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Stability of the Memory of Eye Position in a Recurrent Network of Conductance-Based Model Neurons
Open accessAbstract
Studies of the neural correlates of short-term memory in a wide variety of brain areas have found that transient inputs can cause persistent changes in rates of action potential firing, through a mechanism that remains unknown. In a premotor area that is responsible for holding the eyes still during fixation, persistent neural firing encodes the angular position of the eyes in a characteristic manner: below a threshold position the neuron is silent, and above it the firing rate is linearly related to position. Both the threshold and linear slope vary from neuron to neuron. We have reproduced this behavior in a biophysically plausible network model. Persistence depends on precise tuning of the strength of synaptic feedback, and a relatively long synaptic time constant improves the robustness to mistuning.
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“Miller et al. (Miller et al., 2003) showed that fine-tuning of connectivity in this model yields a line attractor capable of parametric working memory, similar to the one in the gaze-control system (Seung et al., 2000).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Seung et al., 2000
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Citations
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Craggs, J. W. (1973) · The Mathematical Gazette · doi:10.2307/3615195 · Cited by 4,167
Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons
Wilson, H. R., & Cowan, J. D. (1972) · Biophysical Journal · doi:10.1016/s0006-3495(72)86068-5 · Cited by 3,579
Computing with Neural Circuits: A Model
Hopfield, J. J., & Tank, D. W. (1986) · Science · doi:10.1126/science.3755256 · Cited by 2,118
Self-citationNonlinear neural networks: Principles, mechanisms, and architectures
Grossberg, S. (1988) · Neural Networks · doi:10.1016/0893-6080(88)90021-4 · Cited by 1,635
Averaging Methods in Nonlinear Dynamical Systems
Sanders, J. A., Verhulst, F., & Murdock, J. A. (2007) · Applied Mathematical Sciences · doi:10.1007/978-0-387-48918-6 · Cited by 1,461
Averaging Methods in Nonlinear Dynamical Systems
Sanders, J. A., & Verhulst, F. (1985) · Applied mathematical sciences · doi:10.1007/978-1-4757-4575-7 · Cited by 1,376
Theory of orientation tuning in visual cortex.
Ben-Yishai, R., Bar‐Or, R. L., & Sompolinsky, H. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.9.3844 · Cited by 1,091
Zhang, K. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-06-02112.1996 · Cited by 1,001
The mechanics of human saccadic eye movement
Robinson, D. A. (1964) · The Journal of Physiology · doi:10.1113/jphysiol.1964.sp007485 · Cited by 893
Research works printed but not linked: 1
- Seung, 2000, The autapse, J. Comput. Neurosci., in press
Cited by
Sorted by citations
Items 201–300 of 338
Combined mechanisms of neural firing rate homeostasis
Miller, P. C., & Cannon, J. J. (2018) · Biological Cybernetics · doi:10.1007/s00422-018-0768-8 · Cited by 15
Sornborger, A. T., Wang, Z., & Tao, L. (2015) · Journal of Computational Neuroscience · doi:10.1007/s10827-015-0570-8 · Cited by 14
Supervised Learning Through Neuronal Response Modulation
Swinehart, C. D., & Abbott, L. F. (2005) · Neural Computation · doi:10.1162/0899766053019980 · Cited by 15
Computation with Spiking Neurons
Wills, S. A. (2004) · Cited by 14
Identifying nonlinear dynamical systems with multiple time scales and long-range dependenciesPreprint
Schmidt, D., Koppe, G., Monfared, Z., Beutelspacher, M., & Durstewitz, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.03471 · Cited by 28
Recurrent Network Models for Perfect Temporal Integration of Fluctuating Correlated Inputs
Okamoto, H., & Fukai, T. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000404 · Cited by 14
Renart, A., Brunel, N., & Wang, X. (2003) · Cited by 14
Neuronal, mathematical, and molecular bases of perceptual decision-making in C. elegans
