The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing | Paper Citation Record

Observation e425e5cb-a990-4ffa-8f69-a3ac53bed0e7 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Imagenet classification with deep convolutional neural networks

Reference 1

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This paper cites Deep learning.Nature, 521(7553):436–444, 2015.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Deep learning.Nature, 521(7553):436–444, 2015

Reference 2

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This paper cites High-performance medicine: the convergence of human and artificial intelligence.Nature Medicine, 25(1):44–56, 2019.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing High-performance medicine: the convergence of human and artificial intelligence.Nature Medicine, 25(1):44–56, 2019

Reference 3

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This paper cites Military applications of artificial intel- ligence: ethical concerns in an uncertain world.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Military applications of artificial intel- ligence: ethical concerns in an uncertain world

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This paper cites An introduction to deep learning for the physical layer.IEEE Transactions on Cognitive Communications and Networking, 3(4):563– 575, 2017.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing An introduction to deep learning for the physical layer.IEEE Transactions on Cognitive Communications and Networking, 3(4):563– 575, 2017

Reference 5

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This paper cites Language mod- els are few-shot learners.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Language mod- els are few-shot learners

Reference 6

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This paper cites GPT-4 Technical Report.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing GPT-4 Technical Report

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This paper cites Scaling Laws for Neural Language Models.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Scaling Laws for Neural Language Models

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This paper cites AI and compute.https://openai.com/index/ai-and-compute/, 2018.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing AI and compute.https://openai.com/index/ai-and-compute/, 2018

Reference 9

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The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Unresolved cited work

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This paper cites Desislavov, F.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Desislavov, F

Reference 11

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This paper cites Data centres and data transmis- sion networks.https://www.iea.org/energy-system/buildings/ data-centres-and-data-transmission-networks#overview, 2023.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Data centres and data transmis- sion networks.https://www.iea.org/energy-system/buildings/ data-centres-and-data-transmission-networks#overview, 2023

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This paper cites Shehabi, S.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Shehabi, S

Reference 13

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Observation 8de8440d-c433-4420-a7e4-7c302289c368 · outbound

This paper cites Stop explaining black box machine learning models for high stakes de- cisions and use interpretable models instead.Nature machine intelligence, 1(5):206– 215, 2019.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Stop explaining black box machine learning models for high stakes de- cisions and use interpretable models instead.Nature machine intelligence, 1(5):206– 215, 2019

Reference 14

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This paper cites Concept bottleneck models.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Concept bottleneck models

Reference 15

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This paper cites Interactive concept bottleneck models.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Interactive concept bottleneck models

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Observation 22f8f5be-a0ae-4f72-acf8-4831c5f9d426 · outbound

This paper cites Towards monosemanticity: Decomposing language models with dictionary learning.Trans- former Circuits Thread, 2023.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Towards monosemanticity: Decomposing language models with dictionary learning.Trans- former Circuits Thread, 2023

Reference 17

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This paper cites Axiomatic attribution for deep networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Axiomatic attribution for deep networks

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This paper cites Towards artificial general intelligence with hybrid Tianjic chip architecture.Nature, 572(7767):106–111, 2019.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Towards artificial general intelligence with hybrid Tianjic chip architecture.Nature, 572(7767):106–111, 2019

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This paper cites TrueNorth: Accelerating from zero to 64 million neurons in 10 years.Computer, 52(5):20–29, 2019.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing TrueNorth: Accelerating from zero to 64 million neurons in 10 years.Computer, 52(5):20–29, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.762290Z

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This paper cites Loihi: a neuromorphic manycore processor with on-chip learning.IEEE Micro, 38(1):82–99, 2018.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Loihi: a neuromorphic manycore processor with on-chip learning.IEEE Micro, 38(1):82–99, 2018

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Observation d8cd94e0-fb3c-4a75-8746-881e6ae1d044 · outbound

This paper cites Incorporating learnable membrane time constant to enhance learn- ing of spiking neural networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Incorporating learnable membrane time constant to enhance learn- ing of spiking neural networks

