Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:45:00.153554Z
Paper Citation Record · LEDGER
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods
As of 22 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2507.02960.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
45 of 45 outbound references displayed
- verified exact4
- verified fuzzy36
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b2ca7e3c-9a98-4743-b187-dbd85214def3 · outbound
This paper cites A spiking central pattern generator for the control of a simulated lamprey robot running on spin- naker and loihi neuromorphic boards.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods A spiking central pattern generator for the control of a simulated lamprey robot running on spin- naker and loihi neuromorphic boards
Reference 1
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7ce47349-8e1a-4d7b-88d2-2334f4f370df · outbound
This paper cites Refractoriness enhances temporal cod- ing by auditory nerve fibers.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Refractoriness enhances temporal cod- ing by auditory nerve fibers
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:06.736649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 99d49743-9d9a-4a31-b2de-53670488f70c · outbound
This paper cites A solution to the learning dilemma for recurrent net- works of spiking neurons.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods A solution to the learning dilemma for recurrent net- works of spiking neurons
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:06.557495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation df87f2b1-14b8-4c65-b6c3-2b598ff2852c · outbound
This paper cites Refractoriness and neu- ral precision.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Refractoriness and neu- ral precision
Reference 4
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2dc31714-434e-4f24-98fa-d1d0b05c92ac · outbound
This paper cites an unresolved cited work.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Unresolved cited work
Reference 5
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c0d4df86-b754-4a5c-8a9b-31ece4f67e41 · outbound
This paper cites Rhythms of the Brain.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Rhythms of the Brain
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:06.009821Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bf650012-4a9a-4c2a-ba05-3244e0cb6b3a · outbound
This paper cites Biophysics-inspired spike rate adaptation for computa- tionally efficient phenomenological nerve modeling.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Biophysics-inspired spike rate adaptation for computa- tionally efficient phenomenological nerve modeling
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:05.830996Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 262e69cb-5be8-41f0-8941-69fbd33bf9fa · outbound
This paper cites Surrogate module learning: Reduce the gradient error accumulation in training spiking neural networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Surrogate module learning: Reduce the gradient error accumulation in training spiking neural networks
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:05.596713Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 23e5f9ae-6114-493b-b51c-4330025481bc · outbound
This paper cites Parallel spiking neurons with high efficiency and abil- ity to learn long-term dependencies.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Parallel spiking neurons with high efficiency and abil- ity to learn long-term dependencies
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:05.417268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3de941ac-642b-4013-863a-28584a6c80b2 · outbound
This paper cites HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:45:00.739632Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation fa77b7ed-8c0d-442c-bd18-10a822dbe4b7 · outbound
This paper cites Spatiotemporal pattern formation in two-dimensional neural circuits: roles of refractoriness and noise.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Spatiotemporal pattern formation in two-dimensional neural circuits: roles of refractoriness and noise
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:05.252568Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 65ccd953-f3b6-4df2-ad74-dd37c03a7108 · outbound
This paper cites Direct learning-based deep spiking neural networks: a review.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Direct learning-based deep spiking neural networks: a review
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:05.107797Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 54c70c02-8497-4616-928f-4e41d3dc60e6 · outbound
This paper cites Mem- brane potential batch normalization for spiking neural net- works.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Mem- brane potential batch normalization for spiking neural net- works
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:04.729133Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7d877848-bb68-491e-bd1e-ccc6a3b951dc · outbound
This paper cites Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:45:00.613919Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation de823fd1-161a-4ece-9a4e-e7c37bdb4620 · outbound
This paper cites A progressive training framework for spiking neural networks with learnable multi-hierarchical model.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods A progressive training framework for spiking neural networks with learnable multi-hierarchical model
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:04.387870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2776cb31-0f96-4d66-8d06-988dffee73ce · outbound
This paper cites Balanced Resonate-and-Fire Neurons.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Balanced Resonate-and-Fire Neurons
Reference 16
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 70fb06b5-2dcc-4eaa-a64a-13406cc3db2d · outbound
This paper cites Ad- vancing spiking neural networks toward deep residual learn- ing.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Ad- vancing spiking neural networks toward deep residual learn- ing
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:04.131778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4d8afc88-22ab-4e42-a585-aecaa6002497 · outbound
This paper cites CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
Reference 18
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 20304455-2067-4dfc-9dcf-ca853f533fa1 · outbound
This paper cites Rethinking the role of normalization and resid- ual blocks for spiking neural networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Rethinking the role of normalization and resid- ual blocks for spiking neural networks
Reference 19
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- raw_fallback, observed 2026-08-06T21:45:03.957115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 45bba4ce-8e96-41ae-8d83-a589e0ac7a56 · outbound
This paper cites How inhibition shapes cortical activity.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods How inhibition shapes cortical activity
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:03.851404Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 83df7516-1fb8-46ba-9989-bcd2d4558546 · outbound
This paper cites Dynamical systems in neuroscience.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Dynamical systems in neuroscience
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:03.733724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f1ba2d4d-034d-4da7-be9d-fa9bab33a52e · outbound
This paper cites Tab: Temporal accumulated batch normal- ization in spiking neural networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Tab: Temporal accumulated batch normal- ization in spiking neural networks
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:03.645136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b60934a0-5513-4fa7-93c0-908464cd57f2 · outbound
This paper cites Biophysics of computation: information pro- cessing in single neurons.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Biophysics of computation: information pro- cessing in single neurons
