Paper Citation Record · LEDGER

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks

As of 19 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2505.04034.

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

pith.paper-citation-record.v1
2505.04034 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:43:47.574774Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact4
verified fuzzy27
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a646468b-39a9-4949-bdf1-472a0568e7f7 · outbound

This paper cites Towards spike-based machine intelligence with neuromorphic computing,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Towards spike-based machine intelligence with neuromorphic computing,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.199333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78d53310-1034-4d3d-a770-5707433dc42a · outbound

This paper cites Spiking neural networks and their applications: A review,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Spiking neural networks and their applications: A review,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:48.114536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a481e4de-cdee-4149-9e3f-4135c6a4e087 · outbound

This paper cites Gerstner and W.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Gerstner and W

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:48.102153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 671dac7a-78b8-4591-b020-4fd17541dc92 · outbound

This paper cites Which model to use for cortical spiking neurons?.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Which model to use for cortical spiking neurons?

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:48.091540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08810531-90e9-4386-8000-cbc8f370c3e2 · outbound

This paper cites Simple model of spiking neurons,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Simple model of spiking neurons,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:48.079189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba6daaa8-e89d-4e87-a56c-96f948aff0b7 · outbound

This paper cites Rapid neural coding in the retina with relative spike latencies,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Rapid neural coding in the retina with relative spike latencies,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.316675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01eaa63f-c3c7-45a9-ad6e-0bae35b0ac75 · outbound

This paper cites Mnist handwritten digit database,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Mnist handwritten digit database,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.320059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e762f18-f6db-474d-a1fa-2c70f6eec0a8 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.323568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60875ca9-aaea-4cc7-9609-34c6d311afab · outbound

This paper cites Cifar-10 (canadian institute for advanced research),.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Cifar-10 (canadian institute for advanced research),

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:48.056099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ac38d37c-3001-4822-853b-f2e43ff19d0d · outbound

This paper cites an unresolved cited work.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.331198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 089d165d-482b-4c63-8c46-99133241ec2a · outbound

This paper cites Breast Cancer,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Breast Cancer,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.334689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5925337-b83d-404c-a355-dd6e5476218d · outbound

This paper cites Membership inference attacks against machine learning models,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Membership inference attacks against machine learning models,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.338318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b20a8cc-6897-4552-9031-5868002e7069 · outbound

This paper cites Spiking neural networks,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Spiking neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:48.038282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 867b2f7d-9e4f-4dad-b1dd-05ae165e4417 · outbound

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

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Networks of spiking neurons: the third generation of neural network models,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.345468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 523aeb68-e33a-41c1-8ed8-5e752e59c258 · outbound

This paper cites Adaptive exponential integrate-and-fire model as an effective description of neuronal activity,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Adaptive exponential integrate-and-fire model as an effective description of neuronal activity,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:48.021116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f4a115be-1fab-4b88-9c65-99c13641d428 · outbound

This paper cites Spiking Deep Networks with LIF Neurons.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Spiking Deep Networks with LIF Neurons

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.364109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4d4192f-befe-474e-b33b-77b3105045fa · outbound

This paper cites A theoretical analysis of neuronal variability,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks A theoretical analysis of neuronal variability,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.419313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a2777b6-e83c-4f0d-bcee-69274b0ec8f7 · outbound

This paper cites A quantitative description of membrane current and its application to conduction and excitation in nerve,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks A quantitative description of membrane current and its application to conduction and excitation in nerve,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:48.004564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 789bb400-022c-4dc2-ba2b-2742d3bd024f · outbound

This paper cites Intrinsic firing patterns of diverse neocortical neurons,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Intrinsic firing patterns of diverse neocortical neurons,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.992814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2dd06925-2ac3-4d64-8d05-17053ff882e5 · outbound

This paper cites Bursts as a unit of neural information: making unreliable synapses reliable,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Bursts as a unit of neural information: making unreliable synapses reliable,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.981326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3fe47d1e-41b8-4f93-b23b-a90ab681d99b · outbound

This paper cites Large-scale model of mammalian thalamocortical systems,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Large-scale model of mammalian thalamocortical systems,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.971847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9555864a-4d74-47ee-8f47-4c5c475d027e · outbound

This paper cites Dynamical systems in neuroscience,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Dynamical systems in neuroscience,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.962111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13bd184e-8d4d-4de4-9e5d-463482975481 · outbound

