Neural Computation
E80658
Neural Computation is a peer-reviewed scientific journal focusing on theoretical and computational aspects of neural systems, machine learning, and artificial intelligence.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Neural Computation canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T645519 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neural Computation Context triple: [A fast learning algorithm for deep belief nets, publishedIn, Neural Computation]
-
A.
Hopfield networks
Hopfield networks are recurrent artificial neural networks that serve as content-addressable memory systems, storing patterns as stable states and retrieving them through dynamics that minimize an energy function.
-
B.
SyNAPSE neuromorphic computing program
The SyNAPSE neuromorphic computing program is a DARPA initiative to develop brain-inspired electronic systems that emulate neural architectures for highly efficient, scalable cognitive computing.
-
C.
“Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
-
D.
Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
-
E.
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence is a leading peer-reviewed journal that publishes cutting-edge research in computer vision, pattern recognition, and machine learning.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Neural Computation Target entity description: Neural Computation is a peer-reviewed scientific journal focusing on theoretical and computational aspects of neural systems, machine learning, and artificial intelligence.
-
A.
Hopfield networks
Hopfield networks are recurrent artificial neural networks that serve as content-addressable memory systems, storing patterns as stable states and retrieving them through dynamics that minimize an energy function.
-
B.
SyNAPSE neuromorphic computing program
The SyNAPSE neuromorphic computing program is a DARPA initiative to develop brain-inspired electronic systems that emulate neural architectures for highly efficient, scalable cognitive computing.
-
C.
“Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
-
D.
Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
-
E.
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence is a leading peer-reviewed journal that publishes cutting-edge research in computer vision, pattern recognition, and machine learning.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
academic journal
ⓘ
peer-reviewed journal ⓘ scientific journal ⓘ |
| academicDiscipline |
artificial intelligence
ⓘ
computational neuroscience ⓘ computer science ⓘ machine learning ⓘ neuroscience ⓘ |
| coversTopic |
algorithmic aspects of neural processing
ⓘ
computational theories of brain function ⓘ learning in artificial neural systems ⓘ learning in biological neural systems ⓘ optimization methods for neural networks ⓘ probabilistic models of neural activity ⓘ representation learning ⓘ |
| field |
computational modeling
ⓘ
neural computation ⓘ theoretical computer science ⓘ |
| focusesOn |
artificial intelligence
ⓘ
computational aspects of neural systems ⓘ computational models of cognition ⓘ computational models of perception ⓘ dynamical systems in neural computation ⓘ information processing in neural systems ⓘ learning algorithms ⓘ machine learning ⓘ neural networks ⓘ statistical learning theory ⓘ theoretical aspects of neural systems ⓘ theoretical neuroscience ⓘ |
| hasFormat | research journal ⓘ |
| hasReviewProcess | single-blind peer review ⓘ |
| hasScope |
computational experiments
ⓘ
interdisciplinary research ⓘ theory-driven studies ⓘ |
| language | English ⓘ |
| medium |
online
ⓘ
print ⓘ |
| peerReviewed | true ⓘ |
| publishes |
computational modeling studies
ⓘ
original research articles ⓘ review articles ⓘ theoretical papers ⓘ |
| publishingModel | subscription-based journal ⓘ |
| targetAudience |
academics
ⓘ
graduate students ⓘ researchers ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Neural Computation Description of subject: Neural Computation is a peer-reviewed scientific journal focusing on theoretical and computational aspects of neural systems, machine learning, and artificial intelligence.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
A fast learning algorithm for deep belief nets