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Paper Citation Record · LEDGER

Three factor delay learning rules for spiking neural networks

As of 7 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2601.00668.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2601.00668 v2

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measured 41 of 41 reference resolution

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measured 41 of 41 standing notices

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measured 0 of 0 inbound itemization

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41 of 41 outbound references displayed

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Outbound references

Observation c78d3037-9a82-4506-bb56-08d57659cb89 · outbound

This paper cites Bittar and P.

Three factor delay learning rules for spiking neural networks Bittar and P

Reference 1

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Observation 8da12266-9e76-44e5-a829-f1a0a2573f6b · outbound

This paper cites Yinet al.Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks.Nature Machine Intelligence, 3(10):905–913, October 2021.

Three factor delay learning rules for spiking neural networks Yinet al.Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks.Nature Machine Intelligence, 3(10):905–913, October 2021

Reference 2

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Observation 7429504c-f51d-4754-89f6-407c4c3658b6 · outbound

This paper cites Deckerset al.Co-learning synaptic delays, weights and adaptation in spiking neural networks.Frontiers in Neuroscience, 18:1360300, April 2024.

Three factor delay learning rules for spiking neural networks Deckerset al.Co-learning synaptic delays, weights and adaptation in spiking neural networks.Frontiers in Neuroscience, 18:1360300, April 2024

Reference 3

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Observation f33b2175-0883-49ff-8abe-56b91d905c86 · outbound

This paper cites Hammouamriet al.Learning delays in spiking neural networks using dilated convolutions with learnable spacings.

Three factor delay learning rules for spiking neural networks Hammouamriet al.Learning delays in spiking neural networks using dilated convolutions with learnable spacings

Reference 4

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Observation caa0262a-6efa-4c77-8618-211ab0541f56 · outbound

This paper cites Sunet al.Learnable axonal delay in spiking neural networks improves spoken word recognition.Frontiers in Neuroscience, 17:1275944, November 2023.

Three factor delay learning rules for spiking neural networks Sunet al.Learnable axonal delay in spiking neural networks improves spoken word recognition.Frontiers in Neuroscience, 17:1275944, November 2023

Reference 5

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Observation 5d5dcf83-21d1-4ede-987a-9714ce6365a5 · outbound

This paper cites an unresolved cited work.

Three factor delay learning rules for spiking neural networks Unresolved cited work

Reference 6

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Observation e3775ed2-06fb-4303-92a5-cee3ed52a45c · outbound

This paper cites an unresolved cited work.

Three factor delay learning rules for spiking neural networks Unresolved cited work

Reference 7

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Observation 9c85a30d-2955-495b-8b72-41c2897bc172 · outbound

This paper cites an unresolved cited work.

Three factor delay learning rules for spiking neural networks Unresolved cited work

Reference 8

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Observation 05deb2ff-1754-4b26-8c4e-d2b4d871d6ab · outbound

This paper cites Debanneet al.Axon Physiology.Physiological Reviews, 91(2):555– 602, April 2011.

Three factor delay learning rules for spiking neural networks Debanneet al.Axon Physiology.Physiological Reviews, 91(2):555– 602, April 2011

Reference 9

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Observation 60718a76-5727-48a6-a028-71a08c2da589 · outbound

This paper cites London and M.

Three factor delay learning rules for spiking neural networks London and M

Reference 10

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Observation a34a8620-533d-48ec-9592-1222e1de6d56 · outbound

This paper cites an unresolved cited work.

Three factor delay learning rules for spiking neural networks Unresolved cited work

Reference 11

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Observation a4c18bc8-bd24-4973-b1dd-14d6409a7726 · outbound

This paper cites Sunet al.Axonal Delay as a Short-Term Memory for Feed Forward Deep Spiking Neural Networks.

Three factor delay learning rules for spiking neural networks Sunet al.Axonal Delay as a Short-Term Memory for Feed Forward Deep Spiking Neural Networks

Reference 12

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Observation fb342647-1a78-4ed3-8e8b-37084fd4a93c · outbound

This paper cites Khalfaoui-Hassaniet al.Dilated convolution with learnable spacings.

Three factor delay learning rules for spiking neural networks Khalfaoui-Hassaniet al.Dilated convolution with learnable spacings

Reference 13

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Observation a938df76-8b35-45bf-9339-5bdf5576e95d · outbound

This paper cites Learning Delays Through Gradients and Structure: Emergence of Spatiotemporal Patterns in Spiking Neural Networks.

