Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective | Paper Citation Record

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This paper cites Abbott and W.G.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Abbott and W.G

Reference 1

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This paper cites Naous, E.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Naous, E

Reference 2

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This paper cites Yodann: An ultra-low power convolutional neural network accelerator based on binary weights.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Yodann: An ultra-low power convolutional neural network accelerator based on binary weights

Reference 3

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This paper cites Normad-normalized approximate descent based supervised learning rule for spiking neurons.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Normad-normalized approximate descent based supervised learning rule for spiking neurons

Reference 4

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This paper cites Endurance/retention trade off in hfox and taox based rram.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Endurance/retention trade off in hfox and taox based rram

Reference 5

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This paper cites Bartolozzi and G.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Bartolozzi and G

Reference 6

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This paper cites Bartolozzi and G.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Bartolozzi and G

Reference 7

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This paper cites Bartolozzi and G.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Bartolozzi and G

Reference 8

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This paper cites Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets

Reference 9

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

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations

Reference 10

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This paper cites Bi and M-M.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Bi and M-M

Reference 11

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This paper cites Spikeprop: backpropagation for networks of spiking neurons.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Spikeprop: backpropagation for networks of spiking neurons

Reference 12

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This paper cites The probability of neurotransmitter release: variability and feedback control at single synapses.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective The probability of neurotransmitter release: variability and feedback control at single synapses

Reference 13

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Observation d80a18f5-c16a-4854-8787-356d7b9ef739 · outbound

This paper cites Mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse

Reference 14

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This paper cites Physical mechanisms of endurance degradation in tmo-rram.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Physical mechanisms of endurance degradation in tmo-rram

Reference 15

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This paper cites Chicca, F.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Chicca, F

Reference 16

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This paper cites Training deep neural networks with low precision multiplications.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Training deep neural networks with low precision multiplications

Reference 17

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This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 18

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This paper cites Davies, N.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Davies, N

Reference 19

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This paper cites Contrastive Hebbian Learning with Random Feedback Weights.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Contrastive Hebbian Learning with Random Feedback Weights

Reference 20

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This paper cites Pedroni, Nikil Dutt, Jeffrey Krichmar, Gert Cauwenberghs, and Emre Neftci.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Pedroni, Nikil Dutt, Jeffrey Krichmar, Gert Cauwenberghs, and Emre Neftci

Reference 21

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This paper cites Noise in the nervous system.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Noise in the nervous system

Reference 22

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Unresolved cited work

Reference 23

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This paper cites Modeling and analysis of passive switching crossbar arrays.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Modeling and analysis of passive switching crossbar arrays

Reference 24

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This paper cites Overcoming crossbar nonidealities in binary neural networks through learning.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Overcoming crossbar nonidealities in binary neural networks through learning

Reference 25

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This paper cites On Resistive Memories: One Step Row Readout Technique and Sensing Circuitry.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective On Resistive Memories: One Step Row Readout Technique and Sensing Circuitry

Reference 26

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This paper cites The spinnaker project.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective The spinnaker project

Reference 27

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This paper cites Gerstner and W.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Gerstner and W

Reference 28

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

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Neuronal dynamics: From single neurons to networks and models of cognition

Reference 29

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This paper cites Goldberg, G.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Goldberg, G

Reference 30

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This paper cites G \"u tig and H.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective G \"u tig and H

Reference 31

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Training products of experts by minimizing contrastive divergence

Reference 32

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This paper cites Hopfield.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Hopfield

Reference 33

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This paper cites Maldonado Huayaney, Stephen Nease, and Elisabetta Chicca.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Maldonado Huayaney, Stephen Nease, and Elisabetta Chicca

Reference 34

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Gradient Descent for Spiking Neural Networks

Reference 35

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Hyv \"a rinen

Reference 36

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Brain-inspired computing with resistive switching memory (rram): Devices, synapses and neural networks

Reference 37

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Resource-Aware Pareto-Optimal Automated Machine Learning Platform

