Paper Citation Record · LEDGER

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks

As of 16 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:1909.01311.

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

pith.paper-citation-record.v1
1909.01311 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:27:15.016126Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:41:05.727472Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T11:41:06.100668Z

Reference resolution

49 of 49 outbound references displayed

verified exact0
verified fuzzy46
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fa2fe3db-e8ae-498c-b6f9-a46a7e6d7b23 · outbound

This paper cites Rosenblatt, Principles of neurodynamics: Perceptrons and the theory of brain mechanisms, Sparta, NJ, USA: Spartan Books, 1961.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Rosenblatt, Principles of neurodynamics: Perceptrons and the theory of brain mechanisms, Sparta, NJ, USA: Spartan Books, 1961

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.781588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 718ca3c1-0716-4253-ad59-106ed73d015d · outbound

This paper cites Small-world brain networ ks,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Small-world brain networ ks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.766635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9c62ed77-6f71-4506-b34b-6b542c097b6e · outbound

This paper cites Steps toward artiﬁcial intelligence,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Steps toward artiﬁcial intelligence,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.750925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c92ff28d-ddab-424f-a5ba-dd6726b3839c · outbound

This paper cites Learning repre sentations by back-propagating errors,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Learning repre sentations by back-propagating errors,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.736403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2daa5de4-48e5-46a2-9dd6-08107ab1306b · outbound

This paper cites ImageNet c lassiﬁcation with deep convolutional neural networks,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks ImageNet c lassiﬁcation with deep convolutional neural networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.722623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 524486d6-b8d0-4dac-a548-0e9da0308b29 · outbound

This paper cites Deep learning,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Deep learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.709311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation adb99818-e574-41ff-a83f-e297eae5b629 · outbound

This paper cites Deep residual learning for image recogniti on,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Deep residual learning for image recogniti on,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.695598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59f631c9-8c6b-4cb2-89e1-0256e5e172a8 · outbound

This paper cites Deep neural networks for acoustic mo deling in speech recognition,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Deep neural networks for acoustic mo deling in speech recognition,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.680571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a7963e75-4e89-4bdf-a0fb-f990060bc26a · outbound

This paper cites Deep speech 2: End-to-end speech recog nition in english and mandarin,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Deep speech 2: End-to-end speech recog nition in english and mandarin,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.665479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d66e431b-8301-4f37-a57f-26e8fef902bb · outbound

This paper cites Competitive learning: From interactiv e activation to adaptive resonance,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Competitive learning: From interactiv e activation to adaptive resonance,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.650889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1b4302ea-1095-4de7-b4f3-84016c36ab49 · outbound

This paper cites Local learning in RRAM neural networ ks with sparse direct feedback alignment,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Local learning in RRAM neural networ ks with sparse direct feedback alignment,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.634940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 209898bd-5c56-40da-a0b2-ea48abba0b42 · outbound

This paper cites How important is weig ht symmetry in backpropagation?,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks How important is weig ht symmetry in backpropagation?,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.619672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 11699207-e08f-4297-a555-dd25fbcfd24f · outbound

This paper cites Random synaptic feedback weigh ts support error backpropagation for deep learning,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Random synaptic feedback weigh ts support error backpropagation for deep learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.605538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7c8636d9-70f4-4193-a3b8-3449531ffc9e · outbound

This paper cites Learning in the machine : Random backpropagation and the deep learning channel,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Learning in the machine : Random backpropagation and the deep learning channel,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.590879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7257bfd8-2ba5-42b4-ab61-eed2ab8e8659 · outbound

This paper cites Direct feedback alignment provides learn ing in deep neural networks,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Direct feedback alignment provides learn ing in deep neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.576183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fdacd46f-efc9-4797-873a-1c5876fc7ec7 · outbound

This paper cites The MNIST database of handwritt en digits,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks The MNIST database of handwritt en digits,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.560167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bfe4c270-4ac7-4c53-b387-18186f525346 · outbound