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:03.516621Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8fb932c7-0a06-4ed7-9f83-48b9eb0423c6 · outbound
This paper cites Bsnn: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Bsnn: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:03.402437Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 141bf69e-f867-4312-b3a4-4561c889cb72 · outbound
This paper cites Spiking mode-based neu- ral networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Spiking mode-based neu- ral networks
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:03.296891Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3414a29a-2735-41d2-abc7-4b8b0d0821f0 · outbound
This paper cites Information dynam- ics of in silico eeg brain waves: Insights into oscillations and functions.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Information dynam- ics of in silico eeg brain waves: Insights into oscillations and functions
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:03.164821Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 12df68c3-f73e-4999-a22f-707af7f4c77a · outbound
This paper cites Training high- performance low-latency spiking neural networks by dif- ferentiation on spike representation.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Training high- performance low-latency spiking neural networks by dif- ferentiation on spike representation
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:03.045992Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4eb212d3-93fb-4cba-95b6-04833c9466fe · outbound
This paper cites Towards memory-and time-efficient backpropagation for training spiking neural networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Towards memory-and time-efficient backpropagation for training spiking neural networks
Reference 28
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- raw_fallback, observed 2026-08-06T21:45:02.907047Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a0a3e7c3-d2e0-46b4-9b71-35b62690fa1c · outbound
This paper cites A model for single neuron ac- tivity with refractory effects and spike rate estimation tech- niques.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods A model for single neuron ac- tivity with refractory effects and spike rate estimation tech- niques
Reference 29
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- raw_fallback, observed 2026-08-06T21:45:02.796422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b65464b7-b1dc-4128-a560-ab35815f17d5 · outbound
This paper cites Direct training of snn using local zeroth order method.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Direct training of snn using local zeroth order method
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:02.657088Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 94a7b30c-1eb9-4f0c-8caa-c330d0912214 · outbound
This paper cites Mean-reverting neuronal model based on two alternating patterns.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Mean-reverting neuronal model based on two alternating patterns
Reference 31
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- raw_fallback, observed 2026-08-06T21:45:02.427272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 57d46ec4-14c4-4840-bdd2-bbca66a5b941 · outbound
This paper cites Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware
Reference 32
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5f9470a-f2a3-490a-8389-3e98dac597e2 · outbound
This paper cites Effi- cient spiking neural networks with sparse selective activation for continual learning.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Effi- cient spiking neural networks with sparse selective activation for continual learning
Reference 33
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- raw_fallback, observed 2026-08-06T21:45:02.249015Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5fc34211-1d66-4860-b464-e156713d1926 · outbound
This paper cites Modeling eluci- dates how refractory period can provide profound nonlinear gain control to graded potential neurons.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Modeling eluci- dates how refractory period can provide profound nonlinear gain control to graded potential neurons
Reference 34
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- raw_fallback, observed 2026-08-06T21:45:02.128669Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 843880be-633f-41ca-b876-49e8e1732e28 · outbound
This paper cites Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:01.941407Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6fb9b006-53b4-4291-bbc2-00fdcff3f330 · outbound
This paper cites Significant anisotropic neuronal refractory period plasticity.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Significant anisotropic neuronal refractory period plasticity
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:01.744343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 9a0e8e71-d26c-464e-b43c-44c14e620ce3 · outbound
This paper cites Ssf: Accelerating training of spiking neural networks with stabilized spiking flow.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Ssf: Accelerating training of spiking neural networks with stabilized spiking flow
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:01.601236Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 86d4223e-4574-40b8-8227-7682aab30111 · outbound
This paper cites Spatio-temporal backpropagation for training high- performance spiking neural networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Spatio-temporal backpropagation for training high- performance spiking neural networks
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:01.417192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 095b3e90-8688-4886-9188-006a5507d304 · outbound
This paper cites Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks
Reference 39
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:45:00.470819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f6d44e13-c04f-47f2-a3e4-53126b3aaa42 · outbound
This paper cites Glif: A unified gated leaky integrate-and-fire neuron for spiking neural networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Glif: A unified gated leaky integrate-and-fire neuron for spiking neural networks
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:01.232383Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bb41f6fb-3b71-4b3a-bff5-121099bc26ed · outbound
This paper cites A review of spiking neuromorphic hardware commu- nication systems.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods A review of spiking neuromorphic hardware commu- nication systems
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:01.087794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3def8488-19ff-4e3b-9718-7a7239bfb5f9 · outbound
This paper cites Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
Reference 42
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:45:00.348982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 76e84042-1c25-4713-9a01-4a7129a3581e · outbound
This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Cutmix: Regu- larization strategy to train strong classifiers with localizable features
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:44:59.968082Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f02908b-8e7a-4bd5-84b1-de3a0b249adc · outbound
This paper cites Self-evolutionary neuron model for fast-response spiking neural networks.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Self-evolutionary neuron model for fast-response spiking neural networks
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:00.971326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation cf7053ea-885b-415a-b828-1d8dd8f298f1 · outbound
This paper cites Retina-like visual image reconstruction via spiking neural model.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Retina-like visual image reconstruction via spiking neural model
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:45:00.859560Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
No inbound Pith citation observations are available.