Reference 38

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This paper cites A local learning rule for independent component analysis.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective A local learning rule for independent component analysis

Reference 39

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Decoupled Neural Interfaces using Synthetic Gradients

Reference 40

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars

Reference 41

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Predicting spike timing of neocortical pyramidal neurons by simple threshold models

Reference 42

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective SMPLR: Deep SMPL reverse for 3D human pose and shape recovery

Reference 43

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Network Plasticity as Bayesian Inference

Reference 44

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Unresolved cited work

Reference 45

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This paper cites Deep neural network optimized to resistive memory with nonlinear current-voltage characteristics.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Deep neural network optimized to resistive memory with nonlinear current-voltage characteristics

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.419012Z

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This paper cites A memory frontier for complex synapses.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective A memory frontier for complex synapses

Reference 47

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This paper cites Energy-efficient neuronal computation via quantal synaptic failures.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Energy-efficient neuronal computation via quantal synaptic failures

Reference 48

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This paper cites Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Efficient and self-adaptive in-situ learning in multilayer memristor neural networks

Reference 49

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Random synaptic feedback weights support error backpropagation for deep learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.355534Z

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This paper cites Maass, T.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Maass, T

Reference 51

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Observation 3c574af5-3da5-4a6d-9bad-f02bce250fed · outbound

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Unresolved cited work

Reference 52

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Observation d9d5d53a-e8f9-46e1-a1ec-661e77c644e9 · outbound

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

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective A million spiking-neuron integrated circuit with a scalable communication network and interface

Reference 53

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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Interval fragmentations with choice: equidistribution and the evolution of tagged fragments

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:16:31.326075Z

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This paper cites Moreno-Bote.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Moreno-Bote

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.301272Z

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Observation a2e0599f-ad96-4838-aa01-cd40fc948f51 · outbound

This paper cites Deep supervised learning using local errors.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Deep supervised learning using local errors

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:16:31.304373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 60abbcd5-1391-4de0-9d99-7ca413f73e2c · outbound

This paper cites Understanding rram endurance, retention and window margin trade-off using experimental results and simulations.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Understanding rram endurance, retention and window margin trade-off using experimental results and simulations

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.283800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b0c1193b-9f89-4a18-9081-cb9f21fd145c · outbound

This paper cites Memristor-based neural networks: Synaptic versus neuronal stochasticity.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Memristor-based neural networks: Synaptic versus neuronal stochasticity

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.267497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9d481d09-00cd-40b3-bcf8-0b7c21a57177 · outbound

This paper cites Stochastic synapses as resource for efficient deep learning machines.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Stochastic synapses as resource for efficient deep learning machines

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.252236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 65c68b4b-1ffa-476d-b98c-4466e067ba03 · outbound

This paper cites Event-driven random back-propagation: Enabling neuromorphic deep learning machines.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Event-driven random back-propagation: Enabling neuromorphic deep learning machines

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.238260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation df20c34e-7ebb-463e-bd76-e9b984e3487e · outbound

This paper cites an unresolved cited work.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Unresolved cited work

Reference 61

Resolution: verified exact; doi, observed 2026-08-14T05:16:30.948031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 35ac945b-bfbe-4d0e-9c8f-20f3ee362393 · outbound

This paper cites Stochastic synapses enable efficient brain-inspired learning machines.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Stochastic synapses enable efficient brain-inspired learning machines

Reference 62

Resolution: verified exact; raw_fallback, observed 2026-08-14T05:16:31.283242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 04115488-815c-4465-88ab-3838e4ca1eff · outbound

This paper cites Neftci, Charles Augustine, Somnath Paul, and Georgios Detorakis.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Neftci, Charles Augustine, Somnath Paul, and Georgios Detorakis

Reference 63

Resolution: verified exact; raw_fallback, observed 2026-08-14T05:16:31.207935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b3c6f49d-482f-4db8-af6f-991bffe9d77b · outbound

