A Modeling Framework for Deriving the Structural and Functional Architecture of a Short-Term Memory Microcircuit

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,904 · see citing works

The Neural Basis of Decision Making

Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764 · see citing works

Organization, development and function of complex brain networks

SPORNS, O., CHIALVO, D., KAISER, M., & HILGETAG, C. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.07.008 · Cited by 2,055 · see citing works

Synaptic theory of working memory

Mongillo, G., Barak, O., & Tsodyks, M. (2008) · Science · doi:10.1126/science.1150769 · Cited by 1,253 · see citing works

Similar network activity from disparate circuit parameters

Prinz, A. A., Bucher, D., & Marder, E. (2004) · Nature Neuroscience · doi:10.1038/nn1352 · Cited by 1,091 · see citing works

Synaptic reverberation underlying mnemonic persistent activity

Wang, X.-J. (2001) · Trends in Neurosciences · doi:10.1016/s0166-2236(00)01868-3 · Cited by 1,059 · see citing works

Encyclopedia of Neuroscience

(2009) · doi:10.1007/978-3-540-29678-2 · Cited by 900 · see citing works

Computational subunits in thin dendrites of pyramidal cells

Polsky, A., Mel, B. W., & Schiller, J. (2004) · Nature Neuroscience · doi:10.1038/nn1253 · Cited by 817 · see citing works

Pyramidal Neuron as Two-Layer Neural Network

Poirazi, P., Brannon, T., & Mel, B. W. (2003) · Neuron · doi:10.1016/s0896-6273(03)00149-1 · Cited by 792 · see citing works

Array tomography: a new tool for imaging the molecular architecture and ultrastructure of neural circuits

Micheva, K. D., & Smith, S. J. (2007) · Neuron · doi:10.1016/j.neuron.2007.06.014 · Cited by 787 · see citing works

Graded persistent activity in entorhinal cortex neurons

Egorov, A. V., Hamam, B. N., Fransén, E., Hasselmo, M. E., & Alonso, A. A. (2002) · Nature · doi:10.1038/nature01171 · Cited by 769 · see citing works

The brainstem control of saccadic eye movements

Sparks, D. L. (2002) · Nature reviews. Neuroscience · doi:10.1038/nrn986 · Cited by 720 · see citing works

Neurocomputational models of working memory

Durstewitz, D., Seamans, J. K., & B, T. J. S. A. (2000) · Nature Neuroscience · doi:10.1038/81460 · Cited by 710 · see citing works

Flexible control of mutual inhibition: a neural model of two-interval discrimination

Machens, C. K., Romo, R., & Brody, C. D. (2005) · Science · doi:10.1126/science.1104171 · Cited by 535 · see citing works

Heterogeneity in the pyramidal network of the medial prefrontal cortex

Wang, Y., Markram, H., Goodman, P. H., Berger, T. K., Ma, J., & Goldman-Rakic, P. S. (2006) · Nature Neuroscience · doi:10.1038/nn1670 · Cited by 503 · see citing works

A role for NMDA-receptor channels in working memory

Lisman, J. E., Fellous, J., & Wang, X. (1998) · Nature Neuroscience · doi:10.1038/1086 · Cited by 438 · see citing works

Global structure, robustness, and modulation of neuronal models

Goldman, M. S., Golowasch, J., Marder, E., & Abbott, L. F. (2001) · The Journal of Neuroscience · doi:10.1523/jneurosci.21-14-05229.2001 · Cited by 371 · see citing works

Memory without Feedback in a Neural Network

Goldman, M. S. (2009) · Neuron · doi:10.1016/j.neuron.2008.12.012 · Cited by 342 · see citing works

Stability of the memory of eye position in a recurrent network of conductance-based model neurons

Seung, H. S., Lee, D. D., Reis, B. Y., & Tank, D. W. (2000) · Neuron · doi:10.1016/s0896-6273(00)81155-1 · Cited by 339 · see citing works

Integrating with Neurons

Robinson, D. A. (1989) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.12.030189.000341 · Cited by 325 · see citing works

A Model of Visuospatial Working Memory in Prefrontal Cortex: Recurrent Network and Cellular Bistability

Camperi, M., & Wang, X. (1998) · Journal of Computational Neuroscience · doi:10.1023/a:1008837311948 · Cited by 323 · see citing works

