Minimizing information loss reduces spiking neuronal networks to differential equations | Paper Citation Record

Observation a8b5336a-4f26-4be1-8127-2806c4a3d703 · outbound

This paper cites Probabilistic decision making by slow reverberation in cortical circuits.

Minimizing information loss reduces spiking neuronal networks to differential equations Probabilistic decision making by slow reverberation in cortical circuits

Reference 1

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Observation ae1951b8-957c-48e0-80f0-181a9a909070 · outbound

This paper cites Large-scale model of mammalian thalamocortical systems.

Minimizing information loss reduces spiking neuronal networks to differential equations Large-scale model of mammalian thalamocortical systems

Reference 2

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Observation 2db4fb44-27be-4d4c-873e-f82de2fc2c4a · outbound

This paper cites A large-scale model of the functioning brain.

Minimizing information loss reduces spiking neuronal networks to differential equations A large-scale model of the functioning brain

Reference 3

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Observation 70f7a7a9-0b0f-4b26-ac10-3ea29cbf8aa0 · outbound

This paper cites The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model.

Minimizing information loss reduces spiking neuronal networks to differential equations The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model

Reference 4

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Observation 8e734785-49a3-4520-98b3-6a7dd7595e22 · outbound

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

Minimizing information loss reduces spiking neuronal networks to differential equations Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Reference 5

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This paper cites Reconstruction and simulation of neocortical microcircuitry.

Minimizing information loss reduces spiking neuronal networks to differential equations Reconstruction and simulation of neocortical microcircuitry

Reference 6

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Observation 4b26885c-75aa-4580-8ae2-3a60e947b7a4 · outbound

This paper cites Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit.

Minimizing information loss reduces spiking neuronal networks to differential equations Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit

Reference 7

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Observation 1c8c35f9-9bd4-492e-9214-abd99f9d3d49 · outbound

This paper cites Orientation selectivity from very sparse LGN inputs in a comprehensive model of macaque V1 cortex.

Minimizing information loss reduces spiking neuronal networks to differential equations Orientation selectivity from very sparse LGN inputs in a comprehensive model of macaque V1 cortex

Reference 8

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Observation 0b12dea9-45a4-4085-8210-e61889a63e5d · outbound

This paper cites Dissecting the synapse-and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples.

Minimizing information loss reduces spiking neuronal networks to differential equations Dissecting the synapse-and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples

Reference 9

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Observation 6dc736c7-5d4e-4105-b03f-5f03e0742efd · outbound

This paper cites A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas.

Minimizing information loss reduces spiking neuronal networks to differential equations A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

Reference 10

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Observation cb525f3d-f604-48b8-92a8-7389effd5012 · outbound

This paper cites Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex.

Minimizing information loss reduces spiking neuronal networks to differential equations Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex

Reference 11

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Observation 5cce1233-91eb-4d30-9364-298e83be6002 · outbound

This paper cites Survey of spiking in the mouse visual system reveals functional hierarchy.

Minimizing information loss reduces spiking neuronal networks to differential equations Survey of spiking in the mouse visual system reveals functional hierarchy

Reference 12

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This paper cites The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks.

Minimizing information loss reduces spiking neuronal networks to differential equations The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks

Reference 13

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Observation 20bd81a5-d784-440e-b4ff-ca588b298be7 · outbound

This paper cites A computational model of direction selectivity in Macaque V1 cortex based on dynamic differences between ON and OFF pathways.

Minimizing information loss reduces spiking neuronal networks to differential equations A computational model of direction selectivity in Macaque V1 cortex based on dynamic differences between ON and OFF pathways

Reference 14

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Observation e85338c8-d8c2-4075-b88e-09504360dec6 · outbound

This paper cites Reading a neural code.

Minimizing information loss reduces spiking neuronal networks to differential equations Reading a neural code

Reference 15

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Observation 14208409-78c1-4891-a622-b19d7ca2412c · outbound

This paper cites Reliability of spike timing in neocortical neurons.

