Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback | Paper Citation Record

Observation 0099f612-c462-4b6d-a843-24db5574df2e · outbound

This paper cites Data centers on wheels: Emissions from computing onboard autonomous vehicles,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Data centers on wheels: Emissions from computing onboard autonomous vehicles,

Reference 1

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Observation e54d9f2f-87cb-4692-a7bc-e59c7799840a · outbound

This paper cites Low-power neuromorphic hardware for signal processing appli- cations: A review of architectural and system-level design approaches,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Low-power neuromorphic hardware for signal processing appli- cations: A review of architectural and system-level design approaches,

Reference 2

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Observation 78cb0f0b-a933-40c2-94bf-1213e0fd7f39 · outbound

This paper cites Advancing neuromorphic computing with Loihi: A survey of results and outlook,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Advancing neuromorphic computing with Loihi: A survey of results and outlook,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.451906Z

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Observation c1a4f4d5-82a2-4d0d-92b3-a7431360e100 · outbound

This paper cites Neurobench: A framework for benchmarking neuromorphic computing algorithms and systems,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Neurobench: A framework for benchmarking neuromorphic computing algorithms and systems,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.443684Z

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Observation a15fb8ef-bedb-4e28-8f6b-9b477e08c122 · outbound

This paper cites Gerstner and W.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Gerstner and W

Reference 5

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Observation 7f198d57-ea5a-42d3-b6df-8df098ef68ca · outbound

This paper cites Advancing spatio-temporal processing in spiking neural networks through adaptation,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Advancing spatio-temporal processing in spiking neural networks through adaptation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.431681Z

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Observation 2358d182-df31-428f-9023-0986020250cf · outbound

This paper cites A surrogate gradient spiking baseline for speech command recognition,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback A surrogate gradient spiking baseline for speech command recognition,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.424059Z

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Observation 7ca3fde9-1bf0-41b9-a338-09959065c74e · outbound

This paper cites Incorporating learnable membrane time constant to enhance learning of spiking neural networks,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Incorporating learnable membrane time constant to enhance learning of spiking neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.416930Z

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Observation 97c09084-5816-49aa-8438-c8dabc5e02ba · outbound

This paper cites Efficiently modeling long sequences with structured state spaces,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Efficiently modeling long sequences with structured state spaces,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.409398Z

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Observation 3285877a-0525-48a4-abcf-b657422c8b78 · outbound

This paper cites On the parameterization and initialization of diagonal state space models,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback On the parameterization and initialization of diagonal state space models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.401741Z

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Observation f43fffa8-cdf1-4ccb-98bf-aa28f0c7fcb5 · outbound

This paper cites Simplified state space layers for sequence modeling,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Simplified state space layers for sequence modeling,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.394600Z

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Observation cd2c73cc-632a-41ad-9ee2-04943de176b9 · outbound

This paper cites Multilingual spoken words corpus,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Multilingual spoken words corpus,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.387468Z

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Observation 745157f2-2588-498c-8910-1038c37a973a · outbound

This paper cites A low power, fully event-based gesture recognition system,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback A low power, fully event-based gesture recognition system,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.380481Z

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Observation a42d0453-36fe-4d48-947f-a286f5c93a57 · outbound

This paper cites A Simple Way to Initialize Recurrent Networks of Rectified Linear Units.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T22:52:30.611977Z

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Observation c0b43517-f1cd-45a0-807d-9e88048343ec · outbound

This paper cites State-space model inspired multiple-input multiple-output spiking neurons,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback State-space model inspired multiple-input multiple-output spiking neurons,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.373528Z

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Observation 7418e9ed-2b4e-4962-be18-f4965f0f7459 · outbound

This paper cites Perfect Recovery and Sensitivity Analysis of Time Encoded Bandlimited Signals,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Perfect Recovery and Sensitivity Analysis of Time Encoded Bandlimited Signals,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.365742Z

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Observation b09453cf-9700-4173-99bd-7c7d4b133f37 · outbound

This paper cites FRI-TEM: Time encoding sampling of finite-rate-of-innovation signals,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback FRI-TEM: Time encoding sampling of finite-rate-of-innovation signals,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.358400Z

