Paper Citation Record · LEDGER

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware

As of 5 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2601.16118.

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

pith.paper-citation-record.v1
2601.16118 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T12:01:57.764416Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T06:23:49.990308Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-21T06:24:00.461907Z

Reference resolution

40 of 40 outbound references displayed

verified exact13
verified fuzzy23
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3b37b7d2-6c12-4120-a787-bfef4289b640 · outbound

This paper cites Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.434944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6168ea93-6a37-41f3-8609-1d0a3b274fde · outbound

This paper cites Spiking neural networks hardware implementations and challenges: A survey.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Spiking neural networks hardware implementations and challenges: A survey

Reference 2

Resolution: verified exact; doi, observed 2026-05-16T12:02:50.542957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eb44c998-0e81-4887-9bb0-c3efecaabe94 · outbound

This paper cites Spiking neural networks: A survey.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Spiking neural networks: A survey

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.438009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2755f7f4-d6f5-4fa6-8f75-728197766c40 · outbound

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

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Loihi: A neuromorphic manycore processor with on- chip learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.447404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 18120c85-7cc2-476f-aa38-b53ba4dfdc92 · outbound

This paper cites The spinnaker project.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware The spinnaker project

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.415199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4e4ca685-418e-4073-b130-42a73cfc4f79 · outbound

This paper cites Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.412468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0f32bc26-108d-4f37-be64-4a06fb8eda2d · outbound

This paper cites Mapping very large scale spiking neuron network to neuromorphic hardware.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Mapping very large scale spiking neuron network to neuromorphic hardware

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.444328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0126bfcb-e005-4d40-a2ee-46be7ff5c3a0 · outbound

This paper cites Mapping very large scale spiking neuron network to neuromorphic hardware.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Mapping very large scale spiking neuron network to neuromorphic hardware

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T12:02:50.464289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 04720c8f-f3a9-4f45-8d19-b4c1e7cb674f · outbound

This paper cites A linear-time heuristic for improving network partitions.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware A linear-time heuristic for improving network partitions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.418025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 33c954c6-9fac-4cf7-8cf7-12c032111215 · outbound

This paper cites Hypergraph partitioning and clustering.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Hypergraph partitioning and clustering

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.420730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7e8c19bd-42ea-4a46-a9e5-2703fc5dbf9d · outbound

This paper cites Multilevel hyper- graph partitioning: Applications in vlsi domain.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Multilevel hyper- graph partitioning: Applications in vlsi domain

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.428551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 01cbe8df-ec3a-436c-addc-8f35a83cfc01 · outbound

This paper cites Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.406738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e6338744-6123-4aec-80b6-4b9a3954a73e · outbound

This paper cites Mapping spiking neural networks onto a manycore neuromorphic architecture.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Mapping spiking neural networks onto a manycore neuromorphic architecture

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:02:50.469838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0f25e451-c65a-4f76-8fca-db0843ac814d · outbound

This paper cites Mapping spiking neural networks to neuromorphic hardware.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Mapping spiking neural networks to neuromorphic hardware

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.403612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a2ed3326-aa1e-46b9-8c5f-3dafce59eef4 · outbound

This paper cites Dfsynthesizer: Dataflow-based synthesis of spiking neural networks to neuromorphic hardware.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Dfsynthesizer: Dataflow-based synthesis of spiking neural networks to neuromorphic hardware

Reference 15

Resolution: verified exact; doi, observed 2026-05-16T12:02:50.530773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 78fb6dbe-d7fc-4aef-a6f7-edcaaf11baae · outbound

This paper cites Edgemap: An optimized mapping toolchain for spiking neural network in edge computing.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Edgemap: An optimized mapping toolchain for spiking neural network in edge computing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.409565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a26aacb9-7b16-4506-b6d0-ffe8340da763 · outbound

This paper cites Hierarchical mapping of large-scale spiking convolutional neural networks onto resource-constrained neuromorphic processor.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Hierarchical mapping of large-scale spiking convolutional neural networks onto resource-constrained neuromorphic processor

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.431684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ae943f54-f9bf-4e03-a6a6-ff0a05720d87 · outbound

This paper cites Benchmarking spiking network partitioning methods on loihi 2.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Benchmarking spiking network partitioning methods on loihi 2

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:02:50.513494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a146f6a9-abe6-4307-a0dd-710dabf3d0ce · outbound

This paper cites Liquid computing.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Liquid computing

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.440708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b7c6052f-f691-4ba8-8243-f0a4ca99390a · outbound

This paper cites High-quality hypergraph partitioning.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware High-quality hypergraph partitioning