Minimizing information loss reduces spiking neuronal networks to differential equations Reliability of spike timing in neocortical neurons

Reference 16

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This paper cites Primary cortical representation of sounds by the coordination of action-potential timing.

Minimizing information loss reduces spiking neuronal networks to differential equations Primary cortical representation of sounds by the coordination of action-potential timing

Reference 17

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This paper cites Rieke et al.

Minimizing information loss reduces spiking neuronal networks to differential equations Rieke et al

Reference 18

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Observation 8b45dab2-56db-4845-a97c-0d312267bfaf · outbound

This paper cites Neuronal synchrony: a versatile code for the definition of relations?.

Minimizing information loss reduces spiking neuronal networks to differential equations Neuronal synchrony: a versatile code for the definition of relations?

Reference 19

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Observation ec9a9ac7-7674-4b07-8772-bc5b41e74e60 · outbound

This paper cites Regulation of spike timing in visual cortical circuits.

Minimizing information loss reduces spiking neuronal networks to differential equations Regulation of spike timing in visual cortical circuits

Reference 20

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

Minimizing information loss reduces spiking neuronal networks to differential equations Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type

Reference 21

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Observation 0809e451-c2a8-4b24-8dbf-50d7b7e63250 · outbound

This paper cites Competitive Hebbian learning through spike-timing-dependent synaptic plasticity.

Minimizing information loss reduces spiking neuronal networks to differential equations Competitive Hebbian learning through spike-timing-dependent synaptic plasticity

Reference 22

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Observation 192b3f59-8be5-49c8-b8be-a784950ffdb6 · outbound

This paper cites Spike timing-dependent plasticity of neural circuits.

Minimizing information loss reduces spiking neuronal networks to differential equations Spike timing-dependent plasticity of neural circuits

Reference 23

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This paper cites Neuromodulation of spike-timing-dependent plasticity: past, present, and future.

Minimizing information loss reduces spiking neuronal networks to differential equations Neuromodulation of spike-timing-dependent plasticity: past, present, and future

Reference 24

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This paper cites Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits.

Minimizing information loss reduces spiking neuronal networks to differential equations Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Reference 25

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Observation 3d8ab0d1-676e-42b6-b4f8-39f7934fc3be · outbound

This paper cites Integrator or coincidence detector? The role of the cortical neuron revisited.

Minimizing information loss reduces spiking neuronal networks to differential equations Integrator or coincidence detector? The role of the cortical neuron revisited

Reference 26

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This paper cites Computing with neural synchrony.

Minimizing information loss reduces spiking neuronal networks to differential equations Computing with neural synchrony

Reference 27

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This paper cites Neurophysiological and computational principles of cortical rhythms in cognition.

Minimizing information loss reduces spiking neuronal networks to differential equations Neurophysiological and computational principles of cortical rhythms in cognition

Reference 28

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Observation f5f0cf08-3698-4706-a6ac-dd8900c16785 · outbound

This paper cites First spikes in ensembles of human tactile afferents code complex spatial fingertip events.

Minimizing information loss reduces spiking neuronal networks to differential equations First spikes in ensembles of human tactile afferents code complex spatial fingertip events

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.555313Z

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This paper cites Rhythms for cognition: communication through coherence.

Minimizing information loss reduces spiking neuronal networks to differential equations Rhythms for cognition: communication through coherence

Reference 30

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This paper cites Cracking the neural code for sensory perception by combining statistics, intervention, and behavior.

Minimizing information loss reduces spiking neuronal networks to differential equations Cracking the neural code for sensory perception by combining statistics, intervention, and behavior

Reference 31

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This paper cites Gamma and beta bursts underlie working memory.

Minimizing information loss reduces spiking neuronal networks to differential equations Gamma and beta bursts underlie working memory

Reference 32

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This paper cites Neuronal oscillations in cortical networks.

Minimizing information loss reduces spiking neuronal networks to differential equations Neuronal oscillations in cortical networks

Reference 33

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This paper cites Which model to use for cortical spiking neurons?.