This paper cites Surrogate Gradient Learning in Spiking Neural Networks.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Surrogate Gradient Learning in Spiking Neural Networks

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-14T05:16:30.744750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4cd5b3f-f1fc-400c-b442-aa5ef08226ef · outbound

This paper cites Gabaergic circuits control spike-timing-dependent plasticity.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Gabaergic circuits control spike-timing-dependent plasticity

Reference 65

Resolution: verified exact; doi, observed 2026-08-14T05:16:30.931847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c06fe0a2-99ca-48c2-b70b-41effa65ab0e · outbound

This paper cites an unresolved cited work.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-14T05:16:32.224237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 11a1fd4d-22d6-470f-961e-420fec31818d · outbound

This paper cites Tio x-based rram synapse with 64-levels of conductance and symmetric conductance change by adopting a hybrid pulse scheme for neuromorphic computing.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Tio x-based rram synapse with 64-levels of conductance and symmetric conductance change by adopting a hybrid pulse scheme for neuromorphic computing

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.209886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a23e5d5e-7327-4145-a255-fb3869efc571 · outbound

This paper cites Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.194244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 28fbd135-b06a-4874-a7f1-4ee309e800a2 · outbound

This paper cites Training and operation of an integrated neuromorphic network based on metal-oxide memristors.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Training and operation of an integrated neuromorphic network based on metal-oxide memristors

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.178475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 44df6d07-aaea-4434-aa7e-65d4077e6d93 · outbound

This paper cites Training and operation of an integrated neuromorphic network based on metal-oxide memristors.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Training and operation of an integrated neuromorphic network based on metal-oxide memristors

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.162786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e5036c57-3a61-4e6f-a564-085868e03a8b · outbound

This paper cites A novel program-verify algorithm for multi-bit operation in hfo 2 rram.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective A novel program-verify algorithm for multi-bit operation in hfo 2 rram

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.147781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e589bc68-ed55-4b04-b01a-389eb74b677e · outbound

This paper cites A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128k synapses.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128k synapses

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.132590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5a372425-d208-4648-8899-0cb6e8665123 · outbound

This paper cites Bioinspired programming of memory devices for implementing an inference engine.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Bioinspired programming of memory devices for implementing an inference engine

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.117023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 94ea0576-2895-48c0-bb5e-3d59b780acfc · outbound

This paper cites Xnor-net: Imagenet classification using binary convolutional neural networks.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Xnor-net: Imagenet classification using binary convolutional neural networks

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.101074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a2961c3c-b4ab-40ec-a320-7e3a9d04afe7 · outbound

This paper cites ghi, Christian G Mayr, Teresa Serrano-Gotarredona, Heidemarie Schmidt, Gwendal Lecerf, Jean Tomas, Julie Grollier, S \.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective ghi, Christian G Mayr, Teresa Serrano-Gotarredona, Heidemarie Schmidt, Gwendal Lecerf, Jean Tomas, Julie Grollier, S \

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.086921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0904a34a-0e8e-472b-a91b-6435ef315897 · outbound

This paper cites Independent component analysis in spiking neurons.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Independent component analysis in spiking neurons

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.072473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 98eb8fcd-8b1a-4239-8453-f09f9960f674 · outbound

This paper cites Schemmel, J.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Schemmel, J

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.056356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dbf8a722-b367-46c8-80dd-ab43771754f1 · outbound

This paper cites u derle, A. Gr\.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective u derle, A. Gr\

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.040299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f385df36-3aa4-48af-ae2e-84074391bca0 · outbound

This paper cites Getting formal with dopamine and reward.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Getting formal with dopamine and reward

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.024414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4cc72dff-6fd4-4753-9ccc-a753434c019c · outbound

This paper cites Spike timing dependent plasticity: a consequence of more fundamental learning rules.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Spike timing dependent plasticity: a consequence of more fundamental learning rules

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:32.008054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 406b9b0e-42be-4ecb-abc2-afcc56ea2d66 · outbound

