Understanding and controlling the geometry of memory organization in RNNs | Paper Citation Record

Observation 9b867ff8-f8ae-48d1-8910-e80541a9332f · outbound

This paper cites Reconstructing computational dynamics from neural measurements with recurrent neural networks.

Understanding and controlling the geometry of memory organization in RNNs Reconstructing computational dynamics from neural measurements with recurrent neural networks

Reference 1

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This paper cites Inferring brain-wide interactions using data- constrained recurrent neural network models.

Understanding and controlling the geometry of memory organization in RNNs Inferring brain-wide interactions using data- constrained recurrent neural network models

Reference 2

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This paper cites A neural network that finds a naturalistic solution for the production of muscle activ- ity.

Understanding and controlling the geometry of memory organization in RNNs A neural network that finds a naturalistic solution for the production of muscle activ- ity

Reference 3

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This paper cites Extracting computational mechanisms from neural data using low-rank rnns.

Understanding and controlling the geometry of memory organization in RNNs Extracting computational mechanisms from neural data using low-rank rnns

Reference 4

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This paper cites Attractor dynamics gate cortical information flow during decision-making.

Understanding and controlling the geometry of memory organization in RNNs Attractor dynamics gate cortical information flow during decision-making

Reference 5

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

Understanding and controlling the geometry of memory organization in RNNs A unifying perspective on neural manifolds and circuits for cognition

Reference 6

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This paper cites Recurrent neural networks are universal approx- imators.

Understanding and controlling the geometry of memory organization in RNNs Recurrent neural networks are universal approx- imators

Reference 7

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This paper cites Shap- ing dynamics with multiple populations in low-rank re- current networks.

Understanding and controlling the geometry of memory organization in RNNs Shap- ing dynamics with multiple populations in low-rank re- current networks

Reference 8

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

Understanding and controlling the geometry of memory organization in RNNs Context-dependent computation by recurrent dynamics in prefrontal cortex

Reference 9

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This paper cites Backpropagation algo- rithms and reservoir computing in recurrent neural net- works for the forecasting of complex spatiotemporal dy- namics.

Understanding and controlling the geometry of memory organization in RNNs Backpropagation algo- rithms and reservoir computing in recurrent neural net- works for the forecasting of complex spatiotemporal dy- namics

Reference 10

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This paper cites Circuit mech- anisms for the maintenance and manipulation of in- formation in working memory.

Understanding and controlling the geometry of memory organization in RNNs Circuit mech- anisms for the maintenance and manipulation of in- formation in working memory

Reference 11

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Observation 534c3adc-9de6-42a2-a818-0bb0e7bef5d9 · outbound

This paper cites Peeking inside the black-box: a survey on explainable artificial intelli- gence (xai).

Understanding and controlling the geometry of memory organization in RNNs Peeking inside the black-box: a survey on explainable artificial intelli- gence (xai)

Reference 12

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This paper cites Golub, Surya Ganguli, and David Sussillo.

Understanding and controlling the geometry of memory organization in RNNs Golub, Surya Ganguli, and David Sussillo

Reference 13

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Observation 2a3affd1-f462-43a0-8b9a-7a2a87684f09 · outbound

This paper cites Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks.

Understanding and controlling the geometry of memory organization in RNNs Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks

Reference 14

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This paper cites Golub and David Sussillo.

Understanding and controlling the geometry of memory organization in RNNs Golub and David Sussillo

Reference 15

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Observation c8ae22a2-59dc-4b54-a218-62958cdb4654 · outbound

This paper cites Rethinking brain- wide interactions through multi-region ‘network of net- works’ models.

Understanding and controlling the geometry of memory organization in RNNs Rethinking brain- wide interactions through multi-region ‘network of net- works’ models

Reference 16

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Observation 7213b2ee-0bcd-4f80-b832-d0e137f03b70 · outbound

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Understanding and controlling the geometry of memory organization in RNNs Unresolved cited work

Reference 17

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Observation 270637c7-0633-4d8b-9b06-db935b5a7b96 · outbound

This paper cites Alvarez, Asohan Amaras- ingham, Habiba Azab, Zhe S.