Minimizing information loss reduces spiking neuronal networks to differential equations Which model to use for cortical spiking neurons?

Reference 34

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This paper cites Chaos in neuronal networks with balanced excitatory and inhibitory activity.

Minimizing information loss reduces spiking neuronal networks to differential equations Chaos in neuronal networks with balanced excitatory and inhibitory activity

Reference 35

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

Minimizing information loss reduces spiking neuronal networks to differential equations Neuronal dynamics: From single neurons to networks and models of cognition

Reference 36

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This paper cites Chaotic resonance in typical routes to chaos in the Izhikevich neuron model.

Minimizing information loss reduces spiking neuronal networks to differential equations Chaotic resonance in typical routes to chaos in the Izhikevich neuron model

Reference 37

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This paper cites Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.

Minimizing information loss reduces spiking neuronal networks to differential equations Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons

Reference 38

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This paper cites Irregular dynamics in up and down cortical states.

Minimizing information loss reduces spiking neuronal networks to differential equations Irregular dynamics in up and down cortical states

Reference 39

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This paper cites The impact of structural heterogeneity on excitation-inhibition balance in cortical networks.

Minimizing information loss reduces spiking neuronal networks to differential equations The impact of structural heterogeneity on excitation-inhibition balance in cortical networks

Reference 40

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This paper cites A data-informed mean-field approach to mapping of cortical parameter landscapes.

Minimizing information loss reduces spiking neuronal networks to differential equations A data-informed mean-field approach to mapping of cortical parameter landscapes

Reference 41

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This paper cites From spiking neuron models to linear-nonlinear models.

Minimizing information loss reduces spiking neuronal networks to differential equations From spiking neuron models to linear-nonlinear models

Reference 42

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This paper cites Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

Minimizing information loss reduces spiking neuronal networks to differential equations Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

Reference 43

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This paper cites Macroscopic description for networks of spiking neurons.

Minimizing information loss reduces spiking neuronal networks to differential equations Macroscopic description for networks of spiking neurons

Reference 44

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This paper cites Dynamic finite size effects in spiking neural networks.

Minimizing information loss reduces spiking neuronal networks to differential equations Dynamic finite size effects in spiking neural networks

Reference 45

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This paper cites A quantitative description of membrane current and its application to conduction and excitation in nerve.

Minimizing information loss reduces spiking neuronal networks to differential equations A quantitative description of membrane current and its application to conduction and excitation in nerve

Reference 46

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This paper cites Stable propagation of synchronous spiking in cortical neural net- works.

Minimizing information loss reduces spiking neuronal networks to differential equations Stable propagation of synchronous spiking in cortical neural net- works

Reference 47

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This paper cites Desynchronization in diluted neural networks.

Minimizing information loss reduces spiking neuronal networks to differential equations Desynchronization in diluted neural networks

Reference 48

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This paper cites Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex.

Minimizing information loss reduces spiking neuronal networks to differential equations Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.274360Z

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Observation ac8b01f2-f85a-4e84-afb7-f9990ae20435 · outbound

This paper cites The columnar organization of the neocortex.

Minimizing information loss reduces spiking neuronal networks to differential equations The columnar organization of the neocortex

Reference 50

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This paper cites The basic uniformity in structure of the neocortex.

Minimizing information loss reduces spiking neuronal networks to differential equations The basic uniformity in structure of the neocortex

Reference 51

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This paper cites The minicolumn hypothesis in neuroscience.

Minimizing information loss reduces spiking neuronal networks to differential equations The minicolumn hypothesis in neuroscience

Reference 52

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This paper cites Barrel cortex function.

Minimizing information loss reduces spiking neuronal networks to differential equations Barrel cortex function

Reference 53

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Observation 2236e094-b092-4d18-a290-1163ebece551 · outbound

This paper cites Excitatory and inhibitory interactions in localized populations of model neurons.

