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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

488
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
488
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Machines01:19

Machines

577
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
577
Location and Orientation of the Heart01:13

Location and Orientation of the Heart

10.1K
The human heart, despite its modest size and weight, is an organ of remarkable strength and endurance. Roughly the size of a fist, the heart weighs between 250 and 350 grams and is nestled within the mediastinum, the medial cavity of the thorax. It extends obliquely for about 12 to 14 cm, resting on the superior surface of the diaphragm. The heart is positioned anterior to the vertebral column and posterior to the sternum, with two-thirds of its mass lying to the left of the midsternal line.
10.1K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Machines: Problem Solving II01:30

Machines: Problem Solving II

668
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
668

You might also read

Related Articles

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

Sort by
Same author

DeltaQ: Value-Guided Hebbian Learning in Spiking Neuronal Networks for Multi-Goal Navigation.

bioRxiv : the preprint server for biology·2026
Same author

Turing universal neural networks do not require global clocks.

Nature communications·2026
Same author

Heuristically Adaptive Diffusion-Model Evolutionary Strategy.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Longitudinal Trends in Cardiac Arrest-Related Mortality in Patients With Chronic Obstructive Pulmonary Disease: A Retrospective U.S. Population-Based Study (1999-2023).

Cardiology in review·2026
Same author

A study of different cognitive states for meditators and non-meditators with the use of multiple classification indices derived from the PSD of EEG data and lessons learned about cognitive states and the nature of intelligence in minds and machines.

Frontiers in systems neuroscience·2026
Same author

Neuromodulators Generate Multiple Context-Relevant Behaviors in Recurrent Neural Networks.

Neural computation·2026
Same journal

Predicting vasovagal syncope during head-up tilt test: three machine learning approaches.

Frontiers in neuroinformatics·2026
Same journal

Decoding basal ganglia motor circuit dysfunction from handwriting: a physics-informed neural signal interpretation framework for Parkinson's disease screening.

Frontiers in neuroinformatics·2026
Same journal

FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.

Frontiers in neuroinformatics·2026
Same journal

A 3D-printed phantom to validate subject orientation in 3D imaging and recordings.

Frontiers in neuroinformatics·2026
Same journal

IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.

Frontiers in neuroinformatics·2026
Same journal

Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.

Frontiers in neuroinformatics·2026
See all related articles

Related Experiment Video

Updated: Jan 30, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K

BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python.

Hananel Hazan1, Daniel J Saunders1, Hassaan Khan1

  • 1Biologically Inspired Neural and Dynamical Systems Laboratory, College of Computer and Information Sciences, University of Massachusetts Amherst, Amherst, MA, United States.

Frontiers in Neuroinformatics
|January 12, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces BindsNET, a Python package for simulating spiking neural networks (SNNs) tailored for machine learning. BindsNET enables rapid prototyping and application of SNNs in AI, particularly for reinforcement learning tasks.

Keywords:
GPU-computingPyTorchmachine learningpython (programming language)reinforcement learning (RL)spiking Network

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
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

12.3K

Related Experiment Videos

Last Updated: Jan 30, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
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

12.3K

Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Artificial Intelligence

Background:

  • Spiking neural network (SNN) simulation software is crucial for neural system modeling and biologically inspired algorithms.
  • Existing frameworks often lack suitability for rapid prototyping and machine learning applications.
  • There is a need for specialized SNN simulation tools optimized for machine learning.

Purpose of the Study:

  • To introduce BindsNET, a novel Python package for simulating spiking neural networks.
  • To facilitate rapid building and simulation of SNNs for machine learning and reinforcement learning.
  • To provide a user-friendly interface for applying SNNs to AI problems.

Main Methods:

  • Developed BindsNET, a Python package built on the PyTorch deep learning library.
  • Integrated BindsNET with OpenAI Gym for reinforcement learning.
  • Enabled implementation on CPU and GPU platforms, with potential for other backends like TensorFlow and SpiNNaker.

Main Results:

  • BindsNET offers concise syntax for rapid SNN development and simulation.
  • The framework supports efficient SNN implementation on modern computational hardware.
  • Demonstrated practical application of BindsNET for machine learning tasks.

Conclusions:

  • BindsNET effectively bridges the gap between SNN research and machine learning applications.
  • The package facilitates the use of SNNs for large-scale AI problems, including reinforcement learning.
  • BindsNET represents a significant advancement for researchers and developers in neuromorphic computing and AI.