Understanding and controlling the geometry of memory organization in RNNs Alvarez, Asohan Amaras- ingham, Habiba Azab, Zhe S

Reference 18

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This paper cites The simplicity bias in multi-task rnns: Shared attractors, reuse of dynamics, and geometric representation.

Understanding and controlling the geometry of memory organization in RNNs The simplicity bias in multi-task rnns: Shared attractors, reuse of dynamics, and geometric representation

Reference 19

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This paper cites Buice, Caswell Barry, Robin Hayman, Neil Burgess, and Ila R.

Understanding and controlling the geometry of memory organization in RNNs Buice, Caswell Barry, Robin Hayman, Neil Burgess, and Ila R

Reference 20

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This paper cites The intrinsic attractor mani- fold and population dynamics of a canonical cognitive circuit across waking and sleep.

Understanding and controlling the geometry of memory organization in RNNs The intrinsic attractor mani- fold and population dynamics of a canonical cognitive circuit across waking and sleep

Reference 21

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This paper cites A coupled attractor model of the rodent head direc- tion system.

Understanding and controlling the geometry of memory organization in RNNs A coupled attractor model of the rodent head direc- tion system

Reference 22

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Observation bfe89103-1e5a-4668-bc70-dad0c2a1b258 · outbound

This paper cites Recurrent network models of sequence generation and memory.

Understanding and controlling the geometry of memory organization in RNNs Recurrent network models of sequence generation and memory

Reference 23

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Observation 92596539-c6fc-449f-a61d-b6f7b137799f · outbound

This paper cites Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings.

Understanding and controlling the geometry of memory organization in RNNs Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings

Reference 24

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Observation 781e1c88-04d7-4d73-8172-0b803676fa97 · outbound

This paper cites Recurrent dynamics of prefrontal cortex during context-dependent decision- making.

Understanding and controlling the geometry of memory organization in RNNs Recurrent dynamics of prefrontal cortex during context-dependent decision- making

Reference 25

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Observation fb519bac-27dd-4c2f-97ba-89fbc1371395 · outbound

This paper cites On the difficulty of learning chaotic dynamics with rnns.

Understanding and controlling the geometry of memory organization in RNNs On the difficulty of learning chaotic dynamics with rnns

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:48.135689Z

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Observation 9cb7b87b-7a0d-460d-a288-fedf3263fa82 · outbound

This paper cites On the difficulty of training recurrent neural networks.

Understanding and controlling the geometry of memory organization in RNNs On the difficulty of training recurrent neural networks

Reference 27

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Observation a06aa2d3-ce38-43e7-b31f-6e76f0d5a42b · outbound

This paper cites Bifurcations in the learning of recurrent neural networks 3.

Understanding and controlling the geometry of memory organization in RNNs Bifurcations in the learning of recurrent neural networks 3

Reference 28

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This paper cites Identifying non- linear dynamical systems with multiple time scales and long-range dependencies.

Understanding and controlling the geometry of memory organization in RNNs Identifying non- linear dynamical systems with multiple time scales and long-range dependencies

Reference 29

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This paper cites Identifying nonlinear dy- namical systems via generative recurrent neural networks with applications to fmri.

Understanding and controlling the geometry of memory organization in RNNs Identifying nonlinear dy- namical systems via generative recurrent neural networks with applications to fmri

Reference 30

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This paper cites The role of population structure in computations through neural dynamics.

Understanding and controlling the geometry of memory organization in RNNs The role of population structure in computations through neural dynamics

Reference 31

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This paper cites Task representations in neural networks trained to perform many cognitive tasks.

Understanding and controlling the geometry of memory organization in RNNs Task representations in neural networks trained to perform many cognitive tasks

Reference 32

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Observation 5e0bbff9-eb1c-4784-9c5e-f0fe015665fc · outbound

This paper cites Bifurcations and loss jumps in RNN training.

Understanding and controlling the geometry of memory organization in RNNs Bifurcations and loss jumps in RNN training

Reference 33

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This paper cites On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks.

Understanding and controlling the geometry of memory organization in RNNs On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks

Reference 34

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This paper cites Monfared and D.

