Binding threshold units with artificial oscillatory neurons | Paper Citation Record

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This paper cites Hebbian learning from first principles.

Binding threshold units with artificial oscillatory neurons Hebbian learning from first principles

Reference 1

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This paper cites In search of dispersed memories: Generative diffusion models are associative memory networks.

Binding threshold units with artificial oscillatory neurons In search of dispersed memories: Generative diffusion models are associative memory networks

Reference 2

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This paper cites Using fast weights to attend to the recent past.

Binding threshold units with artificial oscillatory neurons Using fast weights to attend to the recent past

Reference 3

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This paper cites Deep equilibrium models.

Binding threshold units with artificial oscillatory neurons Deep equilibrium models

Reference 4

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This paper cites Universal approximation bounds for superpositions of a sigmoidal function.

Binding threshold units with artificial oscillatory neurons Universal approximation bounds for superpositions of a sigmoidal function

Reference 5

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This paper cites JAX: composable transformations of Python+NumPy programs, 2018.

Binding threshold units with artificial oscillatory neurons JAX: composable transformations of Python+NumPy programs, 2018

Reference 6

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This paper cites Neuronal oscillations in cortical networks.science, 304(5679):1926– 1929, 2004.

Binding threshold units with artificial oscillatory neurons Neuronal oscillations in cortical networks.science, 304(5679):1926– 1929, 2004

Reference 7

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This paper cites Symbolic discovery of optimization algorithms.

Binding threshold units with artificial oscillatory neurons Symbolic discovery of optimization algorithms

Reference 8

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Observation fb495ea3-e7cb-453a-a0e6-9f55a34fa43a · outbound

This paper cites On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.

Binding threshold units with artificial oscillatory neurons On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Reference 9

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Observation d67c4d7c-b3c0-4066-922d-1a5801f49bc0 · outbound

This paper cites Approximation by superpositions of a sigmoidal function.

Binding threshold units with artificial oscillatory neurons Approximation by superpositions of a sigmoidal function

Reference 10

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Observation 57aeab22-ac0a-4c94-baf7-1bde6ea39780 · outbound

This paper cites The DeepMind JAX Ecosystem, 2020.

Binding threshold units with artificial oscillatory neurons The DeepMind JAX Ecosystem, 2020

Reference 11

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Observation 6b1f3ff8-6d6d-49ec-8bb2-a774e47a9e04 · outbound

This paper cites Synchronization in complex networks of phase oscillators: A survey.

Binding threshold units with artificial oscillatory neurons Synchronization in complex networks of phase oscillators: A survey

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.754107Z

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Observation 7dd3fdc9-63c3-47fd-b13a-919261a6eb0a · outbound

This paper cites Brain oscillations and memory.

Binding threshold units with artificial oscillatory neurons Brain oscillations and memory

Reference 13

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This paper cites Training spiking neural networks using lessons from deep learning.

Binding threshold units with artificial oscillatory neurons Training spiking neural networks using lessons from deep learning

Reference 14

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Observation de7faadf-be35-475b-a25a-00df17b15d8f · outbound

This paper cites Nonlinear neural networks: Principles, mechanisms, and architectures.

Binding threshold units with artificial oscillatory neurons Nonlinear neural networks: Principles, mechanisms, and architectures

Reference 15

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Observation 94c90121-0fb2-436a-acd6-e4cf5fb8cc28 · outbound

This paper cites Hagberg, Daniel A.

Binding threshold units with artificial oscillatory neurons Hagberg, Daniel A

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.720079Z

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This paper cites Common oscillatory mechanisms across multiple memory systems.

Binding threshold units with artificial oscillatory neurons Common oscillatory mechanisms across multiple memory systems

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.711048Z

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Observation 64934359-d2a7-4bc0-a96e-41bdd84f8191 · outbound

This paper cites Long short-term memory.Neural computation, 9(8):1735–1780, 1997.

Binding threshold units with artificial oscillatory neurons Long short-term memory.Neural computation, 9(8):1735–1780, 1997

Reference 18

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Observation f3217990-9230-4308-89d8-8249611a37ef · outbound

This paper cites A universal abstraction for hierarchical hopfield networks.

Binding threshold units with artificial oscillatory neurons A universal abstraction for hierarchical hopfield networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.695263Z

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This paper cites Energy transformer.

Binding threshold units with artificial oscillatory neurons Energy transformer

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.686070Z

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Observation 5559d98c-8163-4000-a94e-33712d123a79 · outbound

This paper cites Neural networks and physical systems with emergent collective computational abilities.

Binding threshold units with artificial oscillatory neurons Neural networks and physical systems with emergent collective computational abilities

Reference 21

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This paper cites Neurons with graded response have collective computational properties like those of two-state neurons.

