Uncertainty Quantification in Working Memory via Moment Neural Networks | Paper Citation Record

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This paper cites Probabilistic computation in human perception under variability in encoding precision.

Uncertainty Quantification in Working Memory via Moment Neural Networks Probabilistic computation in human perception under variability in encoding precision

Reference 1

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This paper cites Monkeys and humans take local uncertainty into account when localizing a change.

Uncertainty Quantification in Working Memory via Moment Neural Networks Monkeys and humans take local uncertainty into account when localizing a change

Reference 2

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This paper cites Humans incorporate trial-to-trial working memory uncertainty into rewarded decisions.

Uncertainty Quantification in Working Memory via Moment Neural Networks Humans incorporate trial-to-trial working memory uncertainty into rewarded decisions

Reference 3

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This paper cites Using a betting game to reveal the rich nature of visual working memories.

Uncertainty Quantification in Working Memory via Moment Neural Networks Using a betting game to reveal the rich nature of visual working memories

Reference 4

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This paper cites Uncertainty is maintained and used in working memory.

Uncertainty Quantification in Working Memory via Moment Neural Networks Uncertainty is maintained and used in working memory

Reference 5

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This paper cites A survey of uncertainty in deep neural networks.

Uncertainty Quantification in Working Memory via Moment Neural Networks A survey of uncertainty in deep neural networks

Reference 6

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This paper cites Studying the neural representations of uncertainty.

Uncertainty Quantification in Working Memory via Moment Neural Networks Studying the neural representations of uncertainty

Reference 7

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This paper cites Bridging the gap between explainable ai and uncertainty quantification to enhance trustability.

Uncertainty Quantification in Working Memory via Moment Neural Networks Bridging the gap between explainable ai and uncertainty quantification to enhance trustability

Reference 8

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This paper cites Lightweight probabilistic deep networks.

Uncertainty Quantification in Working Memory via Moment Neural Networks Lightweight probabilistic deep networks

Reference 9

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Observation 9d584281-c677-42bc-967a-ba9144e99421 · outbound

This paper cites Sampling-free epistemic uncertainty estimation using approximated variance propagation.

Uncertainty Quantification in Working Memory via Moment Neural Networks Sampling-free epistemic uncertainty estimation using approximated variance propagation

Reference 10

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This paper cites Neural coding of uncertainty and probability.

Uncertainty Quantification in Working Memory via Moment Neural Networks Neural coding of uncertainty and probability

Reference 11

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Observation bb5ae80b-9b75-4fa4-9c40-4fcda9e3e8da · outbound

This paper cites Bayesian inference with probabilistic population codes.

Uncertainty Quantification in Working Memory via Moment Neural Networks Bayesian inference with probabilistic population codes

Reference 12

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Observation 7ff50631-f6a0-4049-b0a0-f465c1cde771 · outbound

This paper cites Neural variability and sampling-based probabilistic representations in the visual cortex.

Uncertainty Quantification in Working Memory via Moment Neural Networks Neural variability and sampling-based probabilistic representations in the visual cortex

Reference 13

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Observation 6e44525d-1ef4-4f47-8d55-70523fd3cfc7 · outbound

This paper cites Joint representation of working memory and uncertainty in human cortex.

Uncertainty Quantification in Working Memory via Moment Neural Networks Joint representation of working memory and uncertainty in human cortex

Reference 14

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This paper cites Representation and computation in visual working memory.

Uncertainty Quantification in Working Memory via Moment Neural Networks Representation and computation in visual working memory

Reference 15

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This paper cites Benchmarks for models of short-term and working memory.

Uncertainty Quantification in Working Memory via Moment Neural Networks Benchmarks for models of short-term and working memory

Reference 16

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This paper cites Variability in encoding precision accounts for visual short-term memory limitations.

Uncertainty Quantification in Working Memory via Moment Neural Networks Variability in encoding precision accounts for visual short-term memory limitations

Reference 17

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Observation a401757e-d2c9-495d-8a2f-e5809201177c · outbound

This paper cites Introspective judgments predict the precision and likelihood of successful maintenance of visual working memory.

Uncertainty Quantification in Working Memory via Moment Neural Networks Introspective judgments predict the precision and likelihood of successful maintenance of visual working memory

Reference 18

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Observation b8b5cf2e-0090-4609-83a9-9ad9907830c0 · outbound

This paper cites Representation of visual uncertainty through neural gain variability.