Three factor delay learning rules for spiking neural networks Learning Delays Through Gradients and Structure: Emergence of Spatiotemporal Patterns in Spiking Neural Networks

Reference 14

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Observation a95f5986-de3e-4179-80c8-70e0eaca7790 · outbound

This paper cites an unresolved cited work.

Three factor delay learning rules for spiking neural networks Unresolved cited work

Reference 15

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Observation 2234b28e-b4ec-418b-877c-e7f5e8c7a8b3 · outbound

This paper cites Efficient Event-based Delay Learning in Spiking Neural Networks.

Three factor delay learning rules for spiking neural networks Efficient Event-based Delay Learning in Spiking Neural Networks

Reference 16

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Observation 9533f5a0-5351-4ca7-aec7-c3d5c93f9abf · outbound

This paper cites Nowotnyet al.Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks.Neuromorphic Computing and Engineering, 5(1):014001, March 2025.

Three factor delay learning rules for spiking neural networks Nowotnyet al.Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks.Neuromorphic Computing and Engineering, 5(1):014001, March 2025

Reference 17

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Observation 85a55c4d-fd91-4450-b0d1-5afe4eeb6e88 · outbound

This paper cites Jaderberget al.Decoupled neural interfaces using synthetic gradients.

Three factor delay learning rules for spiking neural networks Jaderberget al.Decoupled neural interfaces using synthetic gradients

Reference 18

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Observation 31781c24-9a2d-4e86-9c9e-ddc552492fe5 · outbound

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Three factor delay learning rules for spiking neural networks Unresolved cited work

Reference 19

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Observation adb33ac7-d0ee-4a98-8b9f-c3842fdced04 · outbound

This paper cites Benzinget al.Optimal Kronecker-sum approximation of real time recurrent learning.

Three factor delay learning rules for spiking neural networks Benzinget al.Optimal Kronecker-sum approximation of real time recurrent learning

Reference 20

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Observation 7039b493-2e96-4a51-8e00-01c44236c1e1 · outbound

This paper cites Tallec and Y.

Three factor delay learning rules for spiking neural networks Tallec and Y

Reference 21

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Observation f5bb07ac-6837-4ca3-b7d1-0d595a797de0 · outbound

This paper cites Gerstneret al.Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules.

Three factor delay learning rules for spiking neural networks Gerstneret al.Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules

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Observation bf6058a8-768b-4f50-9c75-f31080b14d62 · outbound

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Three factor delay learning rules for spiking neural networks Unresolved cited work

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Observation 3d34af70-c1b1-4f21-b333-510ea4446ed5 · outbound

This paper cites Liaoet al.How important is weight symmetry in backpropagation? In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI’16, pp.

Three factor delay learning rules for spiking neural networks Liaoet al.How important is weight symmetry in backpropagation? In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI’16, pp

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Observation 5fdcae9c-3036-41fb-b9fa-6b4a63def536 · outbound

This paper cites Bi and M.-m.

Three factor delay learning rules for spiking neural networks Bi and M.-m

Reference 25

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Observation 0c1860df-874f-4e6f-a835-b33c38162947 · outbound

This paper cites Marschallet al.A unified framework of online learning algorithms for training recurrent neural networks.J.

Three factor delay learning rules for spiking neural networks Marschallet al.A unified framework of online learning algorithms for training recurrent neural networks.J

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Observation 1db8166e-7bd3-4c92-9f44-184ec0b29aa4 · outbound

This paper cites Zenke and S.

Three factor delay learning rules for spiking neural networks Zenke and S

Reference 27

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Observation 21a224a2-1aba-4526-a1c9-c62cfbcc3310 · outbound

This paper cites Bellecet al.A solution to the learning dilemma for recurrent networks of spiking neurons.Nature Communications, 11(1):3625, July 2020.

Three factor delay learning rules for spiking neural networks Bellecet al.A solution to the learning dilemma for recurrent networks of spiking neurons.Nature Communications, 11(1):3625, July 2020

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This paper cites Kaiseret al.Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE).Frontiers in Neuroscience, 14:424, May 2020.

Three factor delay learning rules for spiking neural networks Kaiseret al.Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE).Frontiers in Neuroscience, 14:424, May 2020

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Observation 8cce2d80-604e-49b8-9d7c-4feda1107f79 · outbound

This paper cites Bohnstinglet al.Online Spatio-Temporal Learning in Deep Neural Networks.IEEE Transactions on Neural Networks and Learning Systems, pp.

