Paper Citation Record · LEDGER

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions

As of 11 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2507.18139.

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

pith.paper-citation-record.v1
2507.18139 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:42:36.602126Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact1
verified fuzzy25
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5290108d-3422-4f59-874c-013758ee60d7 · outbound

This paper cites Carsnn: An efficient spiking neural network for event- based autonomous cars on the loihi neuromorphic research processor,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Carsnn: An efficient spiking neural network for event- based autonomous cars on the loihi neuromorphic research processor,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.942438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b5550521-cf67-43a8-801f-94e18622abce · outbound

This paper cites Lanesnns: Spiking neural networks for lane detection on the loihi neuromorphic processor,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Lanesnns: Spiking neural networks for lane detection on the loihi neuromorphic processor,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.933367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation af0cbb27-64b0-42cd-b1e5-31a0fb2c6a7e · outbound

This paper cites Embedded neuromorphic using intel’s loihi processor,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Embedded neuromorphic using intel’s loihi processor,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.924278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1e706acf-9504-43c9-912b-f95ab50ae6f1 · outbound

This paper cites Embodied neuromorphic artificial intelligence for robotics: Perspectives, challenges, and research development stack,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Embodied neuromorphic artificial intelligence for robotics: Perspectives, challenges, and research development stack,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.915151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 488cca36-9805-4e96-9058-16482fd661c7 · outbound

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

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Networks of spiking neurons: the third generation of neural network models,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.905614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ceb89216-5de8-426a-92b2-84170c67fa31 · outbound

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

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.897574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 396adf26-75bd-4abc-8193-6584716c1ba3 · outbound

This paper cites Efficient neuromorphic signal processing with loihi 2,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Efficient neuromorphic signal processing with loihi 2,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.889709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4b2ab76d-287c-4935-af09-e110159e317d · outbound

This paper cites The brainscales-2 accelerated neuromorphic system with hybrid plasticity,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions The brainscales-2 accelerated neuromorphic system with hybrid plasticity,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.881726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9da3874c-79ed-4506-9781-87fe54a04266 · outbound

This paper cites Dynap-se2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Dynap-se2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.873383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d872b8b5-b0aa-4b59-937a-db6ce977bc4f · outbound

This paper cites A 128x128 120 db 15us latency asynchronous temporal contrast vision sensor,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions A 128x128 120 db 15us latency asynchronous temporal contrast vision sensor,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.773414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 14856054-a520-4eaf-92f7-48f0b84d4996 · outbound

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

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions A low power, fully event-based gesture recognition system,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.765481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be77d811-72b3-49f5-8849-215d6acf7663 · outbound

This paper cites Hots: a hierarchy of event-based time-surfaces for pattern recognition,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Hots: a hierarchy of event-based time-surfaces for pattern recognition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.757753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 29a2150a-3a16-48fb-93cf-0093d8eef255 · outbound

This paper cites Hats: Histograms of averaged time surfaces for robust event-based object classification,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Hats: Histograms of averaged time surfaces for robust event-based object classification,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.749409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1d152d89-ea28-422d-aedc-fa69b4363e2b · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Towards deep learning models resistant to adversarial attacks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.741551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2935875a-39a6-46bc-af70-3f579e054ea8 · outbound

This paper cites Snn-rat: Robustness-enhanced spiking neural network through regularized adversarial training,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Snn-rat: Robustness-enhanced spiking neural network through regularized adversarial training,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.733612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 29973b1e-fa5e-4353-a4aa-18d9bfeb37d1 · outbound

This paper cites Somewhat practical fully homomorphic encryption,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Somewhat practical fully homomorphic encryption,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T14:42:36.573074Z

Source-reported events for the cited work

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Observation 761d213d-b21a-489c-857d-c5d1665fcb53 · outbound

This paper cites A homomorphic encryption framework for privacy- preserving spiking neural networks,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions A homomorphic encryption framework for privacy- preserving spiking neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.720468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dbe45fa0-56f0-4d0a-94e5-5296247fe6e4 · outbound

This paper cites Securing deep spiking neural networks against adversarial attacks through inherent structural parameters,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Securing deep spiking neural networks against adversarial attacks through inherent structural parameters,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.712965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5e875abb-77a1-4ab5-8aed-46c24cf2b6a4 · outbound

This paper cites enpheeph: A fault injection framework for spiking and compressed deep neural networks,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions enpheeph: A fault injection framework for spiking and compressed deep neural networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.705543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bcce87ad-94de-4f20-8867-ea2c01df592f · outbound

This paper cites Snn4agents: a framework for developing energy-efficient embodied spiking neural networks for autonomous agents,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Snn4agents: a framework for developing energy-efficient embodied spiking neural networks for autonomous agents,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.697525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a709f881-976d-419c-8627-4add12ab357b · outbound

This paper cites Fastspiker: Enabling fast training for spiking neural networks on event-based data through learning rate enhancements for autonomous embedded systems,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Fastspiker: Enabling fast training for spiking neural networks on event-based data through learning rate enhancements for autonomous embedded systems,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.689593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7200841a-cec8-44db-832e-b5fc9717f2f1 · outbound

This paper cites A methodology to study the impact of spiking neural network parameters considering event-based automotive data,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions A methodology to study the impact of spiking neural network parameters considering event-based automotive data,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.681439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80238807-68c3-4bfe-8c30-3c6406edf312 · outbound

This paper cites R-snn: An analysis and design methodology for robustifying spiking neural networks against adversarial attacks through noise filters for dynamic vision sensors,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions R-snn: An analysis and design methodology for robustifying spiking neural networks against adversarial attacks through noise filters for dynamic vision sensors,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.673197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 12041350-6c10-4247-8285-cfecc9a42761 · outbound

This paper cites lpspikecon: Enabling low-precision spiking neural network processing for efficient unsupervised continual learning on autonomous agents,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions lpspikecon: Enabling low-precision spiking neural network processing for efficient unsupervised continual learning on autonomous agents,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.665176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a17c2e81-554f-417c-912c-2e1a6dfe5975 · outbound

This paper cites Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:42:36.640013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 13bf7663-5e4f-4f0f-bd47-21c9833033a1 · outbound

This paper cites Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T14:42:36.597160Z

Source-reported events for the cited work

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Observation 4bd0571a-b69e-4b14-8113-15d0cc94370d · outbound

This paper cites The road to commercial success for neuromorphic technologies,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions The road to commercial success for neuromorphic technologies,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.656904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 39822956-2653-43bc-a832-0b03883f7beb · outbound

This paper cites Neuromorphic computing at scale,.

Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions Neuromorphic computing at scale,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:36.648244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.