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Observation 3f193898-46ba-42f7-8d33-07f6fd512eed · outbound

This paper cites Bandlimited signal reconstruction from leaky integrate-and-fire encoding using POCS,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Bandlimited signal reconstruction from leaky integrate-and-fire encoding using POCS,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.351122Z

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Observation fb8665d2-9a52-4df8-a8c2-6643c2d871b5 · outbound

This paper cites Asynchrony increases efficiency: Time encoding of videos and low-rank signals,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Asynchrony increases efficiency: Time encoding of videos and low-rank signals,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.343645Z

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Observation d94f51a5-a7a7-487f-a600-33f295cdc02e · outbound

This paper cites Scalable event-by-event processing of neuromorphic sensory signals with deep state-space models,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Scalable event-by-event processing of neuromorphic sensory signals with deep state-space models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.336300Z

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Observation d440f56f-fffa-44b1-8705-40433645844f · outbound

This paper cites S7: Selective and simplified state space layers for sequence modeling,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback S7: Selective and simplified state space layers for sequence modeling,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.328973Z

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Observation 61ef434f-a4e4-469a-981d-009249148acf · outbound

This paper cites Quamba: A post-training quantization recipe for selective state space models,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Quamba: A post-training quantization recipe for selective state space models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.321759Z

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Observation b6e805e1-2546-4acd-923f-87a67976f96c · outbound

This paper cites Q-s5: Towards quantized state space models,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Q-s5: Towards quantized state space models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.314712Z

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Observation 013a0e1c-08ce-4a08-8c4a-f594b48d5caa · outbound

This paper cites A diagonal structured state space model on Loihi 2 for efficient streaming sequence processing,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback A diagonal structured state space model on Loihi 2 for efficient streaming sequence processing,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.306551Z

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Observation 1f02184c-32ed-4feb-8218-554eb9358f04 · outbound

This paper cites Rethinking spiking neural networks as state space models,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Rethinking spiking neural networks as state space models,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.298511Z

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Observation 677f2528-11df-4baf-ab84-c5635bcf8c7b · outbound

This paper cites Learning long sequences in spiking neural networks,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Learning long sequences in spiking neural networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.290657Z

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Observation caabb465-d78b-4569-b6aa-02c97a4ebc84 · outbound

This paper cites Spikingssms: Learning long sequences with sparse and parallel spiking state space models,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Spikingssms: Learning long sequences with sparse and parallel spiking state space models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.282381Z

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Observation b968f85b-dec7-4c4c-ae00-ce6c4806990d · outbound

This paper cites SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T22:52:31.769831Z

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Observation 15ce2089-f935-4cba-95d7-7f5cf9d961ed · outbound

This paper cites Zero-shot temporal resolution domain adaptation for spiking neural networks,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Zero-shot temporal resolution domain adaptation for spiking neural networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.273547Z

Source-reported events for the cited work

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Observation 246e4348-499b-4bd1-85b7-45c42dda8422 · outbound

This paper cites Loihi: A neuromorphic manycore processor with on-chip learning,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Loihi: A neuromorphic manycore processor with on-chip learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.264431Z

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 117c3fd6-2dda-4a69-9aed-9f5a909c3bde · outbound

This paper cites Available: https://arxiv.org/abs/2411.04760.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Available: https://arxiv.org/abs/2411.04760

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T22:52:31.965937Z

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Observation a8dcb262-f0b0-4365-89eb-256dc9f71981 · outbound

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

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback A quantitative description of membrane current and its application to conduction and excitation in nerve,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.250661Z

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Observation 38083734-36d6-470d-881f-fde3ef96199f · outbound

This paper cites Simple model of spiking neurons,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Simple model of spiking neurons,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T22:52:32.115392Z

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Observation 7a24ccf7-616b-4e5a-941c-e09ec2a0e960 · outbound

This paper cites Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.236569Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3309c2c4-28a9-431a-901a-be88a02db2cf · outbound