Persistent neural activity: prevalence and mechanisms

Major, G., & Tank, D. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.10.017 · Cited by 320 · see citing works

Model for a robust neural integrator

Koulakov, A. A., Raghavachari, S., Kepecs, Á., & Lisman, J. E. (2002) · Nature Neuroscience · doi:10.1038/nn893 · Cited by 308 · see citing works

Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations

Brody, C. D., Romo, R., & Kepecs, A. (2003) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(03)00050-3 · Cited by 284 · see citing works

Towards neural circuit reconstruction with volume electron microscopy techniques

Briggman, K. L., & Denk, W. T. (2006) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2006.08.010 · Cited by 266 · see citing works

Functional, But Not Anatomical, Separation of “What” and “When” in Prefrontal Cortex

Machens, C. K., Romo, R., & Brody, C. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.3276-09.2010 · Cited by 260 · see citing works

Robust Spatial Working Memory through Homeostatic Synaptic Scaling in Heterogeneous Cortical Networks

Renart, A., Song, P., & Wang, X. (2003) · Neuron · doi:10.1016/s0896-6273(03)00255-1 · Cited by 243 · see citing works

Spatiotemporally Graded NMDA Spike/Plateau Potentials in Basal Dendrites of Neocortical Pyramidal Neurons

Major, G., Poleg-Polsky, A., Denk, W. T., Schiller, J., & Tank, D. W. (2008) · Journal of Neurophysiology · doi:10.1152/jn.00011.2008 · Cited by 222 · see citing works

In vivo intracellular recording and perturbation of persistent activity in a neural integrator

Aksay, E., Gamkrelidze, G., Seung, H. S., Baker, R., & Tank, D. W. (2001) · Nature Neuroscience · doi:10.1038/84023 · Cited by 201 · see citing works

Compartmentalized and Binary Behavior of Terminal Dendrites in Hippocampal Pyramidal Neurons

Wei, D.-S., Mei, Y., Bagal, A. A., Kao, J. P. Y., Thompson, S. M., & Tang, C. (2001) · Science · doi:10.1126/science.1061198 · Cited by 199 · see citing works

Optogenetic Localization and Genetic Perturbation of Saccade-Generating Neurons in Zebrafish

Schoonheim, P. J., Arrenberg, A. B., Del Bene, F., & Baier, H. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5193-09.2010 · Cited by 185 · see citing works

A proposed neural network for the integrator of the oculomotor system

Cannon, S., Robinson, D. A., & Shamma, S. (1983) · Biological cybernetics · doi:10.1007/bf00320393 · Cited by 173 · see citing works

Semi-automated reconstruction of neural circuits using electron microscopy

Chklovskii, D. B., Vitaladevuni, S. N. P., & Scheffer, L. K. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.08.002 · Cited by 173 · see citing works

Robust Persistent Neural Activity in a Model Integrator with Multiple Hysteretic Dendrites per Neuron

Goldman, M. S. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg095 · Cited by 169 · see citing works

Angular Path Integration by Moving “Hill of Activity”: A Spiking Neuron Model without Recurrent Excitation of the Head-Direction System

Song, P., & Wang, X. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.4172-04.2005 · Cited by 161 · see citing works

Machines that learn to segment images: a crucial technology for connectomics

Jain, V., Seung, H. S., & Turaga, S. C. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.07.004 · Cited by 140 · see citing works

Spatial gradients and multidimensional dynamics in a neural integrator circuit

Miri, A., Daie, K., Arrenberg, A. B., Baier, H., Aksay, E., & Tank, D. W. (2011) · Nature Neuroscience · doi:10.1038/nn.2888 · Cited by 138 · see citing works

Neural engineering

Eliasmith, C. (2002) · Cited by 134 · see citing works

Functional dissection of circuitry in a neural integrator

Aksay, E., Olasagasti, I., Mensh, B. D., Baker, R., Goldman, M. S., & Tank, D. W. (2007) · Nature Neuroscience · doi:10.1038/nn1877 · Cited by 134 · see citing works

Anatomy and Discharge Properties of Pre-Motor Neurons in the Goldfish Medulla That Have Eye-Position Signals During Fixations

Aksay, E., Baker, R., Seung, H. S., & Tank, D. W. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.84.2.1035 · Cited by 119 · see citing works