Minimizing information loss reduces spiking neuronal networks to differential equations Excitatory and inhibitory interactions in localized populations of model neurons

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.206545Z

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Observation 80e902e0-b213-4471-8500-32d30e61d3dc · outbound

This paper cites A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue.

Minimizing information loss reduces spiking neuronal networks to differential equations A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.192678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a997a145-8fa7-4d94-b0d5-ad84296295de · outbound

This paper cites Kinetic theory for neuronal network dynamics.

Minimizing information loss reduces spiking neuronal networks to differential equations Kinetic theory for neuronal network dynamics

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.178976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2d64ef88-9cf5-4457-a7a1-415233cb4d0f · outbound

This paper cites Self-consistent stochastic dynamics for finite-size networks of spiking neurons.

Minimizing information loss reduces spiking neuronal networks to differential equations Self-consistent stochastic dynamics for finite-size networks of spiking neurons

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.165067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8bb3c33b-81d4-4bac-9c53-8964f1f1e19f · outbound

This paper cites Beyond mean field theory: statistical field theory for neural networks.

Minimizing information loss reduces spiking neuronal networks to differential equations Beyond mean field theory: statistical field theory for neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.151226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 27eee162-4e57-469f-b868-1bb37345657a · outbound

This paper cites Biophysics of computations.

Minimizing information loss reduces spiking neuronal networks to differential equations Biophysics of computations

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.137232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 29764757-40a7-49a3-87ee-69ea633d092f · outbound

This paper cites A neuronal network model of macaque primary visual cortex (V1): Orientation selectivity and dynamics in the input layer 4Ca.

Minimizing information loss reduces spiking neuronal networks to differential equations A neuronal network model of macaque primary visual cortex (V1): Orientation selectivity and dynamics in the input layer 4Ca

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.123606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b48628db-7b2d-47a6-8075-ed230d7e6f3c · outbound

This paper cites Pharmacology and nerve-endings.

Minimizing information loss reduces spiking neuronal networks to differential equations Pharmacology and nerve-endings

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.109257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7e007b60-f111-4fca-9299-93637e1958b2 · outbound

This paper cites Principles of neural science.

Minimizing information loss reduces spiking neuronal networks to differential equations Principles of neural science

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.095717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1f6184bc-c4ae-4a25-8568-a75e479cda60 · outbound

This paper cites Fast global oscillations in networks of integrate-and-fire neurons with low firing rates.

Minimizing information loss reduces spiking neuronal networks to differential equations Fast global oscillations in networks of integrate-and-fire neurons with low firing rates

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.081594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1cd89f23-5c0b-4700-8d19-8ec8faafd935 · outbound

This paper cites Field-theoretic approach to fluctuation effects in neural networks.

Minimizing information loss reduces spiking neuronal networks to differential equations Field-theoretic approach to fluctuation effects in neural networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.066976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5817307e-ad75-4932-9530-486a72c10598 · outbound

This paper cites Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds.

Minimizing information loss reduces spiking neuronal networks to differential equations Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.053099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2cedddaf-5160-4539-aa00-6781da0bcb45 · outbound

This paper cites Multi-band oscillations emerge from a simple spiking network.

Minimizing information loss reduces spiking neuronal networks to differential equations Multi-band oscillations emerge from a simple spiking network

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:56:51.617927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e8b521d6-a492-42bb-be2d-91c523f754a2 · outbound

This paper cites Kinetic models of synaptic transmission.

Minimizing information loss reduces spiking neuronal networks to differential equations Kinetic models of synaptic transmission

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.038758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 616c9e99-f741-4880-86d3-978dfc5bd11c · outbound

This paper cites Impact of spontaneous synaptic activity on the resting properties of cat neocortical pyramidal neurons in vivo.

Minimizing information loss reduces spiking neuronal networks to differential equations Impact of spontaneous synaptic activity on the resting properties of cat neocortical pyramidal neurons in vivo

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.024778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 649f5719-30be-4965-b2ab-c7a3a6ffc6ba · outbound

This paper cites Averaging for Markov Chains.

