On the public citation graph

Neural networks with low levels of activity: Ising vs. McCulloch-Pitts neurons

0 signed · 52 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1002/phbl.19880440205openalex-snapshot

Spingläser und Hirngespinste: Physikalische Modelle des Lernens und Erkennens

inferredcites →https://doi.org/10.1007/bf00336738openalex-snapshot

On the storage capacity of an associative memory with randomly distributed storage elements

inferredcites →https://doi.org/10.1007/bf01312798openalex-snapshot

Martingale approach to neural networks with hierarchically structured information

inferredcites →https://doi.org/10.1007/bf01314534openalex-snapshot

Glauber dynamics of the Little-Hopfield model

inferredcites →https://doi.org/10.1007/bf02478259openalex-snapshot

A logical calculus of the ideas immanent in nervous activity

inferredcites →https://doi.org/10.1016/0003-4916(87)90092-3openalex-snapshot

Statistical mechanics of neural networks near saturation

inferredcites →https://doi.org/10.1038/222960a0openalex-snapshot

Non-Holographic Associative Memory

inferredcites →https://doi.org/10.1051/jphys:0198600470110185700openalex-snapshot

The ultrametric organization of memories in a neural network

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/10.1088/0305-4470/21/1/031openalex-snapshot

Optimal storage properties of neural network models

inferredcites →https://doi.org/10.1103/physreva.35.2293openalex-snapshot

Information storage in neural networks with low levels of activity

inferredcites →https://doi.org/10.1103/physreva.37.570openalex-snapshot

Neural networks with hierarchically correlated patterns

inferredcites →https://doi.org/10.1103/physrevlett.58.949openalex-snapshot

Learning of correlated patterns in spin-glass networks by local learning rules

inferredcites →https://doi.org/10.1209/0295-5075/4/9/005openalex-snapshot

Dynamical Learning Process for Recognition of Correlated Patterns in Symmetric Spin Glass Models

inferredcites →https://doi.org/10.1209/0295-5075/6/2/002openalex-snapshot

The Enhanced Storage Capacity in Neural Networks with Low Activity Level

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-1-4471-2001-8_1openalex-snapshot

Information and pattern capacities in neural associative memories with feedback for sparse memory patterns

inferred← citeshttps://doi.org/10.1007/978-1-4471-2063-6_200openalex-snapshot

Using Reduce for Replica Calculations

inferred← citeshttps://doi.org/10.1007/978-1-4612-0723-8_3openalex-snapshot

Associative Data Storage and Retrieval in Neural Networks

inferred← citeshttps://doi.org/10.1007/bf01312198openalex-snapshot

Replica symmetry breaking in attractor neural network models

inferred← citeshttps://doi.org/10.1007/bf01313840openalex-snapshot

Neural networks with low activity levels

inferred← citeshttps://doi.org/10.1007/bf01321917openalex-snapshot

Transients and basins of attraction in neutral network models

inferred← citeshttps://doi.org/10.1007/bf01323559openalex-snapshot

Phase transition in a Willshaw net

inferred← citeshttps://doi.org/10.1007/s00422-008-0275-4openalex-snapshot

Optimal learning rules for familiarity detection

inferred← citeshttps://doi.org/10.1016/0378-4371(96)00134-3openalex-snapshot

Averaging and finite-size analysis for disorder: The Hopfield model

inferred← citeshttps://doi.org/10.1016/b978-0-444-88400-8.50051-0openalex-snapshot

REFERENCES FROM ALL CONTRIBUTIONS

inferred← citeshttps://doi.org/10.1016/j.amc.2021.126716openalex-snapshot

Storing, learning and retrieving biased patterns

inferred← citeshttps://doi.org/10.1016/j.physa.2004.11.001openalex-snapshot

Gardner optimal capacity of the diluted Blume–Emery–Griffiths neural network

inferred← citeshttps://doi.org/10.1016/s0378-4371(00)00308-3openalex-snapshot

Mutual information and self-control of a fully-connected low-activity neural network

