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

Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Associative Learning01:27

Associative Learning

1.3K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.3K
Purposive Learning01:22

Purposive Learning

508
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
508
Observational Learning01:12

Observational Learning

979
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
979
Learning Disabilities01:25

Learning Disabilities

613
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
613
Introduction to Learning01:18

Introduction to Learning

1.2K
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Correction: CPX-351 vs. conventional chemotherapy cardiotoxicity in high-risk AML: a post hoc phase III trial analysis.

Cardio-oncology (London, England)·2026
Same author

Prognostic Value of Right Ventricular Septal Longitudinal Strain in Intermediate-Risk Pulmonary Embolism.

Circulation journal : official journal of the Japanese Circulation Society·2026
Same author

Mitigations for extra stimuli of the left ventricular endocardium with the WiSE-CRT System.

International journal of cardiology. Heart & vasculature·2026
Same author

2026 ACC/AHA/HRS advanced training statement on clinical cardiac electrophysiology (Revision of the 2015 ACC/AHA/HRS advanced training statement on clinical cardiac electrophysiology): A report of the ACC Competency Management Committee.

Heart rhythm·2026
Same author

The impact of cardiac resynchronization therapy on diastolic parameters and mitral regurgitation: echocardiographic analysis of ultrasound-based left ventricular endocardial pacing system.

Frontiers in cardiovascular medicine·2026
Same author

CPX-351 vs. conventional chemotherapy cardiotoxicity in high-risk AML: a post hoc phase III trial analysis.

Cardio-oncology (London, England)·2025

Related Experiment Video

Updated: Feb 2, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K

Deep Learning With Spiking Neurons: Opportunities and Challenges.

Michael Pfeiffer1, Thomas Pfeil1

  • 1Bosch Center for Artificial Intelligence, Robert Bosch GmbH, Renningen, Germany.

Frontiers in Neuroscience
|November 10, 2018
PubMed
Summary

Spiking neural networks (SNNs) offer efficient, low-power AI by mimicking the brain. This review categorizes SNN training methods and discusses their hardware implementation, highlighting opportunities for event-based sensors and on-chip learning.

Keywords:
binary networksdeep learningevent-based computingneural networksneuromorphic engineeringspiking neurons

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K
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

7.5K

Related Experiment Videos

Last Updated: Feb 2, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K
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

7.5K

Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning

Background:

  • Spiking neural networks (SNNs) process information using sparse, asynchronous binary signals, mimicking biological systems.
  • SNNs on neuromorphic hardware offer low power consumption, fast inference, and event-driven processing, making them suitable for efficient deep neural network implementation.
  • Deep learning is the dominant method for many machine learning tasks, but efficient hardware implementation remains a challenge.

Purpose of the Study:

  • To review the opportunities presented by deep spiking networks.
  • To investigate the challenges in training SNNs for competitive performance and efficient hardware mapping.
  • To categorize SNN training methods, summarizing their pros and cons.

Main Methods:

  • Review of various SNN training methods: conversion of deep networks, constrained training, spiking backpropagation, and biologically motivated STDP variants.
  • Categorization and comparative analysis of different SNN training approaches.
  • Discussion of the relationship between SNNs and binary networks for hardware efficiency.
  • Comparison of neuromorphic hardware platforms and their suitability for deep SNNs.

Main Results:

  • Deep spiking networks present significant opportunities for efficient AI, particularly with event-based sensors and temporal coding.
  • Various training methods exist, each with specific advantages and disadvantages for SNN implementation.
  • Neuromorphic hardware platforms are crucial for realizing the potential of deep SNNs in real-world applications.
  • SNNs and conventional machine learning have complementary strengths that can be exploited for specific tasks.

Conclusions:

  • Deep spiking networks, trained effectively, can rival conventional deep learning while offering superior efficiency on neuromorphic hardware.
  • Further research and development in SNN training and hardware co-design are needed to fully exploit their potential.
  • The integration of SNNs with event-based sensors and on-chip learning capabilities opens new avenues for intelligent systems.