Binding threshold units with artificial oscillatory neurons Neurons with graded response have collective computational properties like those of two-state neurons

Reference 22

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This paper cites Oscillatory neurocomputers with dynamic connectivity.

Binding threshold units with artificial oscillatory neurons Oscillatory neurocomputers with dynamic connectivity

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.665420Z

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Observation a64f37a8-0c3a-45d8-b9de-684f2e0a626a · outbound

This paper cites Weakly connected neural networks , volume 126.

Binding threshold units with artificial oscillatory neurons Weakly connected neural networks , volume 126

Reference 24

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Observation aa21f891-9c6e-47b5-b16b-e4137923b3fe · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Binding threshold units with artificial oscillatory neurons Lora: Low-rank adaptation of large language models

Reference 25

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Observation fd009de9-40bd-4db8-b63c-2c168be70c30 · outbound

This paper cites Exploring weight symmetry in deep neural networks.

Binding threshold units with artificial oscillatory neurons Exploring weight symmetry in deep neural networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.645467Z

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Observation bb70ac00-d225-4463-8294-c99de07c4f9c · outbound

This paper cites Weakly pulse-coupled oscillators, fm interactions, synchronization, and oscillatory associative memory.

Binding threshold units with artificial oscillatory neurons Weakly pulse-coupled oscillators, fm interactions, synchronization, and oscillatory associative memory

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.635987Z

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Observation 2e1e607a-9864-4f95-9f66-402f435345ac · outbound

This paper cites Simple model of spiking neurons.

Binding threshold units with artificial oscillatory neurons Simple model of spiking neurons

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.626724Z

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Observation 778a74dc-3e66-4ca0-8399-00a283f85bb1 · outbound

This paper cites A new hybrid routing protocol using a modified k-means clustering algorithm and continuous hopfield network for vanet.

Binding threshold units with artificial oscillatory neurons A new hybrid routing protocol using a modified k-means clustering algorithm and continuous hopfield network for vanet

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.618113Z

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Observation 8ec35868-813c-49d8-9c05-0dc7780b1222 · outbound

This paper cites A spacetime perspective on dynamical computation in neural information processing systems.

Binding threshold units with artificial oscillatory neurons A spacetime perspective on dynamical computation in neural information processing systems

Reference 30

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Observation 543ac4e8-af7c-4148-94cf-4ad596cab3aa · outbound

This paper cites On Neural Differential Equations.

Binding threshold units with artificial oscillatory neurons On Neural Differential Equations

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T23:51:16.224956Z

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Observation e5cb7cb7-25f6-432b-8e58-9ff7211e46dd · outbound

This paper cites Equinox: neural networks in JAX via callable PyTrees and filtered transformations.

Binding threshold units with artificial oscillatory neurons Equinox: neural networks in JAX via callable PyTrees and filtered transformations

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.605394Z

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Observation 6c700d09-73b4-472e-bf38-113c55e7da0e · outbound

This paper cites Biological computations: limitations of attractor-based formalisms and the need for transients.

Binding threshold units with artificial oscillatory neurons Biological computations: limitations of attractor-based formalisms and the need for transients

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.597124Z

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Observation a703b1ae-584d-4b68-8569-8039b3140f1e · outbound

This paper cites Hierarchical Associative Memory.

Binding threshold units with artificial oscillatory neurons Hierarchical Associative Memory

Reference 34

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Observation 535b201f-21fe-452d-96db-3cf12ed9a173 · outbound

This paper cites Dense associative memory for pattern recognition.

Binding threshold units with artificial oscillatory neurons Dense associative memory for pattern recognition

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T23:51:16.239350Z

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This paper cites Chemical Oscillations, Waves, and Turbulence.

Binding threshold units with artificial oscillatory neurons Chemical Oscillations, Waves, and Turbulence

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.584814Z

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This paper cites Deep learning.

Binding threshold units with artificial oscillatory neurons Deep learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T23:51:16.245006Z

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This paper cites Mnist handwritten digit database.

Binding threshold units with artificial oscillatory neurons Mnist handwritten digit database

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T23:51:16.247614Z

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Observation 1d0ce5fd-135f-4732-a435-c7d15827681e · outbound

This paper cites Image segmentation with traveling waves in an exactly solvable recurrent neural network.

Binding threshold units with artificial oscillatory neurons Image segmentation with traveling waves in an exactly solvable recurrent neural network

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.568317Z

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Observation 06c9d7f1-f557-4dfc-839d-1cedb9991e76 · outbound

This paper cites Convolutional neural networks as a model of the visual system: Past, present, and future.

Binding threshold units with artificial oscillatory neurons Convolutional neural networks as a model of the visual system: Past, present, and future

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.560474Z

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Observation e12196fa-8a19-4335-a5eb-8633a21264cc · outbound

This paper cites Non-Abelian Kuramoto models and synchronization.

