On the public citation graph

Neural networks: A biased overview

https://doi.org/10.1007/bf01014882
0 signed · 46 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4757-5858-0openalex-snapshot
Model Neural Networks and Behavior
inferredcites →https://doi.org/10.1007/978-3-642-88163-3openalex-snapshot
Self-Organization and Associative Memory
inferredcites →https://doi.org/10.1007/bf01071380openalex-snapshot
Principles of neurodynamics
inferredcites →https://doi.org/10.1007/bf01304440openalex-snapshot
Learning and pattern recognition in spin glass models
inferredcites →https://doi.org/10.1007/bf02478259openalex-snapshot
A logical calculus of the ideas immanent in nervous activity
inferredcites →https://doi.org/10.1007/bfb0057505openalex-snapshot
Heidelberg Colloquium on Glassy Dynamics
inferredcites →https://doi.org/10.1007/bfb0057534openalex-snapshot
Nonlinear neural networks: Efficient storage and retrieval of information
inferredcites →https://doi.org/10.1016/0025-5564(74)90031-5openalex-snapshot
The existence of persistent states in the brain
inferredcites →https://doi.org/10.1016/0167-2789(86)90227-7openalex-snapshot
Evolution, games, and learning: Models for adaptation in machines and nature. An introduction to the Proceedings of the CNLS Conference, Los Alamos, May 1985
inferredcites →https://doi.org/10.1016/0370-1573(87)90096-2openalex-snapshot
Artificial intelligence and large scale computation: A physics perspective
inferredcites →https://doi.org/10.1038/323533a0openalex-snapshot
Learning representations by back-propagating errors
inferredcites →https://doi.org/10.1038/327662a0openalex-snapshot
Understanding physicists' brains
inferredcites →https://doi.org/10.1038/328571a0openalex-snapshot
Modelling for its own sake
inferredcites →https://doi.org/10.1051/jphys:01987004805074100openalex-snapshot
Zero temperature parallel dynamics for infinite range spin glasses and neural networks
inferredcites →https://doi.org/10.1073/pnas.79.8.2554openalex-snapshot
Neural networks and physical systems with emergent collective computational abilities.
inferredcites →https://doi.org/10.1103/physreva.35.380openalex-snapshot
Associative recall of memory without errors
inferredcites →https://doi.org/10.1103/physrevlett.55.1530openalex-snapshot
Storing Infinite Numbers of Patterns in a Spin-Glass Model of Neural Networks
inferredcites →https://doi.org/10.1103/physrevlett.59.359openalex-snapshot
Exact solution of a layered neural network model
inferredcites →https://doi.org/10.1103/revmodphys.58.801openalex-snapshot
Spin glasses: Experimental facts, theoretical concepts, and open questions
inferredcites →https://doi.org/10.1109/massp.1987.1165576openalex-snapshot
An introduction to computing with neural nets
inferredcites →https://doi.org/10.1209/0295-5075/2/3/003openalex-snapshot
Storing and Retrieving Information in a Layered Spin System
inferredcites →https://doi.org/10.1209/0295-5075/4/2/007openalex-snapshot
An Exactly Solvable Asymmetric Neural Network Model
inferredcites →https://doi.org/10.2307/2312103openalex-snapshot
Principles of Neurodynamics.
inferredcites →https://doi.org/10.7551/mitpress/5236.001.0001openalex-snapshot
Parallel Distributed Processing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/9780470110560.ch1openalex-snapshot
Theoretical and Empirical Approaches to Protein‐Structure Prediction and Analysis
inferred← citeshttps://doi.org/10.1007/978-1-4615-3686-4_1openalex-snapshot
Artificial Neural Networks that Learn Many-Body Physics
inferred← citeshttps://doi.org/10.1007/978-1-4899-2136-9_28openalex-snapshot
Long-Term Behavior of Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-642-51008-3_4openalex-snapshot
Synergetics, Predictability and Deterministic Chaos
inferred← citeshttps://doi.org/10.1007/978-3-642-79814-6_9openalex-snapshot
Layered Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-642-97171-6_9openalex-snapshot
Layered Neural Networks
inferred← citeshttps://doi.org/10.1007/bf00201984openalex-snapshot
Variable threshold as a model for selective attention, (de)sensitization, and anesthesia in associative neural networks
inferred← citeshttps://doi.org/10.1007/bf01025862openalex-snapshot
Stochastic analyses of the dynamics of generalized Little-Hopfield-Hemmen type neural networks
inferred← citeshttps://doi.org/10.1007/bfb0104277openalex-snapshot
Neural networks: New tools for modelling and data analysis in science
inferred← citeshttps://doi.org/10.1016/0168-9002(91)90039-sopenalex-snapshot
Tagging the decays of the Z0 boson into b quark pairs with a neural network classifier
inferred← citeshttps://doi.org/10.1016/b978-0-444-88035-2.50005-5openalex-snapshot
INTRODUCTION TO NEURAL NETWORKS
inferred← citeshttps://doi.org/10.1016/b978-0-444-89178-5.50049-xopenalex-snapshot
ON THE RETRIEVAL IN HOPFIELD NETS WITH A FINITE RANGE OF CONNECTIONS
inferred← citeshttps://doi.org/10.1037//0882-7974.13.3.501openalex-snapshot
A molar entropy model of age differences in spatial memory.
inferred← citeshttps://doi.org/10.1051/jphys:0199000510170179700openalex-snapshot
Pattern recognition in Hopfield type networks with a finite range of connections
inferred← citeshttps://doi.org/10.1080/036107398244175openalex-snapshot
Age Differences in Entropy: Primary Versus Secondary Memory
inferred← citeshttps://doi.org/10.1088/0031-8949/1989/t29/004openalex-snapshot
Dynamics and Optimal Storage Capacity of Neural Networks
inferred← citeshttps://doi.org/10.1088/0031-9155/36/10/001openalex-snapshot
Neural network modelling
inferred← citeshttps://doi.org/10.1088/0305-4470/22/12/013openalex-snapshot
Layered neural networks
inferred← citeshttps://doi.org/10.1103/physreva.40.4700openalex-snapshot
Available phase space and robustness of the layered feed-forward neural network
inferred← citeshttps://doi.org/10.1103/physreva.41.590openalex-snapshot
Properties of sparsely connected excitatory neural networks
inferred← citeshttps://doi.org/10.15381/rif.v9i01.8614openalex-snapshot
A VIRTUAL LAB TO VISUALIZE THE PERFORMANCE OF THE HOPFIELD’S NEURAL NETWORK FOR ASSOCIATIVE CONTENT-ADDRESSABLE MEMORY
inferred← citeshttps://doi.org/10.4018/978-1-59904-849-9.ch156openalex-snapshot
Mathematical Modeling of Artificial Neural Networks
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.