Tanimoto, Y., & Kimura, K. D. (2018) · Neuroscience Research · doi:10.1016/j.neures.2018.10.010 · Cited by 14
Resonating neurons stabilize heterogeneous grid-cell networks
Mittal, D., & Narayanan, R. (2021) · eLife · doi:10.7554/elife.66804 · Cited by 14
Thrower, L., Dang, W., Jaffe, R. G., Sun, J. D., & Constantinidis, C. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00290.2023 · Cited by 14
Miller, P. C. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00059 · Cited by 12
Relating observability and compressed sensing of time-varying signals in recurrent linear networks
Kafashan, M., Nandi, A., & Ching, S. (2016) · Neural Networks · doi:10.1016/j.neunet.2016.07.007 · Cited by 14
An integrator circuit in cerebellar cortex
Maex, R., & Steuber, V. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12272 · Cited by 12
Modeling Interval Timing by Recurrent Neural Nets
Raphan, T., Dorokhin, E., & Delamater, A. R. (2019) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2019.00046 · Cited by 12
Course 10 Network models of memory
Brunel, N. (2005) · Les Houches/Les Houches · doi:10.1016/s0924-8099(05)80016-2 · Cited by 13
A model-based theory on the signal transformation for microsaccade generation
Inagaki, K., Hirata, Y., & Usui, S. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.06.007 · Cited by 12
Input-Dependent Wave Attenuation in a Critically-Balanced Model of Cortex
Yan, X.-H., & Magnasco, M. O. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041419 · Cited by 11
Saito, Y., Sugimura, T., & Yanagawa, Y. (2017) · eNeuro · doi:10.1523/eneuro.0180-17.2017 · Cited by 11
Miri, A., Bhasin, B. J., Aksay, E. R. F., Tank, D. W., & Goldman, M. S. (2022) · The Journal of Physiology · doi:10.1113/jp282496 · Cited by 9
Self-citationJochems, A., & Yoshida, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123799 · Cited by 12
A Nonlinear Model of the Neural Integrator Improves Detection of Deficits in the Human VOR
Chan, W. W. P., & Galiana, H. L. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2016112 · Cited by 11
Bolton, A. D., & Constantine‐Paton, M. (2018) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2018.00016 · Cited by 11
Alonso, L. M., & Magnasco, M. O. (2018) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5011766 · Cited by 10
A biophysical counting mechanism for keeping time
Zemlianova, K. O., Bose, A., & Rinzel, J. (2022) · Biological Cybernetics · doi:10.1007/s00422-021-00915-4 · Cited by 10
Integrating Philosophy of Understanding With the Cognitive Sciences
Khalifa, K., Islam, F., Gamboa, J. P., Wilkenfeld, D. A., & Kostić, D. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.764708 · Cited by 10
Neural Integrator: A Sandpile Model
Nikitchenko, M., & Koulakov, A. A. (2008) · Neural Computation · doi:10.1162/neco.2008.12-06-416 · Cited by 10
The population dynamics of a canonical cognitive circuitPreprint
Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A., & Fiete, I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/516021 · Cited by 10
Mahrach, A., Bestue, D., Qi, X.-L., Constantinidis, C., & Compte, A. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1225-23.2024 · Cited by 7
Li, F., Chang, C.-H., Basu, A., & Siek, L. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.02.060 · Cited by 9
Subthreshold basis for reward-predictive persistent activity in mouse prefrontal cortex
Kim, E., Bari, B. A., & Cohen, J. Y. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109082 · Cited by 9
Implementation of a Minimal Recurrent Spiking Neural Network in a Solid-State Device
Stoliar, P., Schneegans, O., & Rozenberg, M. J. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.16.034030 · Cited by 9
Context-dependent retrieval of information by neural-network dynamics with continuous attractors
Tsuboshita, Y., & Okamoto, H. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.02.002 · Cited by 9
Connecting Connectomes to Physiology
Borst, A., & Leibold, C. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.2208-22.2023 · Cited by 8
Joshi, P. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.04.015 · Cited by 9