Understanding and controlling the geometry of memory organization in RNNs Monfared and D

Reference 35

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Understanding and controlling the geometry of memory organization in RNNs Unresolved cited work

Reference 36

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This paper cites Generalized Teacher Forcing for Learning Chaotic Dynamics.

Understanding and controlling the geometry of memory organization in RNNs Generalized Teacher Forcing for Learning Chaotic Dynamics

Reference 37

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Observation 719aa72d-cfef-408e-a0f6-ed04fbfabb28 · outbound

This paper cites Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineer- ing.

Understanding and controlling the geometry of memory organization in RNNs Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineer- ing

Reference 38

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This paper cites Preventing gradient explosions in gated recur- rent units.

Understanding and controlling the geometry of memory organization in RNNs Preventing gradient explosions in gated recur- rent units

Reference 39

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Observation 105ffc7e-7c38-4d1e-af0e-33cdfb00fce1 · outbound

This paper cites The effect of the forget gate on bifurcation boundaries and dynamics in re- current neural networks and its implications for gradient- based optimization.

Understanding and controlling the geometry of memory organization in RNNs The effect of the forget gate on bifurcation boundaries and dynamics in re- current neural networks and its implications for gradient- based optimization

Reference 40

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This paper cites Ribeiro, Koen Tiels, Luis A.

Understanding and controlling the geometry of memory organization in RNNs Ribeiro, Koen Tiels, Luis A

Reference 41

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Observation c06d6703-98f4-4a9f-85c5-e279971ebcba · outbound

This paper cites Chaos in random neural networks.

Understanding and controlling the geometry of memory organization in RNNs Chaos in random neural networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.960963Z

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Observation 431b9f5d-ca3c-4ac6-b756-a51b7fa5aa7a · outbound

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

Understanding and controlling the geometry of memory organization in RNNs Real-time computing without stable states: A new framework for neural computation based on per- turbations

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.944684Z

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This paper cites Harnessing nonlinear- ity: Predicting chaotic systems and saving energy in wire- less communication.

Understanding and controlling the geometry of memory organization in RNNs Harnessing nonlinear- ity: Predicting chaotic systems and saving energy in wire- less communication

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.927960Z

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Observation 3a736cd7-a603-4f0e-9d3a-796d40120404 · outbound

This paper cites Generating coherent patterns of activity from chaotic neural networks.

Understanding and controlling the geometry of memory organization in RNNs Generating coherent patterns of activity from chaotic neural networks

Reference 45

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This paper cites full-force: A target- based method for training recurrent networks.

Understanding and controlling the geometry of memory organization in RNNs full-force: A target- based method for training recurrent networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.897520Z

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Observation f922ace7-fe49-4507-91e6-4c40ec5ed2f7 · outbound

This paper cites A ghost mechanism: An analytical model of abrupt learning in recurrent networks.

Understanding and controlling the geometry of memory organization in RNNs A ghost mechanism: An analytical model of abrupt learning in recurrent networks

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-08T13:26:47.321434Z

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Observation c2f58777-83d1-4c79-8944-f0cf439ef22c · outbound

This paper cites A line attractor encoding a persistent internal state requires neuropeptide signal- ing.

Understanding and controlling the geometry of memory organization in RNNs A line attractor encoding a persistent internal state requires neuropeptide signal- ing

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.877834Z

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Observation 5cc2371b-8584-4155-bd47-248665a0e748 · outbound

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

Understanding and controlling the geometry of memory organization in RNNs Ring attractor dynamics in the drosophila central brain

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.861290Z

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Observation 1b203649-a927-4d7f-8378-354628d380cc · outbound

This paper cites Toroidal topology of population activity in grid cells.

Understanding and controlling the geometry of memory organization in RNNs Toroidal topology of population activity in grid cells

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.844785Z

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Observation 2cd6c1e7-2cc1-4208-abbf-e7ec286bd1d2 · outbound

This paper cites Generalized teacher forcing for learning chaotic dynamics.

Understanding and controlling the geometry of memory organization in RNNs Generalized teacher forcing for learning chaotic dynamics

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.827772Z

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Observation b87834bf-e680-4d52-bbb0-70d8ede23f60 · outbound

This paper cites Beyond exploding and vanishing gradi- ents: analysing rnn training using attractors and smooth- ness.

