Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time | Paper Citation Record

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This paper cites write newline.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time write newline

Reference 1

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This paper cites I., Jantan, A., Omolara, A.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time I., Jantan, A., Omolara, A

Reference 2

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This paper cites Discrete Mathematics of Neural Networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Discrete Mathematics of Neural Networks

Reference 3

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This paper cites Understanding deep neural networks with rectified linear units.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Understanding deep neural networks with rectified linear units

Reference 4

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This paper cites and Baraniuk, R.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time and Baraniuk, R

Reference 5

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Unresolved cited work

Reference 6

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This paper cites Optimal approximation with sparsely connected deep neural networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Optimal approximation with sparsely connected deep neural networks

Reference 7

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This paper cites W., Choudhary, A., Agrawal, A., Billinge, S.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time W., Choudhary, A., Agrawal, A., Billinge, S

Reference 8

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This paper cites M., Potempa, K., Versari, L., Fischbacher, T., Gesmundo, A., and Alakuijala, J.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time M., Potempa, K., Versari, L., Fischbacher, T., Gesmundo, A., and Alakuijala, J

Reference 9

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Unresolved cited work

Reference 10

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This paper cites Are SNNs really more energy-efficient than ANNs ? A n in-depth hardware-aware study.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Are SNNs really more energy-efficient than ANNs ? A n in-depth hardware-aware study

Reference 11

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This paper cites K., Ward, M., Neftci, E.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time K., Ward, M., Neftci, E

Reference 12

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This paper cites Incorporating learnable membrane time constant to enhance learning of spiking neural networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Incorporating learnable membrane time constant to enhance learning of spiking neural networks

Reference 13

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

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence

Reference 14

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This paper cites Parallel spiking neurons with high efficiency and ability to learn long-term dependencies.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Parallel spiking neurons with high efficiency and ability to learn long-term dependencies

Reference 15

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This paper cites and van Hemmen, J.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time and van Hemmen, J

Reference 16

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This paper cites M., Naud, R., and Paninski, L.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time M., Naud, R., and Paninski, L

Reference 17

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This paper cites A., Huang, J., Kelber, F., Nazeer, K.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time A., Huang, J., Kelber, F., Nazeer, K

Reference 18

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Unresolved cited work

Reference 19

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This paper cites Error bounds for approximations with deep R e LU neural networks in W^ s,p norms.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Error bounds for approximations with deep R e LU neural networks in W^ s,p norms

Reference 20

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This paper cites Direct learning-based deep spiking neural networks: a review.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Direct learning-based deep spiking neural networks: a review

Reference 21

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This paper cites Fast and energy-efficient neuromorphic deep learning with first-spike times.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Fast and energy-efficient neuromorphic deep learning with first-spike times

Reference 22

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This paper cites Universal function approximation by deep neural nets with bounded width and R e LU activations.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Universal function approximation by deep neural nets with bounded width and R e LU activations

Reference 23

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This paper cites and Rolnick, D.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time and Rolnick, D

Reference 24

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This paper cites and Rolnick, D.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time and Rolnick, D

Reference 25

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This paper cites and Jones, M.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time and Jones, M

Reference 26

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This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 27

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time K., and Wessels, H

Reference 28

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Multilayer feedforward networks are universal approximators

Reference 29

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This paper cites When Deep Learning Meets Polyhedral Theory: A Survey.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time When Deep Learning Meets Polyhedral Theory: A Survey

Reference 30

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time I., Balestriero, R., and Baraniuk, R

Reference 31

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This paper cites Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 32

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Neural networks with linear threshold activations: structure and algorithms

Reference 33

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This paper cites Neural architecture search for spiking neural networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Neural architecture search for spiking neural networks

Reference 34

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Unresolved cited work

Reference 35

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time A theoretical analysis of deep neural networks and parametric pdes

Reference 36

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This paper cites H., Delbruck, T., and Pfeiffer, M.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time H., Delbruck, T., and Pfeiffer, M