This paper cites SLAYER: Spike Layer Error Reassignment in Time.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective SLAYER: Spike Layer Error Reassignment in Time

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-14T05:16:30.821284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9dfec58-f2f4-4dee-be52-9405e2c0c5f1 · outbound

This paper cites Fully parallel rram synaptic array for implementing binary neural network with (+ 1,- 1) weights and (+ 1, 0) neurons.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Fully parallel rram synaptic array for implementing binary neural network with (+ 1,- 1) weights and (+ 1, 0) neurons

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.989797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 53bd889b-4890-42c7-a643-9f9928d53477 · outbound

This paper cites Finn: A framework for fast, scalable binarized neural network inference.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Finn: A framework for fast, scalable binarized neural network inference

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.974737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 14a090e0-6e66-4693-ac29-4902a46da079 · outbound

This paper cites Learning by the dendritic prediction of somatic spiking.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Learning by the dendritic prediction of somatic spiking

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.959348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6dde0f13-06fe-4da4-ad7b-d6811fc1d526 · outbound

This paper cites Regularization of neural networks using dropconnect.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Regularization of neural networks using dropconnect

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.944972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 78f0389e-3a26-4043-a6fd-b371cbe09bf7 · outbound

This paper cites Fully memristive neural networks for pattern classification with unsupervised learning.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Fully memristive neural networks for pattern classification with unsupervised learning

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.930425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a6a989ca-11a1-450b-9928-b5d97fef2556 · outbound

This paper cites A learning algorithm for continually running fully recurrent neural networks.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective A learning algorithm for continually running fully recurrent neural networks

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-14T05:16:30.848508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fb1ee6c-e3a5-46c5-a6fd-a89a5d413ef3 · outbound

This paper cites International technology roadmap for semiconductors.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective International technology roadmap for semiconductors

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.906056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d6e978b9-d2be-4025-9fd5-fee9ad4cc8c6 · outbound

This paper cites Resistive memory-based analog synapse: The pursuit for linear and symmetric weight update.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Resistive memory-based analog synapse: The pursuit for linear and symmetric weight update

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.890887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 17060ee1-1aab-4e60-bfd8-861337dd7b1a · outbound

This paper cites Equivalence of backpropagation and contrastive hebbian learning in a layered network.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Equivalence of backpropagation and contrastive hebbian learning in a layered network

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.874394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e2867a27-a9e8-4c4d-afe9-eee8c30f9375 · outbound

This paper cites Voltage fluctuations in neurons: signal or noise? Physiological Reviews, 91 0 (3): 0 917--929, 2011.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Voltage fluctuations in neurons: signal or noise? Physiological Reviews, 91 0 (3): 0 917--929, 2011

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.857017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4c418d2d-e9b7-4810-b342-06198ccb93a2 · outbound

This paper cites Neuro-inspired computing with emerging nonvolatile memorys.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Neuro-inspired computing with emerging nonvolatile memorys

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.842025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7c70a4d7-df5b-438c-9102-42f9770950d1 · outbound

This paper cites Stochastic learning in oxide binary synaptic device for neuromorphic computing.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Stochastic learning in oxide binary synaptic device for neuromorphic computing

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.826726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 24b81df1-530e-4709-a210-4281bcd08e91 · outbound

This paper cites Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

Reference 94

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:16:31.013498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b8769c2-f61f-4d8c-b484-a96c56bca314 · outbound

This paper cites SuperSpike: Supervised learning in multi-layer spiking neural networks.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective SuperSpike: Supervised learning in multi-layer spiking neural networks

Reference 95

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:16:30.990921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 15257247-260e-41bf-9da1-ed91ba89c31d · outbound

This paper cites Characterizing endurance degradation of incremental switching in analog rram for neuromorphic systems.

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective Characterizing endurance degradation of incremental switching in analog rram for neuromorphic systems

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:16:31.810202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Reference resolution

Outbound references

Pith citing papers