Dimensional reduction for reward-based learning
Swinehart, C. D., & Abbott, L. F. (2006) · Network Computation in Neural Systems · doi:10.1080/09548980600773215 · Cited by 9
Simar, C., Colot, M., Cebolla, A.-M., Petieau, M., Cheron, G. A., & Bontempi, G. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1329411 · Cited by 9
A Novel Hardware-Oriented Recurrent Network of Asynchronous CA Neurons for a Neural Integrator
Takeda, K., & Torikai, H. (2021) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2021.3063932 · Cited by 9
Saito, Y., & Sugimura, T. (2019) · eNeuro · doi:10.1523/eneuro.0364-19.2019 · Cited by 8
Coop, A. D., & Reeke, G. N. (2001) · Journal of Computational Neuroscience · doi:10.1023/a:1011269014373 · Cited by 8
Robustness in spiking networks: a geometric perspectivePreprint
Calaim, N., Dehmelt, F. A., Gonçalves, P. J., & Machens, C. K. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.15.148338 · Cited by 7
Self-Organization of Nonlinearly Coupled Neural Fluctuations Into Synergistic Population Codes
Ma, H., Qi, Y., Gong, P., Zhang, J., Lu, W., & Feng, J. (2023) · Neural Computation · doi:10.1162/neco_a_01612 · Cited by 9
Emergence of time persistence in a data-driven neural network model
Wolf, S., Goc, G. L., Debrégeas, G., Cocco, S., & Monasson, R. (2023) · eLife · doi:10.7554/elife.79541 · Cited by 8
Towards blueprints for network architecture, biophysical dynamics and signal transduction
Coombes, S., Doiron, B., Josić́, K., & Shea‐Brown, E. (2006) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2006.1903 · Cited by 7
A Gaussian Attractor Network for Memory and Recognition with Experience-Dependent Learning
Hu, X., & Zhang, B. (2010) · Neural Computation · doi:10.1162/neco.2010.02-09-957 · Cited by 7
Non-linearity in gaze holding: Experimental results and possible mechanisms
Bertolini, G., Romano, F., Feddermann‐Demont, N., Straumann, D., Tarnutzer, A. A., & Ramat, S. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2019.04.033 · Cited by 7
Blanc, G. (2010) · Cited by 7
Gilra, A., & Gerstner, W. (2017) · arXiv (Cornell University) · Cited by 7
What neuroscience can tell AI about learning in continuously changing environments
Durstewitz, D., Averbeck, B. B., & Koppe, G. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01146-z · Cited by 7
Abrevaya, G., Dumas, G., Aravkin, A. Y., Zheng, P., Gagnon-Audet, J.-C., Kozloski, J., Polosecki, P. I., Lajoie, G., Cox, D., Dawson, S. P., et al. (2021) · Neural Computation · doi:10.1162/neco_a_01401 · Cited by 6
Integrating with Action Potentials
Abbott, L. F. (2000) · Neuron · doi:10.1016/s0896-6273(00)81130-7 · Cited by 6
Inferring Dynamical Systems with Long-Range Dependencies through Line Attractor Regularization.Preprint
Schmidt, D., Koppe, G., Beutelspacher, M., & Durstewitz, D. (2019) · arXiv (Cornell University) · Cited by 6
Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural NetworksPreprint
Durstewitz, D., Koppe, G., & Thurm, M. I. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.31.514408 · Cited by 6
Dynamics of neural motifs realized with a minimal memristive neurosynaptic unit
Wu, J., d’Hollande, A., Du, H., & Rozenberg, M. J. (2025) · Physical Review Applied · doi:10.1103/physrevapplied.23.034030 · Cited by 5
Stability Analysis of Complex Networks with Linear-Threshold Rate Dynamics
Nozari, E., & Cortés, J. (2018) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2018.8431222 · Cited by 5
Extensive Four-Dimensional Chaos in a Mesoscopic Model of the Electroencephalogram
Dafilis, M. P., Frascoli, F., Cadusch, P. J., & Liley, D. T. J. (2015) · The Journal of Mathematical Neuroscience · doi:10.1186/s13408-015-0028-3 · Cited by 5
Subdiffusive Dynamics of Bump Attractors: Mechanisms and Functional Roles
Qi, Y., Breakspear, M. J., & Gong, P. (2014) · Neural Computation · doi:10.1162/neco_a_00698 · Cited by 5
Wang, C., Xiao, Z.-C., Wang, Z., Sornborger, A. T., & Tao, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.00520 · Cited by 6
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory NeuronsPreprint