Minimizing information loss reduces spiking neuronal networks to differential equations Averaging for Markov Chains

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:52.010733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9c4352d2-d56a-40ab-98ab-64d90d60c35e · outbound

This paper cites Stochastic neural field model: multiple firing events and correlations.

Minimizing information loss reduces spiking neuronal networks to differential equations Stochastic neural field model: multiple firing events and correlations

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.995866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9c67a77e-db80-49be-a74d-60639cd160e7 · outbound

This paper cites Emergent dynamics in a model of visual cortex.

Minimizing information loss reduces spiking neuronal networks to differential equations Emergent dynamics in a model of visual cortex

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.982526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8479d130-c105-4b47-b8a0-61a78eb4cf0b · outbound

This paper cites Dynamics of multistable states during ongoing and evoked cortical activity.

Minimizing information loss reduces spiking neuronal networks to differential equations Dynamics of multistable states during ongoing and evoked cortical activity

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.968843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eff26b6a-ed5b-47ff-9af1-d93c6b1e69f1 · outbound

This paper cites Synchronization in networks of excitatory and inhibitory neurons with sparse, random connectivity.

Minimizing information loss reduces spiking neuronal networks to differential equations Synchronization in networks of excitatory and inhibitory neurons with sparse, random connectivity

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.954289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 48b32607-d540-4bac-9e44-0ad755c08759 · outbound

This paper cites Firing rate models for gamma oscillations.

Minimizing information loss reduces spiking neuronal networks to differential equations Firing rate models for gamma oscillations

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.938968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dcc245d3-dec3-431d-85c2-162c9348a39f · outbound

This paper cites A master equation formalism for macroscopic modeling of asynchronous irregular activity states.

Minimizing information loss reduces spiking neuronal networks to differential equations A master equation formalism for macroscopic modeling of asynchronous irregular activity states

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.924342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e22b867e-3022-4e29-8683-a9fcf50c9dea · outbound

This paper cites Stochastic neural field theory and the system-size expansion.

Minimizing information loss reduces spiking neuronal networks to differential equations Stochastic neural field theory and the system-size expansion

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.909964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 68855802-1a10-4e6c-acbe-a040e5c97b4f · outbound

This paper cites Impact of network structure and cellular response on spike time correlations.

Minimizing information loss reduces spiking neuronal networks to differential equations Impact of network structure and cellular response on spike time correlations

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.895722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 74d13b5b-a4c8-4800-8f12-c6785a46cabb · outbound

This paper cites Systematic fluctuation expansion for neural network activity equations.

Minimizing information loss reduces spiking neuronal networks to differential equations Systematic fluctuation expansion for neural network activity equations

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.881398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ce4cf7a-ad11-4a39-b01f-119bf4aaf207 · outbound

This paper cites A case study in the functional consequences of scaling the sizes of realistic cortical models.

Minimizing information loss reduces spiking neuronal networks to differential equations A case study in the functional consequences of scaling the sizes of realistic cortical models

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.867257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ad59d6c1-0c7d-41d5-95d2-1998c16cfbe8 · outbound

This paper cites Beyond blow-up in excitatory integrate and fire neuronal networks: refractory period and spontaneous activity.

Minimizing information loss reduces spiking neuronal networks to differential equations Beyond blow-up in excitatory integrate and fire neuronal networks: refractory period and spontaneous activity

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.852718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 47feef4b-0067-4a66-8627-647ecf1f7b74 · outbound

This paper cites How well do reduced models capture the dynamics in models of interacting neurons?.

Minimizing information loss reduces spiking neuronal networks to differential equations How well do reduced models capture the dynamics in models of interacting neurons?

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.838859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3bfef939-cfa6-4538-bdeb-16b6e265591f · outbound

This paper cites Learning spiking neuronal networks with artificial neural networks: neural oscillations.

