Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods | Paper Citation Record

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

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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

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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

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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

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Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods Unresolved cited work

Reference 5

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:03.957115Z

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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

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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

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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

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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

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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

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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

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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

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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

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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:02.796422Z

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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

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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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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

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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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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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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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