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This paper cites Brain-inspired learning on neuromorphic substrates.Proceedings of the IEEE, 109(5):935–950, 2021.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Brain-inspired learning on neuromorphic substrates.Proceedings of the IEEE, 109(5):935–950, 2021

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Observation 5af7c7ec-5b8b-454e-b14d-9acff7096ac8 · outbound

This paper cites Spike-driven transformer V2: Meta spiking neural network architecture inspiring the design of next-generation neuromorphic chips.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Spike-driven transformer V2: Meta spiking neural network architecture inspiring the design of next-generation neuromorphic chips

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Observation 70decc1f-2479-46a5-8d11-f4b35814fec9 · outbound

This paper cites Spiking deep convolutional neural networks for energy-efficient object recognition.International Journal of Computer Vision, 113(1):54–66, 2015.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Spiking deep convolutional neural networks for energy-efficient object recognition.International Journal of Computer Vision, 113(1):54–66, 2015

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Observation 7ca3e6e4-d9c8-43bf-a839-53d21073d3a4 · outbound

This paper cites Towards spike-based ma- chine intelligence with neuromorphic computing.Nature, 575(7784):607–617, 2019.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Towards spike-based ma- chine intelligence with neuromorphic computing.Nature, 575(7784):607–617, 2019

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This paper cites Benchmarking energy consumption and latency for neuro- morphic computing in condensed matter and particle physics.APL Machine Learn- ing, 1(1), 2023.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Benchmarking energy consumption and latency for neuro- morphic computing in condensed matter and particle physics.APL Machine Learn- ing, 1(1), 2023

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This paper cites Neuromor- phic principles for efficient large language models on Intel Loihi 2.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Neuromor- phic principles for efficient large language models on Intel Loihi 2

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Observation 01143b0c-a0ac-4a04-a220-a93da9ecf1ce · outbound

This paper cites Energy-efficient neuromorphic computing for edge AI: A framework with adaptive spiking neural networks and hardware-aware optimization.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Energy-efficient neuromorphic computing for edge AI: A framework with adaptive spiking neural networks and hardware-aware optimization

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This paper cites Backpropagation through time: what it does and how to do it.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Backpropagation through time: what it does and how to do it

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This paper cites Efficient training of spiking neural networks with temporally-truncated local back- propagation through time.Frontiers in neuroscience, 17:1047008, 2023.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Efficient training of spiking neural networks with temporally-truncated local back- propagation through time.Frontiers in neuroscience, 17:1047008, 2023

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This paper cites Neftci, Hesham Mostafa, and Friedemann Zenke.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Neftci, Hesham Mostafa, and Friedemann Zenke

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This paper cites The remarkable robustness of surrogate gra- dient learning for instilling complex function in spiking neural networks.Neural Computation, 33(4):899–925, 2021.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing The remarkable robustness of surrogate gra- dient learning for instilling complex function in spiking neural networks.Neural Computation, 33(4):899–925, 2021

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This paper cites Training spik- ing neural networks using lessons from deep learning.Proceedings of the IEEE, 111(9):1016–1054, 2023.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Training spik- ing neural networks using lessons from deep learning.Proceedings of the IEEE, 111(9):1016–1054, 2023

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This paper cites Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks

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This paper cites Deep learning in spiking neural networks.Neural Networks, 111:47–63, 2019.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Deep learning in spiking neural networks.Neural Networks, 111:47–63, 2019

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This paper cites Spatio-temporal back- propagation for training high-performance spiking neural networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Spatio-temporal back- propagation for training high-performance spiking neural networks

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Observation 805093f7-76cb-4278-bbdc-674da34ddbe6 · outbound

This paper cites Feature at- tribution explanations for spiking neural networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Feature at- tribution explanations for spiking neural networks

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Observation 7b69542f-6572-404d-abbf-c0843a76e737 · outbound

This paper cites Gradient-based feature- attribution explainability methods for spiking neural networks.Frontiers in Neu- roscience, 17:1153999, 2023.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Gradient-based feature- attribution explainability methods for spiking neural networks.Frontiers in Neu- roscience, 17:1153999, 2023