Understanding and controlling the geometry of memory organization in RNNs Beyond exploding and vanishing gradi- ents: analysing rnn training using attractors and smooth- ness

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.810199Z

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Observation 5c3c2b3f-0800-4634-9ff5-58d9ef6d24e1 · outbound

This paper cites Fixedpointfinder: A tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks.

Understanding and controlling the geometry of memory organization in RNNs Fixedpointfinder: A tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T13:26:47.131366Z

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Observation 85a5f104-956f-46e9-bb6d-e33b0a239936 · outbound

This paper cites Automatic differentiation in pytorch.

Understanding and controlling the geometry of memory organization in RNNs Automatic differentiation in pytorch

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.779563Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1aa2a6c6-979b-41bc-a0c3-886d052439e0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Understanding and controlling the geometry of memory organization in RNNs Adam: A Method for Stochastic Optimization

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T13:26:47.140810Z

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Unavailable: canonical work link unavailable.

Observation 6d2609e3-203a-488e-9d79-ef760dd5e288 · outbound

This paper cites Low- dimensional dynamics for working memory and time en- coding.

Understanding and controlling the geometry of memory organization in RNNs Low- dimensional dynamics for working memory and time en- coding

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.762493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b571c9d6-44eb-47c0-a509-845562c5437f · outbound

This paper cites Ramping activity is a cor- tical mechanism of temporal control of action.

Understanding and controlling the geometry of memory organization in RNNs Ramping activity is a cor- tical mechanism of temporal control of action

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.744816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2721e742-758a-434a-8361-972509fd71a4 · outbound

This paper cites Cornn: Convex optimization of recurrent neural networks for rapid inference of neural dynam- ics.

Understanding and controlling the geometry of memory organization in RNNs Cornn: Convex optimization of recurrent neural networks for rapid inference of neural dynam- ics

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T13:26:47.156045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2234883e-900f-4d84-b5c8-2c2371c903a4 · outbound

This paper cites Neural networks and physical systems with emergent collective computational abilities.

Understanding and controlling the geometry of memory organization in RNNs Neural networks and physical systems with emergent collective computational abilities

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T13:26:47.161523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15cc679e-df77-4e23-8775-15950e7326be · outbound

This paper cites Domain adaptive video segmentation via temporal consistency regularization, 2021.

Understanding and controlling the geometry of memory organization in RNNs Domain adaptive video segmentation via temporal consistency regularization, 2021

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.704302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9109d34f-3111-4cc5-bd57-ec3e252ce42b · outbound

This paper cites Preserving semantic and temporal consistency for unpaired video- to-video translation.

Understanding and controlling the geometry of memory organization in RNNs Preserving semantic and temporal consistency for unpaired video- to-video translation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.686683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation df2a72a9-8cc3-4096-b7bb-011174a1c66a · outbound

This paper cites Coherent online video style transfer, 2017.

Understanding and controlling the geometry of memory organization in RNNs Coherent online video style transfer, 2017

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.666619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation db085e67-52d4-47cc-b1f1-94cc9e22fe63 · outbound

This paper cites Real- time neural style transfer for videos.

Understanding and controlling the geometry of memory organization in RNNs Real- time neural style transfer for videos

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.649955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1acf87e9-5cd4-4794-bf73-add3bb41aca3 · outbound

This paper cites Artistic style transfer for videos.

Understanding and controlling the geometry of memory organization in RNNs Artistic style transfer for videos

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.632424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c08f6037-da03-4c88-95ff-0daec465b293 · outbound

This paper cites Finally, most relevantly, [3] has utilized a penalty term that regularized the first-order dynamics in RNNs to incentivize RNNs to find simpler final solutions.

Understanding and controlling the geometry of memory organization in RNNs Finally, most relevantly, [3] has utilized a penalty term that regularized the first-order dynamics in RNNs to incentivize RNNs to find simpler final solutions

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:48.547637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6547da30-3d3a-4066-a646-be7c84ec64f0 · outbound

This paper cites Learning differential equations that are easy to solve.

