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

2.5K
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...
2.5K
Learning Disabilities01:25

Learning Disabilities

586
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...
586
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

464
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...
464
Observational Learning01:12

Observational Learning

878
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...
878
Introduction to Learning01:18

Introduction to Learning

1.0K
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...
1.0K

You might also read

Related Articles

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

Sort by
Same journal

CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training.

Neuroinformatics·2026
Same journal

Increasing the Reliability of Functional Connectivity by Predicting Long-Scan Functional Connectivity based on Short-Scan Functional Connectivity: Model Exploration, Explanation, Validation, and Application.

Neuroinformatics·2026
Same journal

HESREN: A Derivative-Informed Reservoir Framework for Detecting Transient Neural Events and Windowless Estimation of Dynamic Functional Connectivity.

Neuroinformatics·2026
Same journal

Computational Morphometry of Peripheral Nerves: A Pipeline Perspective on Reproducibility and Generalization.

Neuroinformatics·2026
Same journal

Multimodal Branched Transport Infers Anatomically Aligned Brain Reaction Maps.

Neuroinformatics·2026
Same journal

Model Validation Pipeline Against Longitudinal Alzheimer's Biomarker Data.

Neuroinformatics·2026
See all related articles

Related Experiment Video

Updated: Jan 26, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K

NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods.

Daniel Rasmussen1

  • 1Applied Brain Research Inc., Waterloo, ON, Canada. daniel.rasmussen@appliedbrainresearch.com.

Neuroinformatics
|April 12, 2019
PubMed
Summary
This summary is machine-generated.

NengoDL integrates neuromorphic modeling and deep learning, enabling the creation and optimization of biologically detailed neural networks. This framework facilitates efficient simulation and training of complex neural models.

Keywords:
Computational neuroscienceDeep learningNengoTensorFlow

More Related Videos

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

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.7K
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

9.8K

Related Experiment Videos

Last Updated: Jan 26, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.7K
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

9.8K

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Neuromorphic modeling and deep learning offer complementary strengths for understanding and simulating neural systems.
  • Existing frameworks may not seamlessly integrate these two powerful approaches.
  • Optimizing biologically detailed neural models often requires advanced training techniques.

Purpose of the Study:

  • To introduce NengoDL, a unified software framework for combining neuromorphic modeling and deep learning.
  • To demonstrate the framework's capability in constructing, simulating, and training biologically detailed neural models.
  • To provide basic usage examples, benchmarking results, and implementation details of NengoDL.

Main Methods:

  • Development of a unified software framework (NengoDL) integrating neuromorphic and deep learning components.
  • Construction of biologically detailed neural models with intermixed deep learning elements (e.g., convolutional networks).
  • Application of deep learning training methods for parameter optimization of neural models.
  • Benchmarking to assess the efficiency and performance of NengoDL simulations.

Main Results:

  • NengoDL successfully integrates neuromorphic modeling with deep learning functionalities.
  • The framework allows for efficient simulation of complex, biologically detailed neural models.
  • Deep learning training methods can be effectively applied to optimize neural model parameters within NengoDL.
  • Basic usage examples and benchmarking data demonstrate the framework's utility and performance.

Conclusions:

  • NengoDL provides a powerful and user-friendly platform for researchers at the intersection of neuroscience and artificial intelligence.
  • The framework facilitates the development and optimization of sophisticated neural models by leveraging deep learning techniques.
  • NengoDL represents a significant advancement in simulating and understanding neural computation.