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Observation cb54e1c4-c889-4cd8-b250-d84cfe8e54dc · outbound

This paper cites Binary spiking neural net- works as causal models.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Binary spiking neural net- works as causal models

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Observation 4f45beb4-611e-4d06-9b2f-3f7669f786e2 · outbound

This paper cites Deep learning of explainable EEG patterns as dynamic spatiotemporal clusters and rules in a brain-inspired spiking neural network.Sensors, 21(14):4900, 2021.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Deep learning of explainable EEG patterns as dynamic spatiotemporal clusters and rules in a brain-inspired spiking neural network.Sensors, 21(14):4900, 2021

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This paper cites In defense of one-vs-all classification.Journal of machine learning research, 5(Jan):101–141, 2004.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing In defense of one-vs-all classification.Journal of machine learning research, 5(Jan):101–141, 2004

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Observation e94f9fce-b42a-4dba-a274-971ffb4763c1 · outbound

This paper cites One-vs-one classification for deep neural networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing One-vs-one classification for deep neural networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.377659Z

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Observation 4998d019-9994-4067-867f-6d5eec478a70 · outbound

This paper cites Plasticity in inhibitory networks improves pattern separation in early olfactory processing.Communications biology, 8(1):590, 2025.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Plasticity in inhibitory networks improves pattern separation in early olfactory processing.Communications biology, 8(1):590, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.352201Z

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Observation 1f2daf30-0c2e-44c8-923c-c5e5366fcc5e · outbound

This paper cites Synaptic activity and the construction of cortical circuits.Science, 274(5290):1133–1138, 1996.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Synaptic activity and the construction of cortical circuits.Science, 274(5290):1133–1138, 1996

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.331667Z

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Observation aea7624f-b7a7-43cc-b6e5-492a619d21ee · outbound

This paper cites Synapse elimination and indelible memory.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Synapse elimination and indelible memory

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The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Unresolved cited work

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Observation 90f34c04-be1d-4f65-afa5-ae3ff131a454 · outbound

This paper cites an unresolved cited work.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-12T00:15:04.265828Z

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 d135f246-0319-4379-bf35-4e421d59a9bb · outbound

This paper cites Complementary contributions of non-REM and REM sleep to visual learning.Nature neuroscience, 23(9):1150–1156, 2020.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Complementary contributions of non-REM and REM sleep to visual learning.Nature neuroscience, 23(9):1150–1156, 2020

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.249694Z

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 237cb7ad-4f0e-4f34-9c6c-dba9f5b3282c · outbound

This paper cites Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278– 2324, 1998.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278– 2324, 1998

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.229251Z

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 69af2091-45fd-4953-b7d7-0a1c01228cac · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T00:15:03.338281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d7cb0a6-c25c-4653-bf7c-954101d9c880 · outbound

This paper cites Unsupervised learning of digit recognition us- ing spike-timing-dependent plasticity.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Unsupervised learning of digit recognition us- ing spike-timing-dependent plasticity

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.212628Z

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 db527dbb-0df0-4e46-bb45-58dbd35f2c01 · outbound

This paper cites STDP-based spiking deep convolutional neural networks for object recog- nition.Neural Networks, 99:56–67, 2018.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing STDP-based spiking deep convolutional neural networks for object recog- nition.Neural Networks, 99:56–67, 2018

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.199269Z

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 5b856223-6ac2-448c-962c-4b7fa5d3ff64 · outbound

This paper cites Training deep spiking neural networks using backpropagation.Frontiers in Neuroscience, 10:508, 2016.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Training deep spiking neural networks using backpropagation.Frontiers in Neuroscience, 10:508, 2016

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.184472Z

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 d613863b-60a8-4b7f-8dd2-9149eb92965c · outbound

This paper cites A biologically plausible super- vised learning method for spiking neural networks using the symmetric STDP rule.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing A biologically plausible super- vised learning method for spiking neural networks using the symmetric STDP rule

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.162228Z

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 b62b58b8-526d-436e-9feb-5509a2f09093 · outbound