This paper cites Krizhevsky, Learning multiple layers of features from tiny images , Technical Report, University of Toronto, 2009.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Krizhevsky, Learning multiple layers of features from tiny images , Technical Report, University of Toronto, 2009

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.545520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 27af0272-bd4d-4e81-9aef-7a84fdeb2633 · outbound

This paper cites Towards deep learning with segregated dendrites,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Towards deep learning with segregated dendrites,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.530637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0edd8278-c3e8-4a91-9d4f-3f1cce03435f · outbound

This paper cites Event-driven random back-propagati on: Enabling neuromorphic deep learning machines,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Event-driven random back-propagati on: Enabling neuromorphic deep learning machines,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.515773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1afd7da2-9561-41f5-b7a1-2075c6a63a37 · outbound

This paper cites Learning by the dendritic pre diction of somatic spiking,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Learning by the dendritic pre diction of somatic spiking,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.502261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c37537cf-2f09-4fa4-8b87-d394b7ba51b4 · outbound

This paper cites Difference target propagation,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Difference target propagation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.488568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5dfc5d47-e046-476a-8e25-a01556ea112a · outbound

This paper cites Biologically motivated algo rithms for propagating local target representations,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Biologically motivated algo rithms for propagating local target representations,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.473788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 84dfda9d-3d5a-4327-922b-e871b50f0d00 · outbound

This paper cites Decoupled neural interfaces usin g synthetic gradients,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Decoupled neural interfaces usin g synthetic gradients,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.459290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8fd333c2-5f5b-4312-99fe-3d81747c3a21 · outbound

This paper cites Understanding synthetic gradien ts and decoupled neural interfaces,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Understanding synthetic gradien ts and decoupled neural interfaces,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.444507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 25488cc2-9373-445b-be0e-b02211067d14 · outbound

This paper cites Deep superv ised learning using local errors.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Deep superv ised learning using local errors

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.429596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 939bbcb3-d7e5-4517-a340-bf9a40e51c78 · outbound

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

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Synaptic plastici ty dynamics for deep continuous local learning (DECOLLE),

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.413961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e168f47e-84e8-4269-957f-fc1bb0377a67 · outbound

This paper cites Training neural networks w ith local error signals.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Training neural networks w ith local error signals

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.398744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e8bf8a0f-acd8-4796-9e69-b7dfd6e03c6a · outbound

This paper cites Decoupled Greedy Learning of CNNs.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Decoupled Greedy Learning of CNNs

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T05:27:14.917344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5746a847-7877-401b-9588-6d30884cd51f · outbound

This paper cites ImageNet: A large-scale hierarchical i mage database,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks ImageNet: A large-scale hierarchical i mage database,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.383521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 15fa638a-e1ad-4ded-b2da-be9f26e27479 · outbound

This paper cites Can we connect trill ions of IoT sensors in a sustainable way? A technol- ogy/circuit perspective,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Can we connect trill ions of IoT sensors in a sustainable way? A technol- ogy/circuit perspective,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.368221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e0d0b3f7-d545-48fe-9ab3-d61405e7e511 · outbound

This paper cites Obstacle avoidance and target acqui sition for robot navigation using a mixed signal ana- log/digital neuromorphic processing system,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Obstacle avoidance and target acqui sition for robot navigation using a mixed signal ana- log/digital neuromorphic processing system,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.354509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5cb5ab39-a12b-4e2a-906a-8a7c805fca5b · outbound

This paper cites Assessing the scalability of biolo gically-motivated deep learning algorithms and architec- tures,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Assessing the scalability of biolo gically-motivated deep learning algorithms and architec- tures,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.339682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 540fa071-6b7a-4830-96d5-543dfaf2e537 · outbound

This paper cites A 28-nm convolution al neuromorphic processor enabling online learning with spike-based retinas,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks A 28-nm convolution al neuromorphic processor enabling online learning with spike-based retinas,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.323419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c1a442de-93b5-4d4b-a69d-e85e799b6963 · outbound