Li, Q., & Pehlevan, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08115 · Cited by 5
Explicit error coding can mediate gain recalibration in continuous bump attractor networks
Secer, G., Knierim, J. J., & Cowan, N. J. (2025) · Nature Communications · doi:10.1038/s41467-025-63817-0 · Cited by 7
Single-neuron and population measures of neuronal activity in working memory tasks
Mozumder, R., & Constantinidis, C. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00245.2023 · Cited by 4
Belief embodiment through eye movements facilitates memory-guided navigation
Stavropoulos, A., Lakshminarasimhan, K. J., & Angelaki, D. E. (2025) · Nature Communications · doi:10.1038/s41467-025-66080-5 · Cited by 5
Connectivity Matrix Seriation via Relaxation
Borst, A., & Denk, W. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011904 · Cited by 4
Graded information extraction by neural-network dynamics with multihysteretic neurons
Tsuboshita, Y., & Okamoto, H. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.07.005 · Cited by 4
Lectures in mathematical neuroscience
Bressloff, P. C. (2009) · IAS/Park City mathematics series · doi:10.1090/pcms/014/08 · Cited by 5
Reverberatory activity in neuronal networks in vitro
Lau, P.-M., & Bi, G. (2009) · 中国科学通报：英文版 · doi:10.1007/s11434-009-0135-1 · Cited by 4
Qi, X.-L., Liu, R., Singh, B., Bestue, D., Compte, A., Vazdarjanova, A. I., Blake, D. T., & Constantinidis, C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/674465 · Cited by 4
Linearly decodable functions from neural population codes
Westover, M. B., Eliasmith, C., & Anderson, C. H. (2002) · Neurocomputing · doi:10.1016/s0925-2312(02)00459-9 · Cited by 3
Hayton, K., Moirogiannis, D., & Magnasco, M. O. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196566 · Cited by 3
Automated tuning of bifurcations via feedbackPreprint
Moreau, L., Sontag, E. D., & Arcak, M. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.math/0208167 · Cited by 3
Editorial overview: Computational neuroscience
Fairhall, A. L., & Machens, C. K. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.09.009 · Cited by 3
Improving Visual Working Memory with Cholinergic Deep Brain Stimulation
Bava, J., Wang, Z., Bick, S. K. B., Englot, D. J., & Constantinidis, C. (2023) · Brain Sciences · doi:10.3390/brainsci13060917 · Cited by 2
A modeling study on the signal transformation for the microsaccade generation
Inagaki, K., Hirata, Y., & Usui, S. (2010) · BMC Neuroscience · doi:10.1186/1471-2202-11-s1-p115 · Cited by 2
Facilitating the science in computational neuroscience
Graham, L. J. (2000) · Nature Neuroscience · doi:10.1038/81467 · Cited by 2
Neuronal modeling with intracellular calcium signaling.
Shen, R., & Shuai, J. (2011) · PubMed · Cited by 2
Methods for Building Network Models of Neural Circuits
DePasquale, B. (2016) · doi:10.7916/d8w09600 · Cited by 2
Cao, A. S., & Hooser, S. D. V. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.803065 · Cited by 2
The elusive role of normal‐proper function in cognitive science
Egan, F. (2022) · Philosophy and Phenomenological Research · doi:10.1111/phpr.12930 · Cited by 3
Local organization of spatial and shape information in the primate prefrontal cortex
Sun, Y., Dang, W., Jaffe, R. G., & Constantinidis, C. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae384 · Cited by 2
Modeling Working Memory and Decision Making Using Generic Neural Microcircuits
Joshi, P. (2006) · Lecture notes in computer science · doi:10.1007/11840817_54 · Cited by 2
Membrane potential fluctuations in a neural integrator
Zhao, H. (2009) · Cited by 2
Extracting information in a graded manner from a neural-network system with continuous attractors
Tsuboshita, Y., & Okamoto, H. (2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1381166 · Cited by 2
Neural oscillators and integrators in the dynamics of decision tasks
Brown, E. (2004) · Cited by 2
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTMPreprint
Abrevaya, G., Aravkin, A. Y., Cecchi, G., Rish, I., Polosecki, P. I., Zheng, P., & Dawson, S. P. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/330548 · Cited by 2
Recurrent connectivity underlies lateralized temporal processing differences in Auditory CortexPreprint
Neophytou, D., Arribas, D. M., Levy, R. B., Park, M., & Oviedo, H. V. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.14.439872 · Cited by 2
Attractor-like circuits improve visual decoding and behavior in zebrafishPreprint
Privat, M., Hansen, E. C. A., Pietri, T., Marachlian, E., Uribe‐Arias, A., Duchemin, A., Candat, V., Nourin, S., & Sumbre, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.03.578596 · Cited by 2
Molecular control of temporal integration matches decision-making to motivational statePreprint
Gautham, A. K., Miner, L. E., Franco, M. N., Thornquist, S. C., & Crickmore, M. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.01.582988 · Cited by 2
Attractor-based models for sequences and pattern generation in neural circuitsPreprint
Alvarez, J. L., Morrison, K., & Curto, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.07.642121 · Cited by 2
Claudi, F., Chandra, S., & Fiete, I. (2025) · eLife · doi:10.7554/elife.107224.1 · Cited by 1
How neural feedback enables flexible visual processing in Drosophila
Mickels, C. A., & Turner, M. H. (2025) · Trends in Neurosciences · doi:10.1016/j.tins.2025.10.012 · Cited by 1
The performance of mind: from movement, mental states, and consciousness
Cheron, G. A., & Cebolla, A. M. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1736933 · Cited by 1
Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits
Alvarez, J. L., Morrison, K., & Curto, C. (2026) · Neural Computation · doi:10.1162/neco.a.1492 · Cited by 5
Shaping Slow Manifolds in Equivariant Recurrent Neural Networks
Bernardo, A. D., Valente, A., Mastrogiuseppe, F., & Ostojic, S. (2026) · Neural Computation · doi:10.1162/neco.a.1577 · Cited by 5
Course 8 Theory of large recurrent networks: From spikes to behavior
Sompolinsky, H., & White, O. L. (2005) · Les Houches/Les Houches · doi:10.1016/s0924-8099(05)80014-9 · Cited by 1
Making the Grade with Models of Persistent Activity
Dudman, J. T., & Siegelbaum, S. A. (2006) · Neuron · doi:10.1016/j.neuron.2006.02.010 · Cited by 1
The effect of “robust” integrator dynamics on decision-making performance
Cain, N., Barreiro, A. K., Shadlen, M., & Shea‐Brown, E. (2010) · BMC Neuroscience · doi:10.1186/1471-2202-11-s1-p97 · Cited by 1
Okamoto, H., Tsuboshita, Y., & Fukai, T. (2005) · The Brain & Neural Networks · doi:10.3902/jnns.12.235 · Cited by 1
Neuronal Mechanisms for Generation of Microsaccades
Inagaki, K., & Usui, S. (2012) · The Brain & Neural Networks · doi:10.3902/jnns.19.135 · Cited by 1
Time Series from a Nonlinear Dynamical Systems Perspective
Durstewitz, D. (2017) · Bernstein series in computational neuroscience · doi:10.1007/978-3-319-59976-2_9 · Cited by 1
A Bio-Feasible Computational Circuit for Neural Activities Persisting and Decaying
Dai, D., Weihui, & Su, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01421-6_37 · Cited by 1
Wei, H., Jin, X., & Su, Z. (2022) · Brain Sciences · doi:10.3390/brainsci12050547 · Cited by 1
Metadata
- Authors
- Seung, H. S., Lee, D. D., Reis, B. Y., & Tank, D. W.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Seung, H.Sebastian
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2000-04
- Volume
- 26
- Issue
- 1
- Pages
- 259-271
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2052296894
- Cited by
- 339
- References
- 65
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 9 · 2023: 11 · 2022: 12 · 2021: 12 · 2020: 6 · 2019: 13 · 2018: 15 · 2017: 14 · 2016: 11 · 2015: 15 · 2014: 13 · 2013: 14 · 2012: 13
- FWCI
- 9.58
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- short-term memory, eye position, eye fixations, recurrent neural networks, synaptic time constants, persistent firing, premotor neurons, memory stability
- MeSH terms
- Animals, Electric Conductivity, Eye Movements, Goldfish, Membrane Potentials, Memory, Short-Term, Oculomotor Muscles, Oculomotor Nerve, Neural Networks, Computer
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