This paper cites LISNN: Improving spiking neural networks with lateral interactions for robust object recognition.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing LISNN: Improving spiking neural networks with lateral interactions for robust object recognition

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.148692Z

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 9f277647-a661-41bd-8440-02a644229e5f · outbound

This paper cites DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T00:15:03.358598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac492de5-1b6d-480f-a7f2-100ec1871db3 · outbound

This paper cites Learning multiple layers of features from tiny images.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Learning multiple layers of features from tiny images

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.133892Z

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 fbe0f8e4-2f1e-4afd-acd1-159dd63df9b2 · outbound

This paper cites Spiking Deep Networks with LIF Neurons.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Spiking Deep Networks with LIF Neurons

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T00:15:03.366174Z

Source-reported events for the cited work

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Observation 31f9dbfe-6fac-440a-97d5-b8fc4f5cd550 · outbound

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

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Direct training for spiking neural networks: Faster, larger, better

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.115233Z

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 937004f7-35ad-4ea7-8621-1307b4e5deca · outbound

This paper cites Enabling deep spiking neural networks with hybrid conversion and spike timing de- pendent backpropagation.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Enabling deep spiking neural networks with hybrid conversion and spike timing de- pendent backpropagation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.092776Z

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 625ea186-1d72-40e0-bd14-323072797958 · outbound

This paper cites Activity pruning for efficient spiking neural networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Activity pruning for efficient spiking neural networks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.078076Z

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 00f5f3f1-7073-4bc8-87d9-647d99b07d3a · outbound

This paper cites Catastrophic forgetting in connectionist networks.Trends in cognitive sciences, 3(4):128–135, 1999.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Catastrophic forgetting in connectionist networks.Trends in cognitive sciences, 3(4):128–135, 1999

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.063899Z

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 c856b82d-84cf-4e5f-ad01-425303a88903 · outbound

This paper cites Replay in deep learning: Current approaches and missing biological elements.Neural Computation, 33(11):2908–2950, 2021.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Replay in deep learning: Current approaches and missing biological elements.Neural Computation, 33(11):2908–2950, 2021

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.043295Z

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 f2051053-d176-44f0-8539-86f3cb622d63 · outbound

This paper cites Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.023333Z

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 e5064592-e53a-4c5a-9740-39ac079e73a3 · outbound

This paper cites Catastrophic interference in connectionist net- works: The sequential learning problem.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Catastrophic interference in connectionist net- works: The sequential learning problem

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:04.008799Z

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 6ce23bec-3a91-432d-82a0-5b8a99feb384 · outbound

This paper cites Biologi- cally inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Biologi- cally inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.993671Z

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 1e8bd915-11e6-4cf2-98e5-02624e5af414 · outbound

This paper cites Toward Lifelong Learning in Equilibrium Propagation: Sleep-like and Awake Rehearsal for Enhanced Stability.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Toward Lifelong Learning in Equilibrium Propagation: Sleep-like and Awake Rehearsal for Enhanced Stability

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-12T00:15:03.406594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86cfffc8-b09e-4d85-a8ad-16f78a2e709e · outbound

This paper cites Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence, 40(12):2935–2947, 2017.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence, 40(12):2935–2947, 2017

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T00:15:03.410821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 818d120a-6a25-4f0e-a192-2653676db51f · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Overcoming catastrophic forgetting in neural networks

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.955908Z

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 66e06117-3fe0-4d68-a41b-920552711e3e · outbound

This paper cites Continual learning through synap- tic intelligence.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Continual learning through synap- tic intelligence

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.943170Z

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 917690d2-8058-4fa7-8fc5-4dc5bfadbd78 · outbound

This paper cites Continual learning of context- dependent processing in neural networks.Nature Machine Intelligence, 1(8):364–372, 2019.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Continual learning of context- dependent processing in neural networks.Nature Machine Intelligence, 1(8):364–372, 2019

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.930783Z

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 21da5997-3590-4810-8dbc-ac59595772d1 · outbound

