Paper Citation Record · LEDGER

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons

As of 8 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2506.03392.

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

pith.paper-citation-record.v1
2506.03392 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:12:16.451892Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:53:56.536752Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:45:51.935399Z

Reference resolution

60 of 60 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd2b81f3-7cd9-4af5-b7e7-4ba0df33643e · outbound

This paper cites Target-driven visual navigation in indoor scenes using deep reinforcement learning.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Target-driven visual navigation in indoor scenes using deep reinforcement learning

Reference 1

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Source-reported events for the cited work

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Observation 859fbd22-57c2-4360-9418-d22a0eab38f5 · outbound

This paper cites Automated deep reinforcement learning environment for hardware of a modular legged robot.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Automated deep reinforcement learning environment for hardware of a modular legged robot

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.728633Z

Source-reported events for the cited work

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Observation 41c61817-77ae-4218-95d0-aeb8d252888e · outbound

This paper cites A sim-to-real pipeline for deep reinforcement learning for autonomous robot navigation in cluttered rough terrain.IEEE Robotics and Automation Letters, 6(4):6569–6576, 2021.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons A sim-to-real pipeline for deep reinforcement learning for autonomous robot navigation in cluttered rough terrain.IEEE Robotics and Automation Letters, 6(4):6569–6576, 2021

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.713499Z

Source-reported events for the cited work

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Observation ae4e0289-331e-485f-bfb6-9dc6f38cbc6a · outbound

This paper cites Tactical decision-making for autonomous driving using dueling double deep q network with double attention.IEEE Access, 9:151983–151992, 2021.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Tactical decision-making for autonomous driving using dueling double deep q network with double attention.IEEE Access, 9:151983–151992, 2021

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.697548Z

Source-reported events for the cited work

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Observation 77e517d8-ed70-427a-970a-5fb133fb0ee5 · outbound

This paper cites High-performance temporal reversible spiking neural networks with o(l) training memory and o(1) inference cost.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons High-performance temporal reversible spiking neural networks with o(l) training memory and o(1) inference cost

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.678760Z

Source-reported events for the cited work

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Observation 809e2351-abb1-42a2-b18b-052bf69bcb1d · outbound

This paper cites Spiking neural networks.J.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Spiking neural networks.J

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.662074Z

Source-reported events for the cited work

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Observation 949fc223-fc93-481f-bf5a-9248f10f7f4c · outbound

This paper cites an unresolved cited work.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:12:17.636369Z

Source-reported events for the cited work

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Observation 30e621bb-32d9-4c80-b23f-c447ffe39a82 · outbound

This paper cites Spiking deep convolutional neural networks for energy-efficient object recognition.Int’l J.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Spiking deep convolutional neural networks for energy-efficient object recognition.Int’l J

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.613688Z

Source-reported events for the cited work

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Observation 60c0c234-58e3-4acb-bca6-a9ecf8aacd4c · outbound

This paper cites Spatio-temporal backpropagation for training high-performance spiking neural networks.Frontiers in neuroscience, 12:323875, 2018.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Spatio-temporal backpropagation for training high-performance spiking neural networks.Frontiers in neuroscience, 12:323875, 2018

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.596420Z

Source-reported events for the cited work

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Observation fe429a73-a08c-4122-a26a-ff931119a608 · outbound

This paper cites Enabling spike-based backpropagation for training deep neural network architec- tures.Frontiers in neuroscience, 14:497482, 2020.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Enabling spike-based backpropagation for training deep neural network architec- tures.Frontiers in neuroscience, 14:497482, 2020

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.577147Z

Source-reported events for the cited work

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Observation 74239b99-6f52-4f22-b85f-ca7dc6a0e28d · outbound

This paper cites Spiking convolutional neural networks for text classification.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Spiking convolutional neural networks for text classification

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.555008Z

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Observation 18b3ef4e-2d74-408f-a7f7-cb5bb5cce23b · outbound

This paper cites an unresolved cited work.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Unresolved cited work

Reference 12

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Observation dbeecbff-0d31-404e-b146-34a21cf357a8 · outbound

This paper cites Generalized leaky integrate-and-fire models classify multiple neuron types.Nature communications, 9(1):709, 2018.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Generalized leaky integrate-and-fire models classify multiple neuron types.Nature communications, 9(1):709, 2018