Three factor delay learning rules for spiking neural networks Bohnstinglet al.Online Spatio-Temporal Learning in Deep Neural Networks.IEEE Transactions on Neural Networks and Learning Systems, pp

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Observation 8246e98a-6167-4165-849a-79b72731d909 · outbound

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Three factor delay learning rules for spiking neural networks Unresolved cited work

Reference 31

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Observation 5be7cc02-1b06-4e7c-8beb-78ae7c55958d · outbound

This paper cites Gerstneret al.

Three factor delay learning rules for spiking neural networks Gerstneret al

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Observation 0f027131-b6b2-4c07-accd-572cdbb9a2d7 · outbound

This paper cites DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardware.

Three factor delay learning rules for spiking neural networks DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardware

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Observation 55fbb5d8-265a-4186-99a1-ca20fdec8a1f · outbound

This paper cites Wanget al.A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons.Frontiers in Neuroscience, 13:252, March 2019.

Three factor delay learning rules for spiking neural networks Wanget al.A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons.Frontiers in Neuroscience, 13:252, March 2019

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Observation b27de4ce-74c4-4f9e-8e53-72d5c66236f9 · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

Three factor delay learning rules for spiking neural networks Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

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Observation f18e84f4-7025-4114-9de9-a5cbb67ac9f3 · outbound

This paper cites Fanget al.Spikingjelly: An open-source machine learning in- frastructure platform for spike-based intelligence.Science Advances, 9(40):eadi1480, 2023.

Three factor delay learning rules for spiking neural networks Fanget al.Spikingjelly: An open-source machine learning in- frastructure platform for spike-based intelligence.Science Advances, 9(40):eadi1480, 2023

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Observation fe0fb308-3c4c-4751-86c9-965b9aada39c · outbound

This paper cites Baroniget al.Advancing spatio-temporal processing through adaptation in spiking neural networks.Nature Communications, 16(1):5776, July 2025.

Three factor delay learning rules for spiking neural networks Baroniget al.Advancing spatio-temporal processing through adaptation in spiking neural networks.Nature Communications, 16(1):5776, July 2025

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source=pdf_text observed=2026-08-03T13:05:04.915062Z digest=sha256:8f3e952d384beb879a3b8f0bebd125db7830b945b16a8b71174fe43a97858a5d

Observation 4a123ca9-beae-4f4b-a003-b5ff4d52ddcd · outbound

This paper cites Sch ¨oneet al.Scalable Event-by-Event Processing of Neuromorphic Sensory Signals with Deep State-Space Models.

Three factor delay learning rules for spiking neural networks Sch ¨oneet al.Scalable Event-by-Event Processing of Neuromorphic Sensory Signals with Deep State-Space Models

Reference 38

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source=pdf_text observed=2026-08-03T13:05:05.057811Z digest=sha256:9a886e19e6f35993e7fbd2ae11c7683c2337de3c1f9813776df6532451204a5c

Observation 12fb3861-878a-4c54-b5d8-60b6a0a1e4ca · outbound

This paper cites S7: Selective and Simplified State Space Layers for Sequence Modeling.

Three factor delay learning rules for spiking neural networks S7: Selective and Simplified State Space Layers for Sequence Modeling

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-03T13:05:05.137445Z

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source=pdf_text observed=2026-08-03T13:05:05.137445Z digest=sha256:4d6b5f38c24bc060fb1f2ccccfaacaf17da9cb541dece4835c724364082f1713

Observation f6b6eb02-7eb1-43ff-b7da-59878a589f92 · outbound

This paper cites Frenkel and G.

Three factor delay learning rules for spiking neural networks Frenkel and G

Reference 40

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no resolver link, observed 2026-08-03T13:05:05.181398Z

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source=pdf_text observed=2026-08-03T13:05:05.181398Z digest=sha256:7b4fe077bc0a60c7df42ac90999e5119c3a9952c9b3e34db9c0c23095658fa97

Observation 126e2a80-e9ae-4ab8-8d55-1332d83d1b2d · outbound

This paper cites Davieset al.Loihi: A Neuromorphic Manycore Processor with On- Chip Learning.IEEE Micro, 38(1):82–99, January 2018.

Three factor delay learning rules for spiking neural networks Davieset al.Loihi: A Neuromorphic Manycore Processor with On- Chip Learning.IEEE Micro, 38(1):82–99, January 2018

Reference 41

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no resolver link, observed 2026-08-03T13:05:05.310263Z

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source=pdf_text observed=2026-08-03T13:05:05.310263Z digest=sha256:489d967fa729487190e90993aef8d474fba9360be17dcf3dbbe178cb57cafffb

Pith citing papers

No inbound Pith citation observations are available.