This paper cites Intrinsic and circuit prop- erties favor coincidence detection for decoding oscillatory input.Journal of Neuro- science, 24(26):6037–6047, 2004.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Intrinsic and circuit prop- erties favor coincidence detection for decoding oscillatory input.Journal of Neuro- science, 24(26):6037–6047, 2004

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.918211Z

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 a60e0f45-7ff7-43c0-a1c3-8b6a54eb97f0 · outbound

This paper cites an unresolved cited work.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-12T00:15:03.900904Z

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 473f20c7-847a-47d0-8ae5-5da718a14a13 · outbound

This paper cites an unresolved cited work.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-12T00:15:03.888454Z

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 da056141-592c-4a75-a110-7b6028ebed5e · outbound

This paper cites Tumkaya, S.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Tumkaya, S

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.876640Z

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 b084887a-ffb7-4bc4-9ed4-2307c977402a · outbound

This paper cites Distributed plasticity for olfactory learning and memory in the honey bee brain.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Distributed plasticity for olfactory learning and memory in the honey bee brain

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.865703Z

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 247de894-a923-45db-9ea1-96ed116101dd · outbound

This paper cites Novelty detection in early olfactory processing of the honey bee, Apis mellifera.Plos one, 17(3):e0265009, 2022.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Novelty detection in early olfactory processing of the honey bee, Apis mellifera.Plos one, 17(3):e0265009, 2022

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.854142Z

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 6694d45f-09a4-46ce-80f2-ff447c53ce3d · outbound

This paper cites Pedigo, Christopher L.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Pedigo, Christopher L

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.843060Z

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 82e86d7b-1728-4305-a376-4b0df3289b5d · outbound

This paper cites The REM sleep-memory consolidation hypothesis.Science, 294(5544):1058–1063, 2001.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing The REM sleep-memory consolidation hypothesis.Science, 294(5544):1058–1063, 2001

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.829166Z

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 b0274a87-b42d-4f5a-8a9a-0396229e92df · outbound

This paper cites an unresolved cited work.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-12T00:15:03.815965Z

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 5a72be72-7605-49e4-9d8f-acbaa9514879 · outbound

This paper cites Sleep- like unsupervised replay reduces catastrophic forgetting in artificial neural networks.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Sleep- like unsupervised replay reduces catastrophic forgetting in artificial neural networks

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.803179Z

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 b80abfe5-ce92-478e-b375-47e16f62ae28 · outbound

This paper cites Improving robustness of convolutional networks through sleep-like replay.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Improving robustness of convolutional networks through sleep-like replay

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.789895Z

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 c2a32fb5-d641-47a5-bfec-09f433b29479 · outbound

This paper cites an unresolved cited work.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-12T00:15:03.774258Z

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 6441b5dc-e860-4705-8104-3532d54e3ec0 · outbound

This paper cites An anatomically constrained model for path integration in the bee brain.Current Biology, 27(20):3069–3085, 2017.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing An anatomically constrained model for path integration in the bee brain.Current Biology, 27(20):3069–3085, 2017

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-12T00:15:03.466384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c79ea07d-5122-495e-8037-278a42ce47fd · outbound

This paper cites CASIA online and offline chinese handwriting databases.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing CASIA online and offline chinese handwriting databases

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.748023Z

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 5d7c3a6b-cb4b-46ec-a83b-bfb49e98b2a1 · outbound

This paper cites Adap- tive mixtures of local experts.Neural Computation, 3(1):79–87, 1991.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Adap- tive mixtures of local experts.Neural Computation, 3(1):79–87, 1991

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.734642Z

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 1eecc811-9560-4694-ad39-6550e2ff20b6 · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.722828Z

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 c200eacd-6fce-4c25-adb0-432fa307006c · outbound

This paper cites An empirical study of catastrophic forgetting in large language models during continual fine-tuning.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing An empirical study of catastrophic forgetting in large language models during continual fine-tuning

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.709604Z

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 b0920d0c-f8ad-498c-987d-06120fb37b44 · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385, 2021.

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing A continual learning survey: Defying forgetting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:15:03.692269Z

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