Neural Integrator Models

Goldman, M. S., Compte, A., & Wang, X.-J. (2009) · Elsevier eBooks · doi:10.1016/b978-008045046-9.01434-0 · Cited by 77 · see citing works

Neural integration by short term potentiation

Shen, L. (1989) · Biological Cybernetics · doi:10.1007/bf00203180 · Cited by 61 · see citing works

Characterization and adaptive modification of the goldfish vestibuloocular reflex by sinusoidal and velocity step vestibular stimulation

Pastor, Á. M., de la Cruz, R. R., & Baker, R. G. V. (1992) · Journal of Neurophysiology · doi:10.1152/jn.1992.68.6.2003 · Cited by 59 · see citing works

Detection Sensitivity and Temporal Resolution of Visual Signals near Absolute Threshold in the Salamander Retina

Chichilnisky, E. J., & Rieke, F. M. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.2339-04.2005 · Cited by 48 · see citing works

Correlated Discharge among Cell Pairs within the Oculomotor Horizontal Velocity-to-Position Integrator

Aksay, E., Baker, R., Seung, H. S., & Tank, D. W. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-34-10852.2003 · Cited by 45 · see citing works

Design of Continuous Attractor Networks with Monotonic Tuning Using a Symmetry Principle

Machens, C. K., & Brody, C. D. (2007) · Neural Computation · doi:10.1162/neco.2007.07-06-297 · Cited by 37 · see citing works

Encoding of Eye Position in the Goldfish Horizontal Oculomotor Neural Integrator

Debowy, O., & Baker, R. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00313.2010 · Cited by 29 · see citing works

An intracellular Ca2+ subsystem as a biologically plausible source of intrinsic conditional bistability in a network model of working memory

Fall, C. P., & Rinzel, J. (2006) · Journal of Computational Neuroscience · doi:10.1007/s10827-006-4791-8 · Cited by 23 · see citing works

Neural network simulations of the primate oculomotor system IV. A distributed bilateral stochastic model of the neural integrator of the vertical saccadic system

Sklavos, S., & Moschovakis, A. K. (2002) · Biological Cybernetics · doi:10.1007/s004220100281 · Cited by 16 · see citing works

Computational Exploration of Neuron and Neural Network Models in Neurobiology

Prinz, A. A. (2007) · Methods in molecular biology · doi:10.1007/978-1-59745-520-6_10 · Cited by 14 · see citing works

Robust neuronal dynamics in premotor cortex during motor planning

Li, N., Daie, K., Svoboda, K., & Druckmann, S. (2016) · Nature · doi:10.1038/nature17643 · Cited by 492 · see citing works

Signal propagation in complex networks

Ji, P., Ye, J., Mu, Y., Lin, W. J., Tian, Y., Hens, C., Perc, M., Tang, Y. S., Sun, J., & Kurths, J. (2023) · Physics Reports · doi:10.1016/j.physrep.2023.03.005 · Cited by 369 · see citing works

Recurrent network models of sequence generation and memory

Rajan, K., Harvey, C. D., & Tank, D. W. (2016) · Neuron · doi:10.1016/j.neuron.2016.02.009 · Cited by 351 · see citing works

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response

Naumann, E. A., Fitzgerald, J. E., Dunn, T. W., Rihel, J., Sompolinsky, H., & Engert, F. (2016) · Cell · doi:10.1016/j.cell.2016.10.019 · Cited by 315 · see citing works

Deep neural networks in psychiatry

Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 287 · see citing works

Training deep neural density estimators to identify mechanistic models of neural dynamics

Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., Chintaluri, C., Podlaski, W. F., Haddad, S. A., Vogels, T. P., et al. (2020) · eLife · doi:10.7554/elife.56261 · Cited by 252 · see citing works

Building functional networks of spiking model neurons

Abbott, L. F., DePasquale, B., & Memmesheimer, R.-M. (2016) · Nature Neuroscience · doi:10.1038/nn.4241 · Cited by 199 · see citing works

Neural circuits for evidence accumulation and decision making in larval zebrafish

Bahl, A., & Engert, F. (2020) · Nature Neuroscience · doi:10.1038/s41593-019-0534-9 · Cited by 166 · see citing works