This paper cites Adversarial robustness of spiking neural networks,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Adversarial robustness of spiking neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.950462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1f43f802-f579-4efe-b889-367509b6f9e8 · outbound

This paper cites Inherent adversarial robustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Inherent adversarial robustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.937009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d2dda087-2481-4f37-8aee-9f69295c1a14 · outbound

This paper cites BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:43:47.747296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 945ad10f-cb75-4487-9655-f8580aff52c2 · outbound

This paper cites Do spikes protect privacy? investigating black-box model inversion attacks in spiking neural networks,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Do spikes protect privacy? investigating black-box model inversion attacks in spiking neural networks,

Reference 26

Resolution: verified exact; raw_fallback, observed 2026-08-15T23:43:47.731069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7d8ac0ec-795f-41dd-bde2-0ad5930e87a1 · outbound

This paper cites Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:43:47.663656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 95f8185c-04e3-41c4-bc5b-e2bb91071c57 · outbound

This paper cites On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:43:47.647256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f5f3e50d-ed80-4d4f-a983-36add0ceb973 · outbound

This paper cites Efficient knowledge transfer strategy for snns from static to event domain,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Efficient knowledge transfer strategy for snns from static to event domain,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.924662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d5a3f181-69e0-4869-8573-1329fc3258fb · outbound

This paper cites Bridging resnet and vision transformer in snns,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Bridging resnet and vision transformer in snns,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.913380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 98a9c174-11a5-4f45-8552-92bba2f03745 · outbound

This paper cites Deep learning with spiking neurons: Oppor- tunities and challenges,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Deep learning with spiking neurons: Oppor- tunities and challenges,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.901332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 28f8b2ad-4913-4864-870c-b73482129d62 · outbound

This paper cites Deep learning in spiking neural networks,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Deep learning in spiking neural networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.891308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ffb1ad1a-c9b1-4f9f-b3a2-fa66a67470d7 · outbound

This paper cites Membership inference attacks against machine learning models,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Membership inference attacks against machine learning models,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.534539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a8b95c6-1d30-419a-9fe4-59b65e8582e7 · outbound

This paper cites Privacy risk in machine learning: Analyzing the connection to overfitting,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Privacy risk in machine learning: Analyzing the connection to overfitting,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.874324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8b45afb0-8a48-415c-be7c-c14dea13b289 · outbound

This paper cites Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.862934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 38bd3b4d-7296-4e78-8632-11f09683e28d · outbound

This paper cites ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.543737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 600674f8-79d5-4c27-a837-174ad5472768 · outbound

This paper cites Towards Demystifying Membership Inference Attacks.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Towards Demystifying Membership Inference Attacks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.547507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21481540-5cf6-4009-b290-50c1b33d6acc · outbound

This paper cites Making large-scale svm learning practical,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Making large-scale svm learning practical,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.850253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a606bf6d-fdb2-4bd6-afd3-4b0f0f299d4a · outbound

This paper cites A survey on transfer learning,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks A survey on transfer learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.839471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2d0684f2-172a-44b5-acfe-723464597e4f · outbound

This paper cites How transferable are features in deep neural networks?.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks How transferable are features in deep neural networks?

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.827197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8cfcd3ed-e6f7-4ea6-8f79-f52b8a342497 · outbound

This paper cites Snntorch: Tutorial 1,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Snntorch: Tutorial 1,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.813699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 470e34de-f13c-41a4-8445-00293943409e · outbound

This paper cites Nvidia a100 tensor core gpu architecture,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Nvidia a100 tensor core gpu architecture,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.802652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c0f51ea6-3378-489e-8beb-b0d2965269cc · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Adam: A Method for Stochastic Optimization

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:47.570959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad808b37-8a6c-4653-9bb7-aa06b301ed20 · outbound

This paper cites A generalized geometric distribution and some of its properties,.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks A generalized geometric distribution and some of its properties,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.778369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fed5ae24-5ed4-47d2-8c83-78c88f665be1 · outbound

This paper cites Available: https://resources.nvidia.com/en-us-tensor-core/ nvidia-ampere-architecture-whitepaper.

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks Available: https://resources.nvidia.com/en-us-tensor-core/ nvidia-ampere-architecture-whitepaper

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:43:47.790370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.