inferred← citeshttps://doi.org/10.1016/s0893-6080(00)00027-7openalex-snapshot

Mutual information of sparsely coded associative memory with self-control and ternary neurons

inferred← citeshttps://doi.org/10.1016/s0893-6080(96)00122-0openalex-snapshot

Informational Capacity and Recall Quality in Sparsely Encoded Hopfield-like Neural Network: Analytical Approaches and Computer Simulation

inferred← citeshttps://doi.org/10.1021/acsaelm.9b00702openalex-snapshot

Emerging Devices for Biologically Accurate Neuron

inferred← citeshttps://doi.org/10.1088/0305-4470/22/21/012openalex-snapshot

Increased storage capacity for hierarchically structured information in a neural network of Ising type

inferred← citeshttps://doi.org/10.1088/0305-4470/24/5/023openalex-snapshot

Associative memory: on the (puzzling) sparse coding limit

inferred← citeshttps://doi.org/10.1088/0305-4470/26/4/012openalex-snapshot

Statistical mechanics for neural networks with continuous-time dynamics

inferred← citeshttps://doi.org/10.1088/0305-4470/31/36/004openalex-snapshot

Retrieval dynamics of neural networks for sparsely coded sequential patterns

inferred← citeshttps://doi.org/10.1088/0954-898x/2/4/009openalex-snapshot

Sparsely coded associative memories: capacity and dynamical properties

inferred← citeshttps://doi.org/10.1088/0954-898x/3/2/006openalex-snapshot

Information capacity in recurrent McCulloch–Pitts networks with sparsely coded memory states

inferred← citeshttps://doi.org/10.1088/0954-898x/4/2/005openalex-snapshot

Associative memory for patterns with different bias

inferred← citeshttps://doi.org/10.1088/0954-898x/4/3/005openalex-snapshot

Sparsely connected Hopfield networks for the recognition of correlated pattern sets

inferred← citeshttps://doi.org/10.1088/0954-898x/4/4/006openalex-snapshot

Informational characteristics of neural networks capable of associative learning based on Hebbian plasticity

inferred← citeshttps://doi.org/10.1088/0954-898x/6/4/001openalex-snapshot

Quality and efficiency of retrieval for Willshaw-like autoassociative networks. I. Correction

inferred← citeshttps://doi.org/10.1088/0954-898x/6/4/002openalex-snapshot

Quality and efficiency of retrieval for Willshaw-like autoassociative networks. II. Recognition

inferred← citeshttps://doi.org/10.1088/1748-0221/14/11/p11019openalex-snapshot

Comparative study between the developed simulators based on the direct and indirect methods for calculation of the energy spectrum of the neutron source

inferred← citeshttps://doi.org/10.1103/physreve.60.2141openalex-snapshot

Information theoretical performance measure for associative memories and its application to neural networks

inferred← citeshttps://doi.org/10.1103/physrevlett.80.2961openalex-snapshot

Self-Control in Sparsely Coded Networks

inferred← citeshttps://doi.org/10.1109/icnn.1993.298675openalex-snapshot

Associative storage and retrieval of highly correlated natural pattern sets in diluted Hopfield networks

inferred← citeshttps://doi.org/10.1109/isninc.1995.480838openalex-snapshot

Associative memory based on sparsely encoded Hopfield-like neural network

inferred← citeshttps://doi.org/10.1140/epjb/s10051-026-01163-wopenalex-snapshot

Retrieval properties of attractor neural networks with regulated levels of activity

inferred← citeshttps://doi.org/10.1142/s012906570700110xopenalex-snapshot

ADAPTIVE THRESHOLDS FOR NEURAL NETWORKS WITH SYNAPTIC NOISE

inferred← citeshttps://doi.org/10.1162/neco.1992.4.5.703openalex-snapshot

On the Information Storage Capacity of Local Learning Rules

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1002836openalex-snapshot

Soft-bound Synaptic Plasticity Increases Storage Capacity

inferred← citeshttps://doi.org/10.48550/arxiv.cond-mat/9806078openalex-snapshot

Mutual Information of Three-State Low Activity Diluted Neural Networks with Self-Control

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.