NetPyNE, a tool for data-driven multiscale modeling of brain circuits

Dura-Bernal, S., Suter, B. A., Gleeson, P., Cantarelli, M., Quintana, A., Rodriguez, F., Kedziora, D. J., Chadderdon, G. L., Kerr, C. C., Neymotin, S. A., et al. (2019) · eLife · doi:10.7554/elife.44494 · Cited by 169 · see citing works

full-FORCE: A target-based method for training recurrent networks

DePasquale, B., Cueva, C. J., Rajan, K., Escola, G. S., & Abbott, L. F. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191527 · Cited by 120 · see citing works

Targeted photostimulation uncovers circuit motifs supporting short-term memory

Daie, K., Svoboda, K., & Druckmann, S. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00776-3 · Cited by 100 · see citing works

A neural manifold view of the brain

Perich, M. G., Narain, D., & Gallego, J. Á. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02031-z · Cited by 105 · see citing works

The Challenge of Understanding the Brain: Where We Stand in 2015

Lisman, J. E. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.032 · Cited by 82 · see citing works

Psychophysical reverse correlation reflects both sensory and decision-making processes

Okazawa, G., Sha, L., Purcell, B. A., & Kiani, R. (2018) · Nature Communications · doi:10.1038/s41467-018-05797-y · Cited by 83 · see citing works

Computational models in the age of large datasets

O’Leary, T., Sutton, A. C., & Marder, E. E. (2015) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.01.006 · Cited by 80 · see citing works

Neural Algorithms and Circuits for Motor Planning

Inagaki, H., Chen, S., Daie, K., Finkelstein, A., Fontolan, L., Romani, S., & Svoboda, K. (2022) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092021-121730 · Cited by 80 · see citing works

Rethinking brain-wide interactions through multi-region ‘network of networks’ models

Perich, M. G., & Rajan, K. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.11.003 · Cited by 75 · see citing works

Beyond excitation/inhibition imbalance in multidimensional models of neural circuit changes in brain disorders

O’Donnell, C., Gonçalves, J. T., Portera‐Cailliau, C., & B, T. J. S. A. (2017) · eLife · doi:10.7554/elife.26724 · Cited by 69 · see citing works

Electron Microscopic Reconstruction of Functionally Identified Cells in a Neural Integrator

Vishwanathan, A., Daie, K., Ramirez, A. D., Lichtman, J. W., Aksay, E. R. F., & Seung, H. S. (2017) · Current Biology · doi:10.1016/j.cub.2017.06.028 · Cited by 74 · see citing works

Inferring brain-wide interactions using data-constrained recurrent neural network models

Perich, M. G., Arlt, C., Soares, S., Zhou, S., Beirán, M., Andalman, A. S., Benster, T., Young, M. E., Mosher, C. P., Minxha, J., et al. (2026) · Neuron · doi:10.1016/j.neuron.2026.07.016 · Cited by 63 · see citing works

Spatial Patterns of Persistent Neural Activity Vary with the Behavioral Context of Short-Term Memory

Daie, K., Goldman, M. S., & Aksay, E. R. F. (2015) · Neuron · doi:10.1016/j.neuron.2015.01.006 · Cited by 42 · see citing works

A tale of two species: Neural integration in zebrafish and monkeys

Joshua, M., & Lisberger, S. G. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2014.04.048 · Cited by 47 · see citing works

Collicular circuits for flexible sensorimotor routing

Duan, C. A., Pagan, M., Piet, A. T., Kopec, C. D., Akrami, A., Riordan, A. J., Erlich, J. C., & Brody, C. D. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00865-x · Cited by 42 · see citing works

Learning to represent continuous variables in heterogeneous neural networks

Darshan, R., & Rivkind, A. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110612 · Cited by 37 · see citing works

Parameter estimation and identifiability in a neural population model for electro-cortical activity

Hartoyo, A., Cadusch, P. J., Liley, D. T. J., & Hicks, D. G. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006694 · Cited by 43 · see citing works

Poisson balanced spiking networks

Buxó, C. E. R., & Pillow, J. W. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008261 · Cited by 40 · see citing works

Cell Assembly Signatures Defined by Short-Term Synaptic Plasticity in Cortical Networks

Carrillo‐Reid, L., López-Huerta, V. G., García-Muñoz, M., Theiss, S., & Arbuthnott, G. W. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500264 · Cited by 33 · see citing works

Using Firing-Rate Dynamics to Train Recurrent Networks of Spiking Model NeuronsPreprint

DePasquale, B., Churchland, M. M., & Abbott, L. F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.07620 · Cited by 35 · see citing works

Understanding Brains: Details, Intuition, and Big Data

Marder, E. E. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002147 · Cited by 32 · see citing works

Accurate action potential inference from a calcium sensor protein through biophysical modeling:Preprint

Greenberg, D. S., Wallace, D. J., Voit, K.-M., Wuertenberger, S., Czubayko, U., Monsees, A., Handa, T., Vogelstein, J. T., Seifert, R., Groemping, Y., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/479055 · Cited by 34 · see citing works

A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity

Haß, J., Hertäg, L., & Durstewitz, D. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004930 · Cited by 31 · see citing works

Predicting how and when hidden neurons skew measured synaptic interactions

Brinkman, B. A. W., Rieke, F. M., Shea‐Brown, E., & Buice, M. A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006490 · Cited by 30 · see citing works

Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution

Lappalainen, J. K., Tschopp, F. D., Prakhya, S., McGill, M., Nern, A., Shinomiya, K., Takemura, S.-Y., Gruntman, E., Macke, J. H., & Turaga, S. C. (2023) · doi:10.1101/2023.03.11.532232 · Cited by 31 · see citing works

Structure and function in artificial, zebrafish and human neural networks

Ji, P., Wang, Y., Peron, T., Li, C., Nagler, J., & Du, J. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.04.004 · Cited by 27 · see citing works

A Structural and Genotypic Scaffold Underlying Temporal Integration

Lee, M. M., Arrenberg, A. B., & Aksay, E. R. F. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.3045-14.2015 · Cited by 27 · see citing works

Functional architecture underlying binocular coordination of eye position and velocity in the larval zebrafish hindbrain

Brysch, C., Leyden, C., & Arrenberg, A. B. (2019) · BMC Biology · doi:10.1186/s12915-019-0720-y · Cited by 26 · see citing works

Predicting modular functions and neural coding of behavior from a synaptic wiring diagram

Vishwanathan, A., Sood, A., Wu, J., Ramirez, A. D., Yang, R., Kemnitz, N., Ih, D., Turner, N., Lee, K., Tartavull, I., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01784-3 · Cited by 25 · see citing works

Geometric framework to predict structure from function in neural networks

Tirthabir Biswas, A., & Fitzgerald, J. E. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.023255 · Cited by 22 · see citing works

Interrogating theoretical models of neural computation with emergent property inference

Bittner, S. R., Palmigiano, A., Piet, A. T., Duan, C. A., Brody, C. D., Miller, K. D., & Cunningham, J. P. (2021) · eLife · doi:10.7554/elife.56265 · Cited by 24 · see citing works

Computational models as statistical tools

Durstewitz, D., Koppe, G., & Toutounji, H. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.07.004 · Cited by 19 · see citing works

Distributing task-related neural activity across a cortical network through task-independent connections

Kim, C. M., Finkelstein, A., Chow, C. C., Svoboda, K., & Darshan, R. (2023) · Nature Communications · doi:10.1038/s41467-023-38529-y · Cited by 18 · see citing works

Prediction of neural activity in connectome-constrained recurrent networks

Beirán, M., & Litwin-Kumar, A. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02080-4 · Cited by 16 · see citing works

Fixational drift is driven by diffusive dynamics in central neural circuitry

Ben-Shushan, N., Shaham, N., Joshua, M., & Burak, Y. (2022) · Nature Communications · doi:10.1038/s41467-022-29201-y · Cited by 16 · see citing works

Molecular diversity of clustered protocadherin-α required for sensory integration and short-term memory in mice

Yamagishi, T., Yoshitake, K., Kamatani, D., Watanabe, K., Tsukano, H., Hishida, R., Takahashi, K., Takahashi, S., Horii, A., Yagi, T., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-28034-4 · Cited by 16 · see citing works

Predicting modular functions and neural coding of behavior from a synaptic wiring diagramPreprint

Vishwanathan, A., Ramirez, A. D., Wu, J., Sood, A., Yang, R., Kemnitz, N., Ih, D., Turner, N. L., Lee, K., Tartavull, I., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.28.359620 · Cited by 14 · see citing works

Interrogating theoretical models of neural computation with emergent property inferencePreprint

Bittner, S. R., Palmigiano, A., Piet, A. T., Duan, C. A., Brody, C. D., Miller, K. D., & Cunningham, J. P. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/837567 · Cited by 13 · see citing works

A multi-region recurrent circuit for evidence accumulation in rats

Gupta, D., Kopec, C. D., Bondy, A., Luo, T. Z., Elliott, V. A., & Brody, C. D. (2026) · Neuron · doi:10.1016/j.neuron.2025.12.029 · Cited by 12 · see citing works

Cyclic structure with cellular precision in a vertebrate sensorimotor neural circuit

Yang, R., Vishwanathan, A., Wu, J., Kemnitz, N., Ih, D., Turner, N., Lee, K., Tartavull, I., Silversmith, W. M., Jordan, C. S., et al. (2023) · Current Biology · doi:10.1016/j.cub.2023.05.010 · Cited by 11 · see citing works

A scalable implementation of the recursive least-squares algorithm for training spiking neural networks

Arthur, B. J., Kim, C. M., Chen, S., Preibisch, S., & Darshan, R. (2023) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2023.1099510 · Cited by 11 · see citing works

Symmetries and Continuous Attractors in Disordered Neural CircuitsPreprint

Clark, D. G., Abbott, L. F., & Sompolinsky, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.26.634933 · Cited by 10 · see citing works

Visuomotor decision-making through multifeature convergence in the larval zebrafish hindbrain

Slangewal, K., Aimon, S., Capelle, M. Q., Kämpf, F., Naumann, H., Slanchev, K., Baier, H., & Bahl, A. (2026) · Nature Communications · doi:10.1038/s41467-026-69633-4 · Cited by 12 · see citing works

Neural circuit models for evidence accumulation through choice-selective sequences

Brown, L. S., Cho, J. R., Bolkan, S. S., Nieh, E. H., Schottdorf, M., Tank, D. W., Brody, C. D., Witten, I. B., & Goldman, M. S. (2026) · Nature Communications · doi:10.1038/s41467-026-70267-9 · Cited by 8 · see citing works

Evolutionarily conserved brainstem architecture enables gravity-guided vertical navigation

Zhu, Y., Gelnaw, H., Auer, F., Hamling, K. R., Ehrlich, D., & Schoppik, D. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002902 · Cited by 8 · see citing works

Interactions between circuit architecture and plasticity in a closed-loop cerebellar system

Payne, H. L., Raymond, J. L., & Goldman, M. S. (2024) · eLife · doi:10.7554/elife.84770 · Cited by 8 · see citing works

A brainstem circuit for gravity-guided vertical navigationPreprint

Zhu, Y., Gelnaw, H., Auer, F., Hamling, K. R., Ehrlich, D., & Schoppik, D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.12.584680 · Cited by 7 · see citing works

Multineuronal activity patterns identify selective synaptic connections under realistic experimental constraints

Brendan, C., & MacLean, J. N. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00429.2015 · Cited by 6 · see citing works

Dynamic control of cortical head-direction signal by angular velocityPreprint

Finkelstein, A., Rouault, H., Romani, S., & Ulanovsky, N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/730374 · Cited by 6 · see citing works

Spiking Neural Networks with Laterally-Inhibited Self-Recurrent Units

Zhang, W., & Li, P. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533726 · Cited by 5 · see citing works

Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems

Jha, A., Gupta, D., Brody, C. D., & Pillow, J. W. (2024) · neural information processing systems · doi:10.52202/079017-1060 · Cited by 5 · see citing works

Harnessing the potential of human induced pluripotent stem cells, functional assays and machine learning for neurodevelopmental disorders

Yang, Z., Teaney, N. A., Buttermore, E. D., Şahin, M., & Saber, W. A. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1524577 · Cited by 4 · see citing works

Theoretical principles for illuminating sensorimotor processing with brain-wide neuronal recordings

Tirthabir Biswas, A., Bishop, W. E., & Fitzgerald, J. E. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.10.021 · Cited by 4 · see citing works

Distributing task-related neural activity across a cortical network through task-independent connectionsPreprint

Kim, C. M., Finkelstein, A., Chow, C. C., Svoboda, K., & Darshan, R. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.17.496618 · Cited by 3 · see citing works