Uncertainty Quantification in Working Memory via Moment Neural Networks Representation of visual uncertainty through neural gain variability

Reference 19

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Observation 472ef0f0-456f-4e6b-ba6a-05eaf08f0767 · outbound

This paper cites Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory.

Uncertainty Quantification in Working Memory via Moment Neural Networks Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory

Reference 20

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This paper cites Ring attractor dynamics in the drosophila central brain.

Uncertainty Quantification in Working Memory via Moment Neural Networks Ring attractor dynamics in the drosophila central brain

Reference 21

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This paper cites The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep.Nature neuroscience, 22(9):1512– 1520, 2019.

Uncertainty Quantification in Working Memory via Moment Neural Networks The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep.Nature neuroscience, 22(9):1512– 1520, 2019

Reference 22

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Observation 25547d81-9c2f-4e7e-ab09-d3aa9ca25541 · outbound

This paper cites Attractor and integrator networks in the brain.

Uncertainty Quantification in Working Memory via Moment Neural Networks Attractor and integrator networks in the brain

Reference 23

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Observation 96703ef4-7c0b-4a3e-8b46-d02f4b97b163 · outbound

This paper cites Bayesian inference in ring attractor networks.

Uncertainty Quantification in Working Memory via Moment Neural Networks Bayesian inference in ring attractor networks

Reference 24

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Observation 7fac5d2d-dced-46cf-a1d0-03e8e88d110d · outbound

This paper cites Accurate path integration in continuous attractor network models of grid cells.

Uncertainty Quantification in Working Memory via Moment Neural Networks Accurate path integration in continuous attractor network models of grid cells

Reference 25

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Observation fbd5d9b9-1a23-44d8-aab3-40c1de550cab · outbound

This paper cites Continuous attractor neural networks: candidate of a canonical model for neural information representation.

Uncertainty Quantification in Working Memory via Moment Neural Networks Continuous attractor neural networks: candidate of a canonical model for neural information representation

Reference 26

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Observation 166143a1-5125-4c9b-aaab-f1a30dd51d73 · outbound

This paper cites A modeling framework for deriving the structural and functional architecture of a short-term memory microcircuit.

Uncertainty Quantification in Working Memory via Moment Neural Networks A modeling framework for deriving the structural and functional architecture of a short-term memory microcircuit

Reference 27

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Observation 43b54a6f-da32-4d2a-9979-fc2039082759 · outbound

This paper cites Context-dependent computation by recurrent dynamics in prefrontal cortex.

Uncertainty Quantification in Working Memory via Moment Neural Networks Context-dependent computation by recurrent dynamics in prefrontal cortex

Reference 28

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This paper cites Training excitatory-inhibitory recurrent neural networks for cognitive tasks: a simple and flexible framework.

Uncertainty Quantification in Working Memory via Moment Neural Networks Training excitatory-inhibitory recurrent neural networks for cognitive tasks: a simple and flexible framework

Reference 29

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Observation 62ec8076-2d54-41ef-b8fe-de906c854d91 · outbound

This paper cites A diverse range of factors affect the nature of neural representations underlying short-term memory.

Uncertainty Quantification in Working Memory via Moment Neural Networks A diverse range of factors affect the nature of neural representations underlying short-term memory

Reference 30

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This paper cites Learning to represent continuous variables in heterogeneous neural networks.

Uncertainty Quantification in Working Memory via Moment Neural Networks Learning to represent continuous variables in heterogeneous neural networks

Reference 31

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Observation ebfe72cd-47eb-4c21-ad39-ad22ee51e9d1 · outbound

This paper cites Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference.

Uncertainty Quantification in Working Memory via Moment Neural Networks Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference

Reference 32

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This paper cites On a gaussian neuronal field model.

Uncertainty Quantification in Working Memory via Moment Neural Networks On a gaussian neuronal field model

Reference 33

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This paper cites Self-organization of nonlinearly coupled neural fluctuations into synergistic population codes.

Uncertainty Quantification in Working Memory via Moment Neural Networks Self-organization of nonlinearly coupled neural fluctuations into synergistic population codes

Reference 34

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This paper cites Learning to integrate parts for whole through correlated neural variability.