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.519553Z

Source-reported events for the cited work

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Observation c7750c47-0c47-4b76-8cfd-3ad854ff32b2 · outbound

This paper cites Opportunities for neuromorphic computing algorithms and applications.Nature Computational Science, 2(1):10–19, 2022.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Opportunities for neuromorphic computing algorithms and applications.Nature Computational Science, 2(1):10–19, 2022

Reference 14

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Observation c33b3150-7623-45e4-9857-ef5b830ad9fe · outbound

This paper cites Moradi, N.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Moradi, N

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.488079Z

Source-reported events for the cited work

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Observation 4e720090-de0f-4140-8457-3891b08c888a · outbound

This paper cites Loihi: A neuromorphic manycore processor with on-chip learning.Ieee Micro, 38(1):82–99, 2018.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Loihi: A neuromorphic manycore processor with on-chip learning.Ieee Micro, 38(1):82–99, 2018

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.471719Z

Source-reported events for the cited work

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Observation 4fcdb657-c46f-434b-bd8f-0cc5897b33f7 · outbound

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Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:12:17.442156Z

Source-reported events for the cited work

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Observation 8a0e3ed2-6609-4aca-a680-c932ce973860 · outbound

This paper cites Debole, Brian Taba, Arnon Amir, et al.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Debole, Brian Taba, Arnon Amir, et al

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.426092Z

Source-reported events for the cited work

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Observation 03eec9fa-1343-4232-9634-2287b8695785 · outbound

This paper cites An efficient FPGA-based overlay inference archi- tecture for fully connected DNNs.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons An efficient FPGA-based overlay inference archi- tecture for fully connected DNNs

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.399499Z

Source-reported events for the cited work

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Observation 5a435aad-5a7b-4548-bf0a-7efa74dd8932 · outbound

This paper cites Gyro: A digital spiking neural network architecture for multi-sensory data analytics.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Gyro: A digital spiking neural network architecture for multi-sensory data analytics

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.381206Z

Source-reported events for the cited work

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Observation 8974413e-02ca-4008-9d09-187f07788120 · outbound

This paper cites A fast and energy-efficient SNN processor with adaptive clock/event-driven computation scheme and online learning.IEEE Trans.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons A fast and energy-efficient SNN processor with adaptive clock/event-driven computation scheme and online learning.IEEE Trans

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.364271Z

Source-reported events for the cited work

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Observation 4cd6bb93-ab02-4444-aea7-82b7e0e38de4 · outbound

This paper cites A low power and low latency FPGA- based spiking neural network accelerator.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons A low power and low latency FPGA- based spiking neural network accelerator

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.346648Z

Source-reported events for the cited work

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Observation 142a8854-05b8-4678-a8af-1e2ec0b28de5 · outbound

This paper cites Spiker: An FPGA-optimized hardware accelerator for spiking neural networks.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Spiker: An FPGA-optimized hardware accelerator for spiking neural networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.328447Z

Source-reported events for the cited work

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Observation 841f509c-6a6b-4df5-990f-a5ff28d2e3f8 · outbound

This paper cites A fully-configurable open-source software-defined digital quantized spiking neural core architecture.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons A fully-configurable open-source software-defined digital quantized spiking neural core architecture

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.308834Z

Source-reported events for the cited work

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Observation 7c6bf5a0-3170-4dfb-b1c6-af15da41d1d5 · outbound

This paper cites Brainqn: Enhancing the robustness of deep reinforcement learning with spiking neural networks.Advanced Intelligent Systems, 6(9):2400075, 2024.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Brainqn: Enhancing the robustness of deep reinforcement learning with spiking neural networks.Advanced Intelligent Systems, 6(9):2400075, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.285851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 50b6d7ba-ab3b-4069-96c6-98c2d8785a78 · outbound

This paper cites Deep rein- forcement learning with population-coded spiking neural network for continuous control.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Deep rein- forcement learning with population-coded spiking neural network for continuous control

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.266166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 12f3cc5c-16fe-4f7e-be70-2158e96dd7cd · outbound

This paper cites Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:15.464585Z

Source-reported events for the cited work

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Observation 31301e75-75d1-44ef-9b54-1994166d88cc · outbound