This paper cites A 0.086-mm 2 12.7-pJ/SOP 64k-synapse 256-neuron online-learning digi tal spiking neuro- morphic processor in 28-nm CMOS,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks A 0.086-mm 2 12.7-pJ/SOP 64k-synapse 256-neuron online-learning digi tal spiking neuro- morphic processor in 28-nm CMOS,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.309838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 20b1eccf-8d2e-40cc-99a3-727a7fb2bab6 · outbound

This paper cites MorphIC: A 65-nm 738 k-synapse/mm2 quad-core binary-weight digital neuromorphic processor with stochastic spike-driven onli ne learning.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks MorphIC: A 65-nm 738 k-synapse/mm2 quad-core binary-weight digital neuromorphic processor with stochastic spike-driven onli ne learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.295459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation be298e5a-96e6-4f4c-96a1-4feb20ccbce3 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-14T05:27:14.954165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d4b2f2d-da97-4f62-9678-e91a68459621 · outbound

This paper cites Scikit-learn: Machine Learning i n Python.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Scikit-learn: Machine Learning i n Python

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.280400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f9b1369a-4ec1-41c4-9551-384e5ff82218 · outbound

This paper cites Delving deep into rectiﬁers: Surpassing h uman-level performance on ImageNet classiﬁcation,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Delving deep into rectiﬁers: Surpassing h uman-level performance on ImageNet classiﬁcation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.264348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 747449be-3cdd-46a7-8176-65ac208bfb72 · outbound

This paper cites Computing’s energy problem (and what we c an do about it),.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Computing’s energy problem (and what we c an do about it),

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.249779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 71d3d34e-977f-4616-a1a1-82a73d52d940 · outbound

This paper cites Synaptic plasticity fo rms and functions,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Synaptic plasticity fo rms and functions,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.235472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ce568a19-69f4-429b-b600-de08d2e75311 · outbound

This paper cites Large-scale neuromorphic spiking array processors: A quest to mimic the brain,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Large-scale neuromorphic spiking array processors: A quest to mimic the brain,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.219975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation da3e18c0-b58b-4e9a-a80c-8d8063bb6459 · outbound

This paper cites Low-Power Neuromorphic Hardware for Signal Processing Applications.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Low-Power Neuromorphic Hardware for Signal Processing Applications

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.203729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 67c9aaf3-fa14-4b41-9d23-617e4d76fe88 · outbound

This paper cites Synaptic modiﬁcations in cultur ed hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Synaptic modiﬁcations in cultur ed hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.187545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d9202e42-0338-4b23-914a-25e113fedeea · outbound

This paper cites Learning real-world s timuli in a neural network with spike-driven synaptic dynamics,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Learning real-world s timuli in a neural network with spike-driven synaptic dynamics,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.169977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0ce0f316-a5ba-430e-bf20-ba8c5945d00f · outbound

This paper cites Neural and synaptic array transce iver: A brain-inspired computing framework for embedded learning,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Neural and synaptic array transce iver: A brain-inspired computing framework for embedded learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.154462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 869e6e3a-b569-4009-8f08-584c5de721eb · outbound

This paper cites A 65-nm neuromorphic image c lassiﬁcation processor with energy-efﬁcient training through direct spike-only feedback,.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks A 65-nm neuromorphic image c lassiﬁcation processor with energy-efﬁcient training through direct spike-only feedback,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.139336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation be32d119-1224-4137-8d4f-4ccbb8f06e05 · outbound

This paper cites Principled Training of Neural Networks with Direct Feedback Alignment.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Principled Training of Neural Networks with Direct Feedback Alignment

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-14T05:27:15.006381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d961f59c-b657-4304-b085-c2d14e66aa80 · outbound

This paper cites Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.122737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8cc4fdbf-2c0b-46a8-8669-19bb51195ee4 · outbound

This paper cites Automatic differentiation in PyTorc h.

Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks Automatic differentiation in PyTorc h

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:15.106535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation 1f38cd2d-757b-415e-b927-8b93c2d471c9 · inbound

Spiking Neural Predictive Coding for Continual Learning from Data Streams cites this paper.

Spiking Neural Predictive Coding for Continual Learning from Data Streams Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-14T11:41:06.104786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.