Paper Citation Record · LEDGER

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.14464.

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

pith.paper-citation-record.v1
2506.14464 v1

Coverage vector

measured 43 of 43 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 7b07c088-cf64-4231-b44a-4b03d46d8bc3 · outbound

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

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Networks of spiking neurons: the third generation of neural network models

Reference 1

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Observation 17788b82-fd0f-401a-861f-41f94e745498 · outbound

This paper cites Neuronal dynamics: From single neurons to networks and models of cognition.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Neuronal dynamics: From single neurons to networks and models of cognition

Reference 2

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Observation ad65981e-5ade-4f33-9022-a3a10f12f99b · outbound

This paper cites A review of spiking neuromorphic hardware communication systems.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks A review of spiking neuromorphic hardware communication systems

Reference 3

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Observation fd6f6f56-42b8-4154-a0cd-23750e706274 · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 4

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Observation d108c32a-546d-4250-b399-f74d30799972 · outbound

This paper cites Hippo: Recurrent mem- ory with optimal polynomial projections.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Hippo: Recurrent mem- ory with optimal polynomial projections

Reference 5

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Observation 79a49d28-9307-4e9d-8aa7-87badef2e73b · outbound

This paper cites Diagonal state spaces are as effective as struc- tured state spaces.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Diagonal state spaces are as effective as struc- tured state spaces

Reference 6

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Observation 4cf66f9a-9bc9-403b-bf0c-fa1e64129add · outbound

This paper cites Resurrecting recurrent neural networks for long sequences.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Resurrecting recurrent neural networks for long sequences

Reference 7

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Observation e8fef2be-d8ca-445a-97a3-f2cdaa242612 · outbound

This paper cites Parallel spiking unit for efficient training of spiking neural networks.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Parallel spiking unit for efficient training of spiking neural networks

Reference 8

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Observation 3011bcde-0447-4bea-9167-eb00883a51b2 · outbound

This paper cites PRF: Parallel Resonate and Fire Neuron for Long Sequence Learning in Spiking Neural Networks.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks PRF: Parallel Resonate and Fire Neuron for Long Sequence Learning in Spiking Neural Networks

Reference 9

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Observation 3ed0a3e8-0908-4644-8a34-c482a2e28a48 · outbound

This paper cites Channel-wise parallelizable spiking neuron with multiplication- free dynamics and large temporal receptive fields.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Channel-wise parallelizable spiking neuron with multiplication- free dynamics and large temporal receptive fields

Reference 10

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Observation 58d2e239-836e-408a-bcb7-6ff414c0b608 · outbound

This paper cites P-spikessm: Harnessing probabilistic spiking state space models for long-range dependency tasks.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks P-spikessm: Harnessing probabilistic spiking state space models for long-range dependency tasks

Reference 11

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Observation 1d8be6a5-a57b-4948-95e9-e314d754e474 · outbound

This paper cites A solution to the learning dilemma for recurrent networks of spiking neurons.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks A solution to the learning dilemma for recurrent networks of spiking neurons

Reference 12

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Observation abf7fd5a-a5eb-4d13-a84d-db103d7da179 · outbound

This paper cites Williams and David Zipser.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Williams and David Zipser

Reference 13

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Observation 87720235-ac40-4342-84ea-859fa997389e · outbound

This paper cites ReckOn: A 28nm sub-mm2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks ReckOn: A 28nm sub-mm2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales

Reference 14

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Observation 8f34f57b-b4be-43f8-adca-291f21274f81 · outbound

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

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation

Reference 15

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Observation 61a41012-52e1-46cf-9a42-17b820e68447 · outbound

This paper cites A surrogate gradient spiking baseline for speech com- mand recognition.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks A surrogate gradient spiking baseline for speech com- mand recognition

Reference 16

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Observation 1f13c924-5ee5-43b9-865f-46bbeb605475 · outbound

This paper cites Balanced resonate-and- fire neurons.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Balanced resonate-and- fire neurons

Reference 17

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Observation 6f07d656-6819-450b-bc54-5602926d9249 · outbound

This paper cites An efficient gradient-based algorithm for on-line training of recurrent network trajectories.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks An efficient gradient-based algorithm for on-line training of recurrent network trajectories

Reference 18

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This paper cites Unbiased online recurrent optimization.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Unbiased online recurrent optimization

Reference 19

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Observation b545a2c3-a981-4757-9a6d-74b443882eaa · outbound

This paper cites Approximating real-time recurrent learning with random kronecker factors.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Approximating real-time recurrent learning with random kronecker factors