This paper cites Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to atari breakout game.Neural Networks, 120:108–115, 2019.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to atari breakout game.Neural Networks, 120:108–115, 2019

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.245845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 317707b3-2bca-492e-bde7-8d418a9b3642 · outbound

This paper cites Human-level control through directly trained deep spiking q-networks.IEEE transactions on cybernetics, 53(11): 7187–7198, 2022.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Human-level control through directly trained deep spiking q-networks.IEEE transactions on cybernetics, 53(11): 7187–7198, 2022

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.220126Z

Source-reported events for the cited work

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Observation ecd0921c-1520-44c1-8907-108dca590478 · outbound

This paper cites Reinforcement co-learning of deep and spiking neural networks for energy-efficient mapless navigation with neuromorphic hardware.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Reinforcement co-learning of deep and spiking neural networks for energy-efficient mapless navigation with neuromorphic hardware

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.194594Z

Source-reported events for the cited work

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Observation 006a79b4-dd53-4403-be05-55e59bbe2981 · outbound

This paper cites Ternary spike: Learning ternary spikes for spiking neural networks.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Ternary spike: Learning ternary spikes for spiking neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.168693Z

Source-reported events for the cited work

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Observation ab43b157-5669-434c-a638-6a8d96b5a1cf · outbound

This paper cites SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms

Reference 32

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Observation 306f0452-e9c7-408a-8fc9-f7be43676a9a · outbound

This paper cites an unresolved cited work.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Unresolved cited work

Reference 33

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Observation aeba2c2f-04a4-4fc9-a7e4-6a032c1a8f65 · outbound

This paper cites Springer, 2023.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Springer, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.122256Z

Source-reported events for the cited work

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Observation 78a0ff09-a1bf-4b00-9dd8-0cb1917d551d · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Playing Atari with Deep Reinforcement Learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:16.268717Z

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Observation d062c52e-3efd-4bd3-9fc5-a6079bd24c66 · outbound

This paper cites Human-level control through deep reinforcement learning.nature, 518(7540):529–533, 2015.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Human-level control through deep reinforcement learning.nature, 518(7540):529–533, 2015

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:16.335902Z

Source-reported events for the cited work

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Observation 5d1bc8dd-4acb-4ea0-997e-e5c447d8adb4 · outbound

This paper cites Deep reinforcement learning with double q-learning.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Deep reinforcement learning with double q-learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:16.340598Z

Source-reported events for the cited work

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Observation 1bc3ef6f-68af-40a4-8d32-d3ea7d6c2f0f · outbound

This paper cites Dueling network architectures for deep reinforcement learning.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Dueling network architectures for deep reinforcement learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:16.345840Z

Source-reported events for the cited work

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Observation d93e2648-ad42-4963-9b93-a29aaed33cc5 · outbound

This paper cites an unresolved cited work.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:12:17.044602Z

Source-reported events for the cited work

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Observation 4e28a34e-f069-4125-ba47-0942ce4b7372 · outbound

This paper cites Strategy and benchmark for converting deep q-networks to event-driven spiking neural networks.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Strategy and benchmark for converting deep q-networks to event-driven spiking neural networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:17.015823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 469a19fa-a8a3-40bc-97f4-679f302abf32 · outbound

This paper cites Solving the spike feature information vanishing problem in spiking deep q network with potential based normalization.Frontiers in Neuroscience, 16: 953368, 2022.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Solving the spike feature information vanishing problem in spiking deep q network with potential based normalization.Frontiers in Neuroscience, 16: 953368, 2022

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.994874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cce6a9a4-4045-4313-83f4-54913d87ed93 · outbound

This paper cites Toward robust and scalable deep spiking reinforcement learning.Frontiers in Neurorobotics, 16:1075647, 2023.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Toward robust and scalable deep spiking reinforcement learning.Frontiers in Neurorobotics, 16:1075647, 2023

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.976092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cbdc079c-5736-445d-9b5c-56fbde3de4e1 · outbound

This paper cites SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:16.369455Z

Source-reported events for the cited work

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Observation 5e2f862b-53ea-4133-b538-335def395a46 · outbound