Reference 37

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This paper cites An analytical estimation of spiking neural networks energy efficiency.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time An analytical estimation of spiking neural networks energy efficiency

Reference 38

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Y., Pinkus, A., and Schocken, S

Reference 39

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This paper cites The expressive power of neural networks: a view from the width.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time The expressive power of neural networks: a view from the width

Reference 40

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Observation 33cde5e1-5f15-4e28-890b-9772e244e189 · outbound

This paper cites Efficient and Effective Time-Series Forecasting with Spiking Neural Networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Efficient and Effective Time-Series Forecasting with Spiking Neural Networks

Reference 41

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This paper cites On the computational complexity of networks of spiking neurons.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time On the computational complexity of networks of spiking neurons

Reference 42

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This paper cites On the computational power of noisy spiking neurons.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time On the computational power of noisy spiking neurons

Reference 43

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This paper cites Lower bounds for the computational power of networks of spiking neurons.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Lower bounds for the computational power of networks of spiking neurons

Reference 44

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Observation 97c6a959-dcdf-4a7f-91a0-9513825f7f89 · outbound

This paper cites Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:15.606952Z

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Observation 9c26f195-eb89-4aed-8264-9f8d682846ea · outbound

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

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Networks of spiking neurons: The third generation of neural network models

Reference 46

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This paper cites Fast sigmoidal networks via spiking neurons.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Fast sigmoidal networks via spiking neurons

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:15.339264Z

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This paper cites G., Chawla, N., Desoli, G., Malavena, G., Monzio Compagnoni, C., Wang, Z., Yang, J.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time G., Chawla, N., Desoli, G., Malavena, G., Monzio Compagnoni, C., Wang, Z., Yang, J

Reference 48

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This paper cites F., Pascanu, R., Cho, K., and Bengio, Y.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time F., Pascanu, R., Cho, K., and Bengio, Y

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:15.090128Z

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This paper cites Supervised learning based on temporal coding in spiking neural networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Supervised learning based on temporal coding in spiking neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:14.985765Z

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Observation 5e719c75-301f-40be-9ceb-9f0010ba4e32 · outbound

This paper cites O., Mostafa, H., and Zenke, F.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time O., Mostafa, H., and Zenke, F

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:14.878740Z

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Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:52:14.745068Z

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Observation 81f13619-0083-4746-875f-0a09a0dc6a9d · outbound

This paper cites Stable Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Stable Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders

Reference 53

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Observation a9e5d5bb-b3db-474c-b8ec-0d039211488b · outbound

This paper cites P., Rubin, D.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time P., Rubin, D

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:14.544587Z

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Observation 93298882-016a-476c-83cb-ef10ea5293c6 · outbound

This paper cites On the number of inference regions of deep feed forward networks with piece-wise linear activations.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time On the number of inference regions of deep feed forward networks with piece-wise linear activations

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:14.435322Z

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Observation 6f176130-cbad-4f0f-975d-71bc0701b059 · outbound

This paper cites On the Local Complexity of Linear Regions in Deep ReLU Networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time On the Local Complexity of Linear Regions in Deep ReLU Networks

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:52:09.660771Z

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Observation 5a57148b-37d0-470f-8ac7-078e7daf7fbc · outbound

This paper cites and Voigtlaender, F.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time and Voigtlaender, F

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:14.278001Z

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Observation 026c909a-385f-49cb-8812-ff38ed6c62c7 · outbound

This paper cites and Zech, J.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time and Zech, J

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:06.925318Z

Source-reported events for the cited work

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Observation e9b72828-0115-40dd-93bb-bb86d78fd243 · outbound

This paper cites On the expressive power of deep neural networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time On the expressive power of deep neural networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:14.118032Z

Source-reported events for the cited work

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Observation 43e7e9ff-6dd5-4f1e-abe4-f294ab51dcb3 · outbound

This paper cites and Roy, K.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time and Roy, K

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:13.967436Z

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Observation a6571ba5-491a-46a1-86ef-9edd9b16f063 · outbound