Understanding and controlling the geometry of memory organization in RNNs Learning differential equations that are easy to solve

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.617169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0a1a2d03-2cf1-49b7-952d-9bbb8d8f12d3 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks.

Understanding and controlling the geometry of memory organization in RNNs Understanding the difficulty of training deep feedforward neural networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.600891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a9a98aaa-7ece-4bf0-bf13-add4ae9382f1 · outbound

This paper cites Chaotic recurrent neural networks for brain modelling: A review.

Understanding and controlling the geometry of memory organization in RNNs Chaotic recurrent neural networks for brain modelling: A review

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.584417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bc8d2dae-7969-4785-a591-c4ce8a01d598 · outbound

This paper cites Robust timing and motor patterns by taming chaos in recurrent neural networks.

Understanding and controlling the geometry of memory organization in RNNs Robust timing and motor patterns by taming chaos in recurrent neural networks

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.568090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 192d8d6c-3a2d-4e7b-bee5-944e904a4d28 · outbound

This paper cites PLRNN dynamical systems equa- tion is as follows.

Understanding and controlling the geometry of memory organization in RNNs PLRNN dynamical systems equa- tion is as follows

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.551589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e2667903-30ad-4fb9-8abb-0dc98164019b · outbound

This paper cites Input to each trial, U = {(u1 1, u2 1), (u1 2, u2 2), ...,(u1 T , u2 T )}, has the shape of T × 2, where T is the trial length.

Understanding and controlling the geometry of memory organization in RNNs Input to each trial, U = {(u1 1, u2 1), (u1 2, u2 2), ...,(u1 T , u2 T )}, has the shape of T × 2, where T is the trial length

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.535903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 11d6ec65-0927-47e3-88fe-c16763ce7021 · outbound

This paper cites an unresolved cited work.

Understanding and controlling the geometry of memory organization in RNNs Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:26:47.519296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d12fcfbe-e6e1-4f70-a564-695e560e2f92 · outbound

This paper cites We first picked a data batch with 100 trials from the test set.

Understanding and controlling the geometry of memory organization in RNNs We first picked a data batch with 100 trials from the test set

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.501474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aa59240a-f1d0-417f-9552-51f175ad9ca2 · outbound

This paper cites To fit the data to PCA, we first fed 100 trials from the test set to the fully- trained PLRNN and obtained the corresponding activa- tions x[t]s.

Understanding and controlling the geometry of memory organization in RNNs To fit the data to PCA, we first fed 100 trials from the test set to the fully- trained PLRNN and obtained the corresponding activa- tions x[t]s

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.482217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b900bdec-2050-4a7e-99c8-084b84d1f0d1 · outbound

This paper cites an unresolved cited work.

Understanding and controlling the geometry of memory organization in RNNs Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:26:47.464284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 45dc88ed-424b-4ff3-ac99-a323abef8b16 · outbound

This paper cites We picked 10 unregularized PLRNNs and 10 PLRNNs trained with MAR and MAI for the delayed addition task from the networks shown in Fig.

Understanding and controlling the geometry of memory organization in RNNs We picked 10 unregularized PLRNNs and 10 PLRNNs trained with MAR and MAI for the delayed addition task from the networks shown in Fig

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.446718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 00afce8d-afcd-41c2-93df-8354981453b5 · outbound

This paper cites To conduct this analysis, we selected 3 representative networks from Fig.

Understanding and controlling the geometry of memory organization in RNNs To conduct this analysis, we selected 3 representative networks from Fig

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.430164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 018cdf5b-a797-40c9-a84f-1ba08affd7cb · outbound

This paper cites epoch b is computed based on the stalling points in the loss function during training.

Understanding and controlling the geometry of memory organization in RNNs epoch b is computed based on the stalling points in the loss function during training

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.412973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 36219841-20af-433b-b1eb-dbfdd26c3f57 · outbound

This paper cites (S11) Here, ri is the activity or firing rate of neuron i and zi is the total input current to neuron i.

Understanding and controlling the geometry of memory organization in RNNs (S11) Here, ri is the activity or firing rate of neuron i and zi is the total input current to neuron i

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:26:47.396975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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