Uncertainty Quantification in Working Memory via Moment Neural Networks Learning to integrate parts for whole through correlated neural variability

Reference 35

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This paper cites Towards free-response paradigm: a theory on decision-making in spiking neural networks.

Uncertainty Quantification in Working Memory via Moment Neural Networks Towards free-response paradigm: a theory on decision-making in spiking neural networks

Reference 36

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This paper cites Access to information in working memory: exploring the focus of attention.

Uncertainty Quantification in Working Memory via Moment Neural Networks Access to information in working memory: exploring the focus of attention

Reference 37

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Observation f26e3ac8-7fad-44d3-9b59-a0037365f82e · outbound

This paper cites Subdiffusive dynamics of bump attractors: mechanisms and functional roles.

Uncertainty Quantification in Working Memory via Moment Neural Networks Subdiffusive dynamics of bump attractors: mechanisms and functional roles

Reference 38

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This paper cites Fundamental limits on persistent activity in networks of noisy neurons.

Uncertainty Quantification in Working Memory via Moment Neural Networks Fundamental limits on persistent activity in networks of noisy neurons

Reference 39

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This paper cites Variability in the quality of visual working memory.

Uncertainty Quantification in Working Memory via Moment Neural Networks Variability in the quality of visual working memory

Reference 40

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This paper cites Variance as a signature of neural computations during decision making.

Uncertainty Quantification in Working Memory via Moment Neural Networks Variance as a signature of neural computations during decision making

Reference 41

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This paper cites Correlated neural variability in persistent state networks.

Uncertainty Quantification in Working Memory via Moment Neural Networks Correlated neural variability in persistent state networks

Reference 42

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Observation f03ee25f-5529-4eb5-94b7-b59e8e4e1c16 · outbound

This paper cites The correlation structure of local neuronal networks intrinsically results from recurrent dynamics.

Uncertainty Quantification in Working Memory via Moment Neural Networks The correlation structure of local neuronal networks intrinsically results from recurrent dynamics

Reference 43

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Observation 1b94f1a8-8def-4282-8bd0-509212dc5994 · outbound

This paper cites Correlated fluctuations in strongly coupled binary networks beyond equilibrium.

Uncertainty Quantification in Working Memory via Moment Neural Networks Correlated fluctuations in strongly coupled binary networks beyond equilibrium

Reference 44

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Observation 089a0493-5a53-4175-8a82-44b2ee568cd8 · outbound

This paper cites Dynamics of pattern formation in lateral-inhibition type neural fields.

Uncertainty Quantification in Working Memory via Moment Neural Networks Dynamics of pattern formation in lateral-inhibition type neural fields

Reference 45

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This paper cites Waves, bumps, and patterns in neural field theories.

Uncertainty Quantification in Working Memory via Moment Neural Networks Waves, bumps, and patterns in neural field theories

Reference 46

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Observation 42afc28d-3d6d-42bd-a111-d3411a77e7da · outbound

This paper cites Neural fields with sigmoidal firing rates: approximate solutions.

Uncertainty Quantification in Working Memory via Moment Neural Networks Neural fields with sigmoidal firing rates: approximate solutions

Reference 47

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This paper cites Quantitative study of attractor neural network retrieving at low spike rates.

Uncertainty Quantification in Working Memory via Moment Neural Networks Quantitative study of attractor neural network retrieving at low spike rates

Reference 48

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Observation c055f345-f968-4c7b-9ebc-f91a0c9e0dc0 · outbound

This paper cites Dynamics of a recurrent network of spiking neurons before and following learning.

Uncertainty Quantification in Working Memory via Moment Neural Networks Dynamics of a recurrent network of spiking neurons before and following learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:42.048690Z

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Observation 72ed16da-0e6c-409d-907c-99a7d8a9d09f · outbound

This paper cites Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks.

Uncertainty Quantification in Working Memory via Moment Neural Networks Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:42.032452Z

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Observation 95ec0daa-50c9-4208-8e10-0c483444cc33 · outbound

This paper cites Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex.

Uncertainty Quantification in Working Memory via Moment Neural Networks Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:42.016462Z

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Observation 6a2aeb38-6b00-4bc1-9404-0729b4c4db7f · outbound

This paper cites A unifying perspective on neural manifolds and circuits for cognition.