Reference 20

Resolution: verified exact; doi, observed 2026-05-16T12:02:50.517924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d316ed49-6808-4b51-b32e-ba843f1488b8 · outbound

This paper cites an unresolved cited work.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-05-16T12:02:51.460258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cfb5d926-1db7-4ee0-b661-d57f5bb1837b · outbound

This paper cites Learning with hypergraphs: Clustering, classification, and embedding.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Learning with hypergraphs: Clustering, classification, and embedding

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.453908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bcad7d35-ad71-40d6-ae10-2fa411158108 · outbound

This paper cites Conversion of continuous-valued deep networks to efficient event-driven networks for image classification.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Conversion of continuous-valued deep networks to efficient event-driven networks for image classification

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.457181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d3ed7f52-bfb7-4ead-9ab7-2f514d7397b5 · outbound

This paper cites Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence

Reference 24

Resolution: verified exact; doi, observed 2026-05-16T12:02:50.549619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1e7141a7-18f9-4a15-8b61-3d802806c85f · outbound

This paper cites Rethinking the performance comparison between snns and anns.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Rethinking the performance comparison between snns and anns

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.463404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 831e0e6f-818f-43dc-b288-ac934816967d · outbound

This paper cites Training feedback spiking neural networks by implicit differentiation on the equilibrium state.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Training feedback spiking neural networks by implicit differentiation on the equilibrium state

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.425333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 756fa737-4c95-44aa-94c2-4e934286400a · outbound

This paper cites Online adaptation and energy minimization for hardware recurrent spiking neural networks.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Online adaptation and energy minimization for hardware recurrent spiking neural networks

Reference 27

Resolution: verified exact; doi, observed 2026-05-16T12:02:50.546110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fbc12e75-a57c-4fc4-ac12-ac5488b827b1 · outbound

This paper cites Line: Large-scale information network embedding.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Line: Large-scale information network embedding

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:02:50.535979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c4afd9ea-5edd-48b6-8062-70363ec6846f · outbound

This paper cites an unresolved cited work.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-05-16T12:02:51.479114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d53b3d99-a69e-46c1-8bff-749b5ef8abfe · outbound

This paper cites k-way Hypergraph Partitioning via n-Level Recursive Bisection.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware k-way Hypergraph Partitioning via n-Level Recursive Bisection

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-16T12:02:50.775445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 18d447b6-560b-4a2c-b06b-248c1b52c315 · outbound

This paper cites Multilevel k-way hypergraph partitioning.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Multilevel k-way hypergraph partitioning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:02:50.527189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 912b18b1-e1e7-4f06-8908-c22201aacffc · outbound

This paper cites title =.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware title =

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T12:02:50.474873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6c57ee46-150d-47e8-8b59-cf2c6514e182 · outbound

This paper cites Drawing graphs by eigenvectors: theory and practice.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Drawing graphs by eigenvectors: theory and practice

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.482941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a09a5ec3-9409-4324-ae01-8e35e082bb26 · outbound

This paper cites Laplacian eigenmaps for dimensionality reduction and data representation.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Laplacian eigenmaps for dimensionality reduction and data representation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.473796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 18ac1949-7d99-43f2-85d4-57509314d11b · outbound

This paper cites Lehoucq, D.C.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Lehoucq, D.C

Reference 35

Resolution: verified exact; doi, observed 2026-05-16T12:02:50.539337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3db0b39f-ff91-4c54-95d4-e857c0cfa6d6 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-16T12:02:50.779981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c205398d-578e-442b-a233-898ad173a49d · outbound

This paper cites Cholletet al., “Keras, ” https://keras.io.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Cholletet al., “Keras, ” https://keras.io

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.466540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 12eff54d-ee8d-4f60-aeaf-e455fddb708a · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Pytorch: An imperative style, high-performance deep learning library

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.470053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f40c3967-3313-44c7-850d-5bb65ee6de68 · outbound

This paper cites Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex

Reference 39

Resolution: verified exact; doi, observed 2026-05-16T12:02:50.521869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0f19f248-3152-4009-9d32-c35841e288dd · outbound

This paper cites On the distribution of firing rates in networks of cortical neurons.

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware On the distribution of firing rates in networks of cortical neurons

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T12:02:51.450729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 0edc6e4e-dce9-4f29-b3f8-7d27f29e6e92 · inbound

Incidence Constraints in Hypergraph Partitioning on GPU cites this paper.

Incidence Constraints in Hypergraph Partitioning on GPU A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-10T11:45:21.363681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d21ebbbe-33c1-49d7-9628-16c04e852763 · inbound

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints cites this paper.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-21T06:24:00.463565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.