Reference 20

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Observation 12dfd109-0c08-4f92-adad-8379afec17b0 · outbound

This paper cites Practical real time recurrent learning with a sparse approximation.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Practical real time recurrent learning with a sparse approximation

Reference 21

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This paper cites an unresolved cited work.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Unresolved cited work

Reference 22

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Observation fc2eaebd-74e2-42b9-aeef-8b53526c34d6 · outbound

This paper cites Online training through time for spiking neural networks.Advances in Neural Information Processing Systems, 35:20717–20730, 2022.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Online training through time for spiking neural networks.Advances in Neural Information Processing Systems, 35:20717–20730, 2022

Reference 23

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Observation 581d0c7d-f7b4-4a93-a1a0-be1843bbe26c · outbound

This paper cites A fixed size storage o(n3) time complexity learning algorithm for fully recurrent continually running networks.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks A fixed size storage o(n3) time complexity learning algorithm for fully recurrent continually running networks

Reference 24

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Observation 891715f9-8e63-40b4-bba4-f8acbba9443c · outbound

This paper cites Exploring the promise and limits of real-time recurrent learning.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Exploring the promise and limits of real-time recurrent learning

Reference 25

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Observation 14a51a31-87f3-4d11-8e70-1dbf10703142 · outbound

This paper cites Online learning of long-range dependencies.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Online learning of long-range dependencies

Reference 26

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Observation f0f122d4-fc9c-4cf9-9559-cfacb93a2036 · outbound

This paper cites Parallel Spiking Neurons with High Efficiency and Abil- ity to Learn Long-term Dependencies.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Parallel Spiking Neurons with High Efficiency and Abil- ity to Learn Long-term Dependencies

Reference 27

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Observation 654a4a39-743c-4412-bd59-670a69ae9ffb · outbound

This paper cites Esser, Paul A.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Esser, Paul A

Reference 28

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Observation 22d3198d-a535-4af8-b134-3ce56450c816 · outbound

This paper cites Long short-term memory and learning-to-learn in networks of spiking neurons.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Long short-term memory and learning-to-learn in networks of spiking neurons

Reference 29

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This paper cites Adam: A Method for Stochastic Optimization.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Adam: A Method for Stochastic Optimization

Reference 30

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Observation 39115bf5-8f57-4dcc-a4d2-acdb3842ed0b · outbound

This paper cites Legendre memory units: Continuous-time representation in recurrent neural networks.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Legendre memory units: Continuous-time representation in recurrent neural networks

Reference 31

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Observation 33bbc927-c97a-421f-920d-8629fe74c604 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 32

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Observation 559fd481-48cb-4762-839a-e3a8e71da6bc · outbound

This paper cites Blelloch.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Blelloch

Reference 33

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Observation a021643e-1351-462d-a895-54f9fb4e8583 · outbound

This paper cites Simplified state space layers for sequence modeling.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Simplified state space layers for sequence modeling

Reference 34

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Observation dd089aa7-5be0-4ca0-b3ca-82d3a61971ca · outbound

This paper cites Accurate and efficient time-domain classi- fication with adaptive spiking recurrent neural networks.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Accurate and efficient time-domain classi- fication with adaptive spiking recurrent neural networks

Reference 35

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Observation 1efa35c7-9bf1-4054-af51-b80b01102e83 · outbound

This paper cites The Hei- delberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks The Hei- delberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks

Reference 36

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Observation fae331dd-6758-48cc-ba1d-ef2f138ede79 · outbound

This paper cites A database for evaluation of algorithms for measurement of qt and other waveform intervals in the ecg.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks A database for evaluation of algorithms for measurement of qt and other waveform intervals in the ecg

Reference 37

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Observation 5dfa3d54-f647-43bc-b7fd-952e858cc588 · outbound

This paper cites The MNIST database of handwritten digits.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks The MNIST database of handwritten digits

Reference 38

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This paper cites Long Range Arena: A Benchmark for Efficient Transformers.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Long Range Arena: A Benchmark for Efficient Transformers

Reference 39

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This paper cites Learning multiple layers of features from tiny images.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Learning multiple layers of features from tiny images

Reference 40

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This paper cites Slayer: Spike layer error reassignment in time.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks Slayer: Spike layer error reassignment in time

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.062913Z

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This paper cites SGDR: Stochastic gradient descent with warm restarts.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks SGDR: Stochastic gradient descent with warm restarts

Reference 42

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This paper cites JAX: composable transformations of Python+NumPy programs, 2018.

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks JAX: composable transformations of Python+NumPy programs, 2018

Reference 43

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Pith citing papers

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