Methods for Building Network Models of Neural Circuits

DePasquale, B. (2016) · doi:10.7916/d8w09600 · Cited by 2 · see citing works

Emergence of Robust Memory Manifolds

Can, T., & Krishnamurthy, K. (2025) · PRX Life · doi:10.1103/prxlife.3.023006 · Cited by 6 · see citing works

Effective synaptic interactions in subsampled nonlinear networks with strong couplingPreprint

Brinkman, B. A. W., Rieke, F. M., Shea‐Brown, E., & Buice, M. A. (2017) · arXiv (Cornell University) · Cited by 2 · see citing works

Models of working memory

Brunel, N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-15754-7.00089-4 · Cited by 1 · see citing works

Symmetries and continuous attractors in disordered neural circuits

Clark, D. G., Abbott, L. F., & Sompolinsky, H. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02452-4 · Cited by 15 · see citing works

Lyapunov theory demonstrating a fundamental limit on the speed of systems consolidation

Alemi, A., Aksay, E. R. F., & Goldman, M. S. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.023174 · Cited by 1 · see citing works

Learning temporal integration from internal feedbackPreprint

Nygren, E., Ramirez, A. D., McMahan, B., Aksay, E. R. F., & Senn, W. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.29.890509 · Cited by 1 · see citing works

Rethinking brain-wide interactions through multi-region “network of networks” modelsPreprint

Perich, M. G., & Rajan, K. (2020) · Current Opinion in Neurobiology · doi:10.31219/osf.io/58qwj · Cited by 2 · see citing works

Parallel and convergent pathways for multifeature visual processing in larval zebrafish sensorimotor decision-makingPreprint

Slangewal, K., Aimon, S., Capelle, M. Q., Kämpf, F., Naumann, H., Baier, H., Slanchev, K., & Bahl, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.12.669772 · Cited by 1 · see citing works

Developmental and genetic modulation of evidence integration dynamics in zebrafish sensorimotor decision-making

Garza, R., Hady, A. E., & Bahl, A. (2026) · Communications Biology · doi:10.64898/2026.03.01.708829 · Cited by 1 · see citing works

Inferring pathological states in cortical neuron microcircuits

Rydzewski, J., Nowak, W., & Nicosia, G. (2015) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.09.004

Neurons that Remember How We Got There

Senn, W., & Brea, J. (2015) · Neuron · doi:10.1016/j.neuron.2015.01.029

Capture of fixation by rotational flow; a deterministic hypothesis regarding scaling and stochasticity in fixational eye movements

Wilkinson, N., & Giorgio Metta, B. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00029

Cellular and Synaptic Phenotype Compensations Limit Circuit Disruption in Fmr1-KO Mouse Layer 4 Barrel Cortex but Fail to Prevent Deficits in Information ProcessingPreprint

Domanski, A. P. F., Booker, S. A., Wyllie, D. J. A., Isaac, J. T. R., & Kind, P. C. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/403725

Neural Networks and Neurocomputational Modeling

Toutounji, H., Hertäg, L., & Durstewitz, D. (2018) · doi:10.1002/9781119170174.epcn517

Constraining inference of across-region interactions using neural activity perturbationsPreprint

Basrai, D., Lee, J., Kristl, A. C., Wang, P., Miri, A., & Glaser, J. I. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.20.689584

Self-Organized Neural Integrators in Noisy Spiking NetworksPreprint

Feng, B., Gao, R., Li, N., & Shouval, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.04.722735

Sparse connectivity in short-term memory networks

Goldman, M. (2010) · Frontiers in Neuroscience · doi:10.3389/conf.fnins.2010.03.00261

Analysis and Modeling of Neural Connectivity in a Short Term Memory System

Chakraborty, S. (2005) · Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory)

Targeted photostimulation uncovers circuit motifs supporting short-term memory

Daie, K., Svoboda, K., & Druckmann, S. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00776-3 · Cited by 100 · see citing works

A neural network model of the dynamics of a short‐term memory system in the temporal cortex

Morita, M. (1992) · Systems and Computers in Japan · doi:10.1002/scj.4690230402 · Cited by 26 · see citing works

Long-term and short-term memory functions of neuronal motifs

Turan, A. S., & Kayıkçıoğlu, T. (2012) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2012.6204790