This paper cites Gajic, Linear Dynamic Systems and Signals.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Gajic, Linear Dynamic Systems and Signals

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.243592Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation abc41711-916f-432b-9248-212aebdf7a32 · outbound

This paper cites Self-adapting spiking neural p systems with refractory period and propagation delay,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Self-adapting spiking neural p systems with refractory period and propagation delay,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.222289Z

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 90297644-6d39-4b38-b866-7d4b15311b6b · outbound

This paper cites Superspike: Supervised learning in multilayer spiking neural networks,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Superspike: Supervised learning in multilayer spiking neural networks,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.229517Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 69bb2c6f-b49c-468b-b79f-9360c2bbdebd · outbound

This paper cites Diagonal state spaces are as effective as structured state spaces,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Diagonal state spaces are as effective as structured state spaces,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.207462Z

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 7db26772-73bd-4fbf-acbc-bae95f1ca637 · outbound

This paper cites Leaky integrate- and-fire neuron with a refractory period mechanism for invariant spikes,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Leaky integrate- and-fire neuron with a refractory period mechanism for invariant spikes,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.215293Z

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 7e57f472-fb76-4930-baf7-35975a2feabd · outbound

This paper cites Spike-driven transformer,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Spike-driven transformer,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.193294Z

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 ce7ffc86-4665-4adc-99d5-aac27e74d6b8 · outbound

This paper cites Batch normalization: accelerating deep network training by reducing internal covariate shift,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Batch normalization: accelerating deep network training by reducing internal covariate shift,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.200455Z

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 a36646cd-a13a-4f41-9354-97e46e0a6bb8 · outbound

This paper cites Very deep convolutional neural networks for raw waveforms,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Very deep convolutional neural networks for raw waveforms,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.178389Z

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 4848a668-a37a-477f-baa7-002c40b5fbe1 · outbound

This paper cites The mnist database of handwritten digits,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback The mnist database of handwritten digits,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.185715Z

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 367cc089-c72a-40a6-8ab0-af67fec21cd8 · outbound

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

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Going deeper with directly-trained larger spiking neural networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.163581Z

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 98d6fe66-2f3d-43ce-a56a-2406291ee97e · outbound

This paper cites Synaptic plasticity dynamics for deep continuous local learning (DECOLLE),.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Synaptic plasticity dynamics for deep continuous local learning (DECOLLE),

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.171107Z

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 8308906e-6d3b-4501-8a2b-347d899831f7 · outbound

This paper cites An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.156113Z

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 2f0e9e8b-5d5c-4d4e-9205-0afc66742483 · outbound

This paper cites The Role of Temporal Hierarchy in Spiking Neural Networks.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback The Role of Temporal Hierarchy in Spiking Neural Networks

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:52:33.987026Z

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 15af11b1-680f-4631-874d-06ec6741d572 · outbound

This paper cites Speech2spikes: Efficient audio encoding pipeline for real-time neuro- morphic systems,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Speech2spikes: Efficient audio encoding pipeline for real-time neuro- morphic systems,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.137134Z

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 8f616831-4b8c-4036-a79a-83e673b72ea0 · outbound

This paper cites A million spiking-neuron integrated circuit with a scalable communication network and interface,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback A million spiking-neuron integrated circuit with a scalable communication network and interface,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.146158Z

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 ef3274bd-18b7-4983-8d76-76ee833fb691 · outbound

This paper cites Efficient recurrent architectures through activity sparsity and sparse back-propagation through time,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Efficient recurrent architectures through activity sparsity and sparse back-propagation through time,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.118608Z

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 734688dd-02a0-4acc-bc84-a31309dace63 · outbound

This paper cites Tonic: event-based datasets and transformations.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Tonic: event-based datasets and transformations

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.128028Z

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 8b134de8-d0bf-4e72-9a9a-b9a770728921 · outbound

This paper cites Temporal binary representation for event-based action recognition,.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Temporal binary representation for event-based action recognition,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:52:34.110744Z

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 567dff47-2044-47e3-a81f-0a0729b5856a · outbound

This paper cites Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation.

Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-05T22:52:29.895419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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