Uncertainty Quantification in Working Memory via Moment Neural Networks A unifying perspective on neural manifolds and circuits for cognition

Reference 52

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Observation 6d3b1f47-2361-4377-a5f2-2fbf332b390c · outbound

This paper cites Optimizing working memory with heterogeneity of recurrent cortical excitation.

Uncertainty Quantification in Working Memory via Moment Neural Networks Optimizing working memory with heterogeneity of recurrent cortical excitation

Reference 53

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Observation 2dbe6b6a-ebd9-45e9-a205-7c24fa3b89d0 · outbound

This paper cites Weight uncertainty in neural network.

Uncertainty Quantification in Working Memory via Moment Neural Networks Weight uncertainty in neural network

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.968700Z

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Observation 438335c5-ad53-4c6f-9f5d-0c4d6ad15e6d · outbound

This paper cites Deterministic variational inference for robust bayesian neural networks.

Uncertainty Quantification in Working Memory via Moment Neural Networks Deterministic variational inference for robust bayesian neural networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.847498Z

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This paper cites Real-time computing without stable states: A new framework for neural computation based on perturbations.

Uncertainty Quantification in Working Memory via Moment Neural Networks Real-time computing without stable states: A new framework for neural computation based on perturbations

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.831842Z

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Observation add37c0f-076e-4013-b50d-429ad8c688b7 · outbound

This paper cites Neural correlates of sparse coding and dimensionality reduction.

Uncertainty Quantification in Working Memory via Moment Neural Networks Neural correlates of sparse coding and dimensionality reduction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.816743Z

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Observation 62df4d6d-f624-4ab7-b8cd-b41f12556f33 · outbound

This paper cites Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment.

Uncertainty Quantification in Working Memory via Moment Neural Networks Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.800587Z

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Observation 61dee447-06dd-4785-b356-018417835f90 · outbound

This paper cites Marginalization in neural circuits with divisive normalization.

Uncertainty Quantification in Working Memory via Moment Neural Networks Marginalization in neural circuits with divisive normalization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.784177Z

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Observation 8aa82709-dfe5-4952-b42d-c2b0b14bec2f · outbound

This paper cites Correlations and neuronal population information.

Uncertainty Quantification in Working Memory via Moment Neural Networks Correlations and neuronal population information

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.766947Z

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Observation d4ede24a-1575-4a40-ae38-fe9b5d8ab9ba · outbound

This paper cites The structures and functions of correlations in neural population codes.

Uncertainty Quantification in Working Memory via Moment Neural Networks The structures and functions of correlations in neural population codes

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.749272Z

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Observation 37c83308-af06-474a-b4af-0dadf25dff80 · outbound

This paper cites Neural correlations, population coding and computa- tion.

Uncertainty Quantification in Working Memory via Moment Neural Networks Neural correlations, population coding and computa- tion

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.732610Z

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Observation c2e074a7-a001-4edc-84dc-827fb863f0e0 · outbound

This paper cites Synaptic plasticity as bayesian inference.

Uncertainty Quantification in Working Memory via Moment Neural Networks Synaptic plasticity as bayesian inference

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.715162Z

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Observation 0da5fe27-a4af-4051-bcd1-a94577c96cf9 · outbound

This paper cites Dynamics of moment neuronal networks.

Uncertainty Quantification in Working Memory via Moment Neural Networks Dynamics of moment neuronal networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.697570Z

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Observation 46b5a360-c2de-4183-9a6c-e6118d3f1b45 · outbound

This paper cites Moment neural network and an efficient numerical method for modeling irregular spiking activity.

Uncertainty Quantification in Working Memory via Moment Neural Networks Moment neural network and an efficient numerical method for modeling irregular spiking activity

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.680276Z

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Observation b6c4d98b-6f00-49ea-a1e7-39649306381d · outbound

This paper cites Backward uniqueness for general parabolic operators in the whole space.

Uncertainty Quantification in Working Memory via Moment Neural Networks Backward uniqueness for general parabolic operators in the whole space

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.661789Z

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Observation 8424ca49-2df1-40ee-b3ab-6852b1bd7378 · outbound

This paper cites Partial differential equations, volume 19.

Uncertainty Quantification in Working Memory via Moment Neural Networks Partial differential equations, volume 19

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:30:41.644174Z

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