This paper cites Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:13.840179Z

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Observation 6dc40b68-90d3-4b16-800a-88508c71950b · outbound

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

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Conversion of continuous-valued deep networks to efficient event-driven networks for image classification

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:13.660223Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6de9f557-e52a-4c39-afb4-f4907dd26c54 · outbound

This paper cites an unresolved cited work.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:52:13.497005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b2531c8-f453-4116-89a3-35c18d248c61 · outbound

This paper cites Bounding and counting linear regions of deep neural networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Bounding and counting linear regions of deep neural networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:13.346114Z

Source-reported events for the cited work

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Observation c3d535b4-b6b4-45a4-80c1-b0599bb0cd88 · outbound

This paper cites Rethinking the membrane dynamics and optimization objectives of spiking neural networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Rethinking the membrane dynamics and optimization objectives of spiking neural networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:13.180636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b69e33fc-a1ff-4284-8f9d-183702c6854a · outbound

This paper cites Deep network approximation characterized by number of neurons.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Deep network approximation characterized by number of neurons

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:13.023803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation de626cef-a188-4cff-9e25-0681aba444f9 · outbound

This paper cites Expressivity of spiking neural networks through the spike response model.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Expressivity of spiking neural networks through the spike response model

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:12.860868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6d68cb51-c9d3-4750-b669-e3229973f8e7 · outbound

This paper cites an unresolved cited work.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:52:12.653511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6855bace-8c66-465c-9260-e26efb99de91 · outbound

This paper cites High-performance deep spiking neural networks with 0.3 spikes per neuron.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time High-performance deep spiking neural networks with 0.3 spikes per neuron

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:12.445477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f41db743-b3b9-4a0e-8ef0-9984a08744e3 · outbound

This paper cites Benefits of depth in neural networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Benefits of depth in neural networks

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:12.281125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b61ded1e-00f7-4ce9-8d29-984f483bbb5c · outbound

This paper cites C., Greenewald, K., Lee, K., and Manso, G.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time C., Greenewald, K., Lee, K., and Manso, G

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:12.135851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a8007c61-98bc-4e4d-8fb1-571858458da8 · outbound

This paper cites Direct training for spiking neural networks: Faster, larger, better.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Direct training for spiking neural networks: Faster, larger, better

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:11.952306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c0fefb09-8d83-4950-9c51-2d64ec8d83a5 · outbound

This paper cites Spiking neural networks and their applications: A review.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Spiking neural networks and their applications: A review

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:11.738668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1dfee50a-0b33-4ccf-8b1d-46f3665d85cd · outbound

This paper cites Error bounds for approximations with deep relu networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Error bounds for approximations with deep relu networks

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:11.485414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dc4c4023-d739-4978-9043-c98ac0651b54 · outbound

This paper cites J., Li, G., Xiao, Z., Jing, Z., Yang, K., Liu, C., Ge, C., Huang, R., and Yang, Y.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time J., Li, G., Xiao, Z., Jing, Z., Yang, K., Liu, C., Ge, C., Huang, R., and Yang, Y

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:11.243435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1f3a18b8-dd1e-4805-8311-49bfd663509c · outbound

This paper cites Facing up to arrangements: Face-count formulas for partitions of space by hyperplanes.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Facing up to arrangements: Face-count formulas for partitions of space by hyperplanes

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:10.992273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9755ab3d-666a-4a5f-b0bb-79db6065fce3 · outbound

This paper cites and Zhou, Z.-H.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time and Zhou, Z.-H

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:10.738248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation be213955-34f7-4396-9040-074ac6ca8439 · outbound

This paper cites On the intrinsic structures of spiking neural networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time On the intrinsic structures of spiking neural networks

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:10.508416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation edf25f51-b223-4d84-8e51-ae5607e7d75f · outbound

This paper cites Universality of deep convolutional neural networks.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Universality of deep convolutional neural networks

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:10.241603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Reference resolution

Outbound references

Pith citing papers