Binding threshold units with artificial oscillatory neurons Non-Abelian Kuramoto models and synchronization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.552286Z

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Observation f4c4553d-26fd-4c21-a3f5-9edbe28f6eb9 · outbound

This paper cites Rotating features for object discovery.

Binding threshold units with artificial oscillatory neurons Rotating features for object discovery

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.544223Z

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Observation 4c620061-cfb6-4f84-abe7-6d16da33839a · outbound

This paper cites Networks of spiking neurons: the third generation of neural network models.

Binding threshold units with artificial oscillatory neurons Networks of spiking neurons: the third generation of neural network models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T23:51:16.261544Z

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Observation 750522d9-cbe9-4738-b1b3-57ce6f779cee · outbound

This paper cites Characterizing cancer subtypes as attractors of hopfield networks.

Binding threshold units with artificial oscillatory neurons Characterizing cancer subtypes as attractors of hopfield networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.531601Z

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Observation ba7a14c2-a1ee-4baf-92ed-9f32f744c2dd · outbound

This paper cites Oscillator array models for associative memory and pattern recognition.

Binding threshold units with artificial oscillatory neurons Oscillator array models for associative memory and pattern recognition

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.523631Z

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Observation 2e1e2c48-d99b-4d54-94d3-91c3d74ab894 · outbound

This paper cites Almost global convergence to practical synchronization in the generalized kuramoto model on networks over the n-sphere.

Binding threshold units with artificial oscillatory neurons Almost global convergence to practical synchronization in the generalized kuramoto model on networks over the n-sphere

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.515276Z

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This paper cites A logical calculus of the ideas immanent in nervous activity.

Binding threshold units with artificial oscillatory neurons A logical calculus of the ideas immanent in nervous activity

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.507122Z

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Observation 0d8f0f2a-8f4f-44dc-9ec0-8cf81765fa83 · outbound

This paper cites Artificial Kuramoto Oscillatory Neurons.

Binding threshold units with artificial oscillatory neurons Artificial Kuramoto Oscillatory Neurons

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T23:51:16.275524Z

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Observation 02918196-c9a3-40a4-8ae7-facef2dab9c0 · outbound

This paper cites Memorization to generalization: The emergence of diffusion models from associative memory.

Binding threshold units with artificial oscillatory neurons Memorization to generalization: The emergence of diffusion models from associative memory

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.498947Z

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This paper cites Hopfield Networks is All You Need.

Binding threshold units with artificial oscillatory neurons Hopfield Networks is All You Need

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T23:51:16.281240Z

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This paper cites End-to-end differentiable clustering with associative memories.

Binding threshold units with artificial oscillatory neurons End-to-end differentiable clustering with associative memories

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.490003Z

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This paper cites Associative memories via predictive coding.

Binding threshold units with artificial oscillatory neurons Associative memories via predictive coding

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.481478Z

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Observation 07535f2b-8f27-467c-96cf-21a4710f6bc9 · outbound

This paper cites Reducing the ratio between learning complexity and number of time varying variables in fully recurrent nets.

Binding threshold units with artificial oscillatory neurons Reducing the ratio between learning complexity and number of time varying variables in fully recurrent nets

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.472538Z

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Observation 0fb36519-5959-43ef-9cad-21cf92b56d89 · outbound

This paper cites Deep learning in neural networks: An overview.

Binding threshold units with artificial oscillatory neurons Deep learning in neural networks: An overview

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.463310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2e4a9d85-ab78-4a7f-8497-a58d012d5dce · outbound

This paper cites Large Associative Memory Problem in Neurobiology and Machine Learning.

Binding threshold units with artificial oscillatory neurons Large Associative Memory Problem in Neurobiology and Machine Learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T23:51:16.295686Z

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Observation 09a0346a-9d99-4d61-99c6-20109f9a55b9 · outbound

This paper cites Processes and measurements: a framework for understanding neural oscillations in field potentials.

Binding threshold units with artificial oscillatory neurons Processes and measurements: a framework for understanding neural oscillations in field potentials

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.454701Z

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Observation 86e686ac-c53a-472c-8dfb-ce0601bb8a7f · outbound

This paper cites Collective dynamics of ‘small-world’networks.

Binding threshold units with artificial oscillatory neurons Collective dynamics of ‘small-world’networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T23:51:16.301574Z

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Observation 58c6b79a-1226-451f-9ce6-d5984b07afc3 · outbound

This paper cites Modern hopfield networks and attention for immune repertoire classification.

Binding threshold units with artificial oscillatory neurons Modern hopfield networks and attention for immune repertoire classification

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.441416Z

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Observation 10be6527-4af5-4445-8e75-5509418e82f1 · outbound

This paper cites encoding capacity.

Binding threshold units with artificial oscillatory neurons encoding capacity

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:51:16.432067Z

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