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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

205
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
205
Signal Flow Graphs01:18

Signal Flow Graphs

318
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
318
SFG Algebra01:16

SFG Algebra

174
In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
174
Equivalent Resistance01:16

Equivalent Resistance

583
In circuit analysis, situations often arise where resistors are neither in series nor parallel configurations. To tackle such scenarios, three-terminal equivalent networks like the wye (Y) (Figure 1 (a)) or tee (T) and delta (Δ) (Figure 1 (b)) or pi (π) networks come into play. These networks offer versatile solutions and are frequently encountered in various applications, including three-phase electrical systems, electrical filters, and matching networks.
583
Neural Circuits01:25

Neural Circuits

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Comparing Two Novel LiDAR-Based Indices for Quantifying Forest Structural Complexity.

Ecology and evolution·2026
Same author

An anthropocene-framed transdisciplinary dialog at the chemistry-energy nexus.

Chemical science·2024
Same author

Associations between Subtalar Muscle Strength and Balance Performance in Healthy Young and Old Adults.

Gerontology·2019
Same author

Tuning a robust system: N,O zinc guanidine catalysts for the ROP of lactide.

Dalton transactions (Cambridge, England : 2003)·2019
Same author

Site-specific binding of a water molecule to the sulfa drugs sulfamethoxazole and sulfisoxazole: a laser-desorption isomer-specific UV and IR study.

Physical chemistry chemical physics : PCCP·2018
Same author

Laser desorption single-conformation UV and IR spectroscopy of the sulfonamide drug sulfanilamide, the sulfanilamide-water complex, and the sulfanilamide dimer.

Physical chemistry chemical physics : PCCP·2017
Same journal

Cross-linguistic patterns of cognitive biases in large language models: a comparative study in English, Hebrew, and Russian.

Frontiers in artificial intelligence·2026
Same journal

From human-like AI to user adoption: the role of trust, attitude, and social influence in shaping behavioral intention.

Frontiers in artificial intelligence·2026
Same journal

Building large-scale English-Romanian literary translation resources with open models.

Frontiers in artificial intelligence·2026
Same journal

Editorial: GenAI in healthcare: technologies, applications and evaluation.

Frontiers in artificial intelligence·2026
Same journal

Logic, inference, understanding: cross-domain generalization for generative language models.

Frontiers in artificial intelligence·2026
Same journal

Label tree semantic losses for rich multi-class medical image segmentation.

Frontiers in artificial intelligence·2026
See all related articles

Related Experiment Video

Updated: Sep 12, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

6.1K

Deriving equivalent symbol-based decision models from feedforward neural networks.

Sebastian Seidel1, Uwe M Borghoff2

  • 1KNDS Deutschland GmbH & Co. KG, Munich, Germany.

Frontiers in Artificial Intelligence
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a method to extract interpretable decision trees from artificial intelligence (AI) feedforward neural networks (FNNs). This approach enhances AI transparency and trust by bridging symbolic and connectionist AI paradigms.

Keywords:
artificial neural networksconnectionismdecision treesexplainable AIsymbolic AI modelssymbolism

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

10.4K
An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.8K

Related Experiment Videos

Last Updated: Sep 12, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

6.1K
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

10.4K
An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.8K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Science

Background:

  • Artificial intelligence (AI) adoption is rapid, yet system opacity hinders trust.
  • Deep learning and natural language processing fuel AI advancements.
  • Interpretable AI is crucial for acceptance and accountability.

Purpose of the Study:

  • To derive interpretable symbolic models, specifically decision trees, from feedforward neural networks (FNNs).
  • To bridge the gap between connectionist and symbolic AI approaches.
  • To enhance trust and accountability in AI systems through transparency.

Main Methods:

  • A systematic methodology to extract symbolic components (fillers, roles, relationships) from FNNs.
  • Tracing neuron activation values and input configurations across network layers.
  • Mapping activations and inputs to decision tree edges for transparency.

Main Results:

  • Successfully derived decision trees that capture FNN decision processes.
  • Demonstrated scalability to deeper networks via iterative refinement.
  • Developed a prototype validating the extraction of symbolic representations from neural networks.

Conclusions:

  • The proposed method effectively translates complex FNNs into interpretable decision trees.
  • This approach enhances AI system trust and accountability.
  • The methodology offers a pathway for more transparent and understandable AI.