Minimizing information loss reduces spiking neuronal networks to differential equations Learning spiking neuronal networks with artificial neural networks: neural oscillations

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.825159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 090e49d1-21d1-4996-bbe5-29b6a63eb7f9 · outbound

This paper cites Data-driven discovery of partial differential equations.

Minimizing information loss reduces spiking neuronal networks to differential equations Data-driven discovery of partial differential equations

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.810889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5191f5fc-ce4d-421a-92b4-ae6aec6f633f · outbound

This paper cites Deep learning for universal linear embeddings of nonlinear dynamics.

Minimizing information loss reduces spiking neuronal networks to differential equations Deep learning for universal linear embeddings of nonlinear dynamics

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.796661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9a474228-749b-4b2d-b0a6-77bf89b04384 · outbound

This paper cites A tour of reinforcement learning: The view from continuous control.

Minimizing information loss reduces spiking neuronal networks to differential equations A tour of reinforcement learning: The view from continuous control

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.782345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 61621bbc-6a37-42f9-b1a5-00cea610f0e0 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Minimizing information loss reduces spiking neuronal networks to differential equations Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.767268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5d0bc7d8-1f03-4708-a90b-110ca790598c · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Minimizing information loss reduces spiking neuronal networks to differential equations Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.752337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4c67503d-8e49-42d7-87a8-d01dd1df6471 · outbound

This paper cites Learning Dynamical Systems and Bifurcation via Group Sparsity.

Minimizing information loss reduces spiking neuronal networks to differential equations Learning Dynamical Systems and Bifurcation via Group Sparsity

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-12T14:56:51.522513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdae8290-8b29-4ac2-9356-6b6ae5822abd · outbound

This paper cites Using scientific machine learning for experimental bifurcation analysis of dynamic systems.

Minimizing information loss reduces spiking neuronal networks to differential equations Using scientific machine learning for experimental bifurcation analysis of dynamic systems

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.738346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c45e4d19-dea8-4643-b1a6-4b94a382d09d · outbound

This paper cites Impulses and physiological states in theoretical models of nerve membrane.

Minimizing information loss reduces spiking neuronal networks to differential equations Impulses and physiological states in theoretical models of nerve membrane

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.723933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 84386a48-45dd-4b11-a1a5-135ef8e576cd · outbound

This paper cites The finite state projection algorithm for the solution of the chemical master equation.

Minimizing information loss reduces spiking neuronal networks to differential equations The finite state projection algorithm for the solution of the chemical master equation

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.709368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 793dd382-b848-455c-a33b-ea0c46642bbb · outbound

This paper cites Adaptive discrete Galerkin methods applied to the chemical master equation.

Minimizing information loss reduces spiking neuronal networks to differential equations Adaptive discrete Galerkin methods applied to the chemical master equation

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.692862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a3e92a9c-fdf3-405f-ac6f-0d2b34ae7c1c · outbound

This paper cites A review of the adjoint-state method for computing the gradient of a functional with geophysical applications.

Minimizing information loss reduces spiking neuronal networks to differential equations A review of the adjoint-state method for computing the gradient of a functional with geophysical applications

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.678389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b8a89643-1f9b-4faa-986e-537de1ea4fc7 · outbound

This paper cites Historical development of the Newton–Raphson method.

Minimizing information loss reduces spiking neuronal networks to differential equations Historical development of the Newton–Raphson method

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.663837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b267b9dd-e123-4aff-a53b-da67fe249486 · outbound

This paper cites A quantitative population model of whisker barrels: re-examining the Wilson-Cowan equations.

Minimizing information loss reduces spiking neuronal networks to differential equations A quantitative population model of whisker barrels: re-examining the Wilson-Cowan equations

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.648704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bddaa5cd-74ad-49ac-ba56-5145967a034e · outbound

This paper cites Rhythm and synchrony in a cortical network model.

Minimizing information loss reduces spiking neuronal networks to differential equations Rhythm and synchrony in a cortical network model

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:56:51.633879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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