enzymatic Biosensor for Sensitive Acetylcholine Detection in Neurological Applications.

IEEE transactions on nanobioscience·2026
See all related articles

Related Experiment Video

Updated: May 19, 2026

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

Performing four basic arithmetic operations with spiking neural P systems.

Xiangxiang Zeng1, Tao Song, Xingyi Zhang

  • 1Department of Computer Science, Xiamen Unviersity, Xiamen 361005, Fujian, China. xzeng@xmu.edu.cn

IEEE Transactions on Nanobioscience
|August 16, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces novel spiking neural P systems for arithmetic operations. These bio-inspired computing systems encode numbers as spike time intervals, offering a new approach to computation.

More Related Videos

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

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

Related Experiment Videos

Last Updated: May 19, 2026

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

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

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

Area of Science:

  • Computational intelligence
  • Bio-inspired computing
  • Theoretical computer science

Background:

  • Spiking neural P systems (SN P systems) are bio-inspired computing devices.
  • Previous work used binary encoding in SN P systems, differing from typical spike-time encoding.
  • Arithmetic operations are fundamental to computation.

Purpose of the Study:

  • To design and implement SN P systems for basic arithmetic operations (addition, subtraction, multiplication, division).
  • To utilize spike-time intervals for number encoding and computation results within SN P systems.
  • To propose a novel computational model inspired by neural signaling.

Main Methods:

  • Four distinct SN P systems were developed: an adder, subtracter, multiplier, and divider.
  • Numerical input was represented by the time interval between two input neuron spikes.
  • Computational output was encoded as the time interval between output neuron spikes.

Main Results:

  • Successfully constructed SN P systems capable of performing addition, subtraction, multiplication, and division.
  • Demonstrated effective encoding of numerical data using spike time intervals.
  • Validated the computational output through the timing of output neuron spikes.

Conclusions:

  • The proposed SN P systems effectively perform arithmetic operations using spike-time encoding.
  • This research advances bio-inspired computing by aligning SN P systems with natural neural signaling.
  • The developed model offers a promising alternative for computational tasks leveraging temporal coding.