This paper cites Path planning via an improved dqn-based learning policy.IEEE Access, 7:67319–67330, 2019.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Path planning via an improved dqn-based learning policy.IEEE Access, 7:67319–67330, 2019

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.951971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 684edff2-a8f8-4b6e-ab8d-cfb1858dd2c5 · outbound

This paper cites Deep q-learning from demonstrations.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Deep q-learning from demonstrations

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:16.381045Z

Source-reported events for the cited work

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Observation 730b0687-bec6-48fc-910e-79db7d2acba2 · outbound

This paper cites Event- triggered deep reinforcement learning using parallel control: A case study in autonomous driving.IEEE Transactions on Intelligent Vehicles, 8(4):2821–2831, 2023.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Event- triggered deep reinforcement learning using parallel control: A case study in autonomous driving.IEEE Transactions on Intelligent Vehicles, 8(4):2821–2831, 2023

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.921314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 66d4b8b9-7a26-40b8-ab79-72fd874a02ac · outbound

This paper cites Distributed Prioritized Experience Replay.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Distributed Prioritized Experience Replay

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:16.390607Z

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Observation 52023c7b-5ec7-4daf-9ce3-f120fd0cd2eb · outbound

This paper cites The frequency of nerve action potentials generated by applied currents.Proceedings of the Royal Society of London.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons The frequency of nerve action potentials generated by applied currents.Proceedings of the Royal Society of London

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.905178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8833aa09-a01e-4df3-bbcd-95855665c4cc · outbound

This paper cites Towards efficient spiking transformer: a token sparsification framework for training and inference acceleration.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Towards efficient spiking transformer: a token sparsification framework for training and inference acceleration

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.880272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bcede633-270c-4a6e-b937-c1806773e46a · outbound

This paper cites Ndot: Neuronal dynamics-based online training for spiking neural networks.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Ndot: Neuronal dynamics-based online training for spiking neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.860050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 583824b1-dd4b-4f5a-a253-eba525d3a1fc · outbound

This paper cites CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:16.408598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ae94c43-fb53-44a9-841c-4536f5017e17 · outbound

This paper cites Adaptive smoothing gradient learning for spiking neural networks.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Adaptive smoothing gradient learning for spiking neural networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.837250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8ea19221-84a0-4f87-b98b-fe5a5479e078 · outbound

This paper cites Enhancing the robustness of spiking neural networks with stochastic gating mechanisms.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Enhancing the robustness of spiking neural networks with stochastic gating mechanisms

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.813819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8fc31e54-383e-4cf3-8040-ff6022790d5c · outbound

This paper cites Cambridge university press, 2002.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Cambridge university press, 2002

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.795733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 395e4bbe-26c6-4783-98e2-37d2e3fd7bb6 · outbound

This paper cites Direct training for spiking neural networks: Faster, larger, better.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Direct training for spiking neural networks: Faster, larger, better

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.770140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 358c6366-2166-45c3-ab23-a5062ef61a33 · outbound

This paper cites Going deeper with directly-trained larger spiking neural networks.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Going deeper with directly-trained larger spiking neural networks

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:16.432757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57938017-f804-4660-a10f-6ec84c4781c3 · outbound

This paper cites Training spiking neural networks using lessons from deep learning.Proceedings of the IEEE, 111(9):1016–1054, 2023.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Training spiking neural networks using lessons from deep learning.Proceedings of the IEEE, 111(9):1016–1054, 2023

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.729516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5b3144ac-ba8a-4024-ae5b-42ba955a99d0 · outbound

This paper cites Why do we have so many excitatory neurons?bioRxiv, pages 2024–09, 2024.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Why do we have so many excitatory neurons?bioRxiv, pages 2024–09, 2024

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.706811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6d0c6189-fa2b-4e57-85d2-f8c3dd50093e · outbound

This paper cites Generation of diverse cortical inhibitory interneurons.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Generation of diverse cortical inhibitory interneurons

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.680678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 127741d3-ff14-421e-afbc-86fc0a60dfe1 · outbound

This paper cites Openai gym, 2016.

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons Openai gym, 2016

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:16.660005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation e06f3005-1186-43de-b664-6b4fe3b83e8c · inbound

Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving cites this paper.

Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:45:51.942367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.