Partitioning neuronal variability

The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding

Shadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178

The analysis of visual motion: a comparison of neuronal and psychophysical performance

Britten, K., Shadlen, M., Newsome, W., & Movshon, J. (1992) · The Journal of Neuroscience · doi:10.1523/jneurosci.12-12-04745.1992 · Cited by 1,958

Reliability of spike timing in neocortical neurons

Mainen, Z. F., & Sejnowski, T. J. (1995) · Science · doi:10.1126/science.7770778 · Cited by 1,950

Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity

van Vreeswijk, C., & Sompolinsky, H. (1996) · Science · doi:10.1126/science.274.5293.1724 · Cited by 1,831

The highly irregular firing of cortical cells is inconsistent with temporal integration of random epsps.

Softky, W., & Koch, C. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00334.1993 · Cited by 1,689

Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex

Luck, S. J., Chelazzi, L., Hillyard, S. A., & Desimone, R. (1997) · Journal of Neurophysiology · doi:10.1152/jn.1997.77.1.24 · Cited by 1,615

Spatio-temporal correlations and visual signalling in a complete neuronal population

Pillow, J. W., Shlens, J., Paninski, L., Sher, A., Litke, A. M., Chichilnisky, E. J., & Simoncelli, E. P. (2008) · Nature · doi:10.1038/nature07140 · Cited by 1,332

Correlated neuronal discharge rate and its implications for psychophysical performance

Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994) · Nature · doi:10.1038/370140a0 · Cited by 1,237

Attention improves performance primarily by reducing interneuronal correlations

Cohen, M. R., & Maunsell, J. H. R. (2009) · Nature Neuroscience · doi:10.1038/nn.2439 · Cited by 1,163

Stimulus onset quenches neural variability: A widespread cortical phenomenon

Churchland, M. M., Yu, B. M., Cunningham, J. P., Sugrue, L. P., Cohen, M. R., Corrado, G. S., Newsome, W. T., Clark, A. M., Hosseini, P., Scott, B. B., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2501 · Cited by 1,084

Systems-based analysis of dendritic nonlinearities reveals temporal feature extraction in mouse L5 cortical neurons

Kalmbach, B. E., Gray, R. G. F., Johnston, D., & Cook, E. P. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00951.2016 · Cited by 19

The geometry of masking in neural populations

Ringach, D. L. (2019) · Nature Communications · doi:10.1038/s41467-019-12881-4 · Cited by 19

Low rank mechanisms underlying flexible visual representations

Ruff, D. A., Xue, C., Kramer, L. E., Baqai, F., & Cohen, M. R. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005797117 · Cited by 20

The Neural Code for Motor Control in the Cerebellum and Oculomotor Brainstem

Chaisanguanthum, K. S., Joshua, M., Medina, J. F., Bialek, W., & Lisberger, S. G. (2014) · eNeuro · doi:10.1523/eneuro.0004-14.2014 · Cited by 20

Brittleness in model selection analysis of single neuron firing ratesPreprint

Chandrasekaran, C., Soldado-Magraner, J., Peixoto, D., Newsome, W. T., Shenoy, K. V., & Sahani, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/430710 · Cited by 20

High precision coding in visual cortexPreprint

Stringer, C., Michaelos, M., & Pachitariu, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/679324 · Cited by 20

Divisively normalized neuronal processing of uncertain visual feedback for visuomotor learning

Makino, Y., Hayashi, T., & Nozaki, D. (2023) · Communications Biology · doi:10.1038/s42003-023-05578-4 · Cited by 18

Reappraising the role of motor surround inhibition in dystonia

Kassavetis, P., Sadnicka, A., Saifee, T. A., Pareés, I., Kojović, M., Bhatia, K. P., Rothwell, J. C., & Edwards, M. J. (2018) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2018.04.015 · Cited by 18

Predicting the Partition of Behavioral Variability in Speed Perception with Naturalistic Stimuli

Chin, B. M., & Burge, J. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1904-19.2019 · Cited by 18

Understanding the circuit basis of cognitive functions using mouse models

Nakajima, M., & Schmitt, L. I. (2019) · Neuroscience Research · doi:10.1016/j.neures.2019.12.009 · Cited by 19

Integration of Eye-Centered and Landmark-Centered Codes in Frontal Eye Field Gaze Responses

Bharmauria, V., Sajad, A., Li, J., Yan, X., Wang, H., & Crawford, J. D. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa090 · Cited by 19

A general decoding strategy explains the relationship between behavior and correlated variability

Ni, A. M., Huang, C., Doiron, B., & Cohen, M. R. (2022) · eLife · doi:10.7554/elife.67258 · Cited by 18

Contrast and luminance adaptation alter neuronal coding and perception of stimulus orientation

Ghodrati, M., Zavitz, E., Rosa, M. G. P., & Price, N. S. C. (2019) · Nature Communications · doi:10.1038/s41467-019-08894-8 · Cited by 19

Cortical Neural Activity Predicts Sensory Acuity Under Optogenetic Manipulation

Briguglio, J. J., Aizenberg, M., Balasubramanian, V., & Geffen, M. N. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2457-17.2017 · Cited by 20

A quantitative confidence signal detection model: 1. Fitting psychometric functions

Yi, Y., & Merfeld, D. M. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00318.2015 · Cited by 17

Amplitude modulations of cortical sensory responses in pulsatile evidence accumulation

Koay, S. A., Thiberge, S. Y., Brody, C. D., & Tank, D. W. (2020) · eLife · doi:10.7554/elife.60628 · Cited by 18

Atypically larger variability of resource allocation accounts for visual working memory deficits in schizophrenia

Zhao, Y., Ma, T., Ran, X., Zhang, L., Zhang, R., & Ku, Y. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009544 · Cited by 17

Brain-wide ongoing activity is responsible for significant cross-trial BOLD variability

Zhang, Q., Cramer, S. R., Ma, Z., Turner, K. L., Gheres, K. W., Liu, Y., Drew, P. J., & Zhang, N. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac016 · Cited by 18

Spatio-chromatic information available from different neural layers via Gaussianization

Malo, J. (2020) · The Journal of Mathematical Neuroscience · doi:10.1186/s13408-020-00095-8 · Cited by 17

A zero-inflated gamma model for post-deconvolved calcium imaging tracesPreprint

Wei, X.-X., Zhou, D. X., Grosmark, A., Ajabi, Z., Sparks, F. T., Zhou, P., Brandon, M. P., Losonczy, A., & Paninski, L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/637652 · Cited by 17

Dopamine influences attentional rate modulation in Macaque posterior parietal cortex

van Kempen, J., Brandt, C., Distler, C., Bellgrove, M. A., & Thiele, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-10634-w · Cited by 16

Single trial variability in neural activity during a working memory task reveals multiple distinct information processing sequences

Nakuci, J., Covey, T. J., Shucard, J. L., Shucard, D. W., & Muldoon, S. F. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119895 · Cited by 16

Targeted V1 comodulation supports task-adaptive sensory decisions

Haimerl, C., Ruff, D. A., Cohen, M. R., Savin, C., & Simoncelli, E. P. (2023) · Nature Communications · doi:10.1038/s41467-023-43432-7 · Cited by 16

A Simple Model for Low Variability in Neural Spike Trains

Ferrari, U., Deny, S., Marre, O., & Mora, T. (2018) · Neural Computation · doi:10.1162/neco_a_01125 · Cited by 16

Investigating the representation of uncertainty in neuronal circuits

Dehaene, G., Coen-Cagli, R., & Pouget, A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008138 · Cited by 18

Pulvinar thalamic nucleus allows for asynchronous spike propagation through the cortex

Cortes, N., & van Vreeswijk, C. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00060 · Cited by 15

Running modulates primate and rodent visual cortex differentlyPreprint

Liska, J. P., Rowley, D. P., Nguyen, T. T. K., Muthmann, J.-O., Butts, D. A., Yates, J. L., & Huk, A. C. (2023) · eLife · doi:10.7554/elife.87736 · Cited by 15

Modelling the neural code in large populations of correlated neurons

Sokoloski, S., Aschner, A., & Coen-Cagli, R. (2021) · eLife · doi:10.7554/elife.64615 · Cited by 17

The same type of visual working memory limitations in humans and monkeys

Devkar, D. T., Wright, A. A., & Ma, W. J. (2015) · Journal of Vision · doi:10.1167/15.16.13 · Cited by 15

Recurrent interactions can explain the variance in single trial responses

Kotekal, S., & MacLean, J. N. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007591 · Cited by 15

Fractional diffusion theory of balanced heterogeneous neural networks

Wardak, A., & Gong, P. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.013083 · Cited by 15

Neuronal Variability as a Proxy for Network State

Nogueira, R., Lawrie, S., & Moreno‐Bote, R. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.02.003 · Cited by 15

Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences

Fetterhoff, D., Kraft, R., Sandler, R., Opriș, I. C., Sexton, C. A., Marmarelis, V. Z., Hampson, R. E., & Deadwyler, S. A. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00130 · Cited by 15

Weak evidence for neural correlates of task-switching in macaque V1

Lange, R. D., Gómez-Laberge, C., Berezovskii, V. K., Pletenev, A., Sherdil, A., Hartmann, T. S., Haefner, R. M., & Born, R. T. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00085.2022 · Cited by 14

Multiple brain activation patterns for the same perceptual decision-making task

Nakuci, J., Yeon, J., Haddara, N., Kim, J. H., Kim, S.-P., & Rahnev, D. (2025) · Nature Communications · doi:10.1038/s41467-025-57115-y · Cited by 19

Is there signal in the noise?

Ecker, A. S., & Tolias, A. S. (2014) · Nature Neuroscience · doi:10.1038/nn.3722 · Cited by 14

Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces

Panzeri, S., Safaai, H., Feo, V. D., & Vato, A. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00165 · Cited by 14

Understanding Sensory Information Processing Through Simultaneous Multi-area Population Recordings

Zavitz, E., & Price, N. S. C. (2019) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00115 · Cited by 17

Lower neuronal variability in the monkey dorsolateral prefrontal than posterior parietal cortex

Qi, X.-L., & Constantinidis, C. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00454.2015 · Cited by 14

Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximationsPreprint

Keeley, S., Zoltowski, D. M., Yu, Y., Yates, J. L., Smith, S. L., & Pillow, J. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03318 · Cited by 14

Stimulus-dependent relationships between behavioral choice and sensory neural responses

Chicharro, D., Panzeri, S., & Haefner, R. M. (2021) · eLife · doi:10.7554/elife.54858 · Cited by 13

Signal switching may enhance processing power of the brain

Groh, J. M., Schmehl, M. N., Caruso, V. C., & Tokdar, S. T. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.04.008 · Cited by 13

Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation

Rungratsameetaweemana, N., Kim, R., Chotibut, T., & B, T. J. S. A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2316745122 · Cited by 19

Sensory population activity reveals downstream confidence computations in the primate visual system

Boundy-Singer, Z. M., Ziemba, C. M., & Goris, R. L. T. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2426441122 · Cited by 16

Correlated Variability in the Neurons With the Strongest Tuning Improves Direction Coding

Zavitz, E., Yu, H., Rosa, M. G. P., & Price, N. S. C. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx344 · Cited by 13

Fractal spike dynamics and neuronal coupling in the primate visual system

Munn, B., Zeater, N., Pietersen, A. N. J., Solomon, S. G., Cheong, S. K., Martin, P. R., & Gong, P. (2020) · The Journal of Physiology · doi:10.1113/jp278935 · Cited by 13

Spatiotemporal Coding in the Macaque Supplementary Eye Fields: Landmark Influence in the Target-to-Gaze Transformation

Bharmauria, V., Sajad, A., Yan, X., Wang, H., & Crawford, J. D. (2020) · eNeuro · doi:10.1523/eneuro.0446-20.2020 · Cited by 13

Logarithmically scaled, gamma distributed neuronal spiking

Levenstein, D., & Okun, M. (2022) · The Journal of Physiology · doi:10.1113/jp282758 · Cited by 13

When do correlations increase with firing rates in recurrent networks?

Barreiro, A. K., & Ly, C. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005506 · Cited by 14

Control Principles of Neural Dynamics Revealed by the Neurobiology of Timing

Stine, G. M., & Jazayeri, M. (2025) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-091724-015512 · Cited by 12

Frequency-band signatures of visual responses to naturalistic input in ferret primary visual cortex during free viewing

Sellers, K. K., Bennett, D., & Frӧhlich, F. (2014) · Brain Research · doi:10.1016/j.brainres.2014.12.016 · Cited by 12

Connecting psychophysical performance to neuronal response properties II: Contrast decoding and detection

May, K. A., & Solomon, J. A. (2015) · Journal of Vision · doi:10.1167/15.6.9 · Cited by 13

A simple linear readout of MT supports motion direction-discrimination performance

Yates, J. L., Katz, L. N., Levi, A. J., Pillow, J. W., & Huk, A. C. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00117.2019 · Cited by 12

Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data

Heller, C. R., & David, S. V. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271136 · Cited by 12

Coupled variability in primary sensory areas and the hippocampus during spontaneous activity

de Vasconcelos, N. A. P., Soares‐Cunha, C., Rodrigues, A. J., Ribeiro, S. T. G., & Sousa, N. (2017) · Scientific Reports · doi:10.1038/srep46077 · Cited by 12

Modeling stimulus-dependent variability improves decoding of population neural responses

Ghanbari, A., Lee, C. M., Read, H. L., & Stevenson, I. H. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab3a68 · Cited by 12

Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable modelsPreprint

Whiteway, M. R., Socha, K. Z., Bonin, V., & Butts, D. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/407858 · Cited by 12

Dynamics and sources of response variability and its coordination in visual cortex

Hoseini, M. S., Wright, N. C., Xià, J., Clawson, W. P., Shew, W. L., & Weßel, R. (2019) · Visual Neuroscience · doi:10.1017/s0952523819000117 · Cited by 12

Bayesian inference by visuomotor neurons in the prefrontal cortex

Langlois, T. A., Charlton, J. A., & Goris, R. L. T. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2420815122 · Cited by 11

Trial-to-Trial Variability of Spiking Delay Activity in Prefrontal Cortex Constrains Burst-Coding Models of Working Memory

Li, D., Constantinidis, C., & Murray, J. D. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0167-21.2021 · Cited by 12

Predictable Fluctuations in Excitatory Synaptic Strength Due to Natural Variation in Presynaptic Firing Rate

Ren, N., Wei, G., Ghanbari, A., & Stevenson, I. H. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0808-22.2022 · Cited by 12

Hierarchical unimodal processing within the primary somatosensory cortex during a bimodal detection task

Parra, S., Díaz, H., Zainos, A., Álvarez, M., Zizumbo, J., Rivera‐Yoshida, N., Pujalte, S., Bayones, L., Romo, R., & Rossi-Pool, R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2213847119 · Cited by 12

Synaptic Basis for the Generation of Response Variation in Auditory Cortex

Tao, C., Zhang, G., Zhou, C., Wang, L., Yan, S., Zhang, L. I., Zhou, Y., & Xiong, Y. (2016) · Scientific Reports · doi:10.1038/srep31024 · Cited by 11

Distinct Correlation Structure Supporting a Rate-Code for Sound Localization in the Owl’s Auditory Forebrain

Beckert, M. V., Pavão, R., & Peña, J. L. (2017) · eNeuro · doi:10.1523/eneuro.0144-17.2017 · Cited by 12

Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data

Capone, C., Gigante, G., & Giudice, P. D. (2018) · Scientific Reports · doi:10.1038/s41598-018-35433-0 · Cited by 11

Dynamic properties of internal noise probed by modulating binocular rivalry

Baker, D. H., & Richard, B. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007071 · Cited by 11

Graph quilting: graphical model selection from partially observed covariancesPreprint

Vinci, G., Dasarathy, G., & Allen, G. I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.05573 · Cited by 20

Spiking dynamics of individual neurons reflect changes in the structure and function of neuronal networks

Yang, R., Ping, H., Xiao, X., Kiani, R., & Bogdan, P. (2025) · Nature Communications · doi:10.1038/s41467-025-62202-1 · Cited by 11

Jensen’s force and the statistical mechanics of cortical asynchronous states

Buendía, V., Villegas, P., di Santo, S., Vezzani, A., Burioni, R., & Muñoz, M. A. (2019) · Scientific Reports · doi:10.1038/s41598-019-51520-2 · Cited by 10

Heterogeneous Synaptic Weighting Improves Neural Coding in the Presence of Common Noise

Sachdeva, P., Livezey, J. A., & DeWeese, M. R. (2020) · Neural Computation · doi:10.1162/neco_a_01287 · Cited by 10

Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions

Fayaz, S., Fakharian, M. A., & Ghazizadeh, A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010256 · Cited by 10

Refractoriness Accounts for Variable Spike Burst Responses in Somatosensory Cortex

Teleńczuk, B., Kempter, R., Curio, G., & Alain Destexhe, A. (2017) · eNeuro · doi:10.1523/eneuro.0173-17.2017 · Cited by 10

State dependence of stimulus-induced variability tuning in macaque MT

Lombardo, J. A., Macellaio, M. V., Liu, B., Palmer, S. E., & Osborne, L. C. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006527 · Cited by 10

When uncertain, does human self-motion decision-making fully utilize complete information?

Clark, T. K., Yi, Y., Galvan-Garza, R. C., Rey, M. C. B., & Merfeld, D. M. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00680.2017 · Cited by 10

Thalamus exhibits less sensory variability quenching than cortex

Poland, E., Donner, T. H., Müller, K.-M., Leopold, D. A., & Wilke, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-43934-9 · Cited by 11

Noise correlations and perceptual inference

Bányai, M., & Orbán, G. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.09.002 · Cited by 10

Compound Stimuli Reveal the Structure of Visual Motion Selectivity in Macaque MT Neurons

Zaharia, A. D., Goris, R. L. T., Movshon, J. A., & Simoncelli, E. P. (2019) · eNeuro · doi:10.1523/eneuro.0258-19.2019 · Cited by 10

Internal gain modulations, but not changes in stimulus contrast, preserve the neural code

Lee, S., Park, J., & Smirnakis, S. M. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.2012-18.2019 · Cited by 10

Feedback Determines the Structure of Correlated Variability in Primary Visual CortexPreprint

Bondy, A., Haefner, R. M., & Cumming, B. G. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/086256 · Cited by 10

An Efficient and Flexible Spike Train Model Via Empirical Bayes

She, Q., Wu, X., Jelfs, B., Charles, A. S., & Chan, R. H. M. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3076885 · Cited by 9

Identification of interacting neural populations: methods and statistical considerations

Kass, R. E., Bong, H., Olarinre, M., Qi, X., & Urban, K. N. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00131.2023 · Cited by 9

Training-Dependent Gradients of Timescales of Neural Dynamics in the Primate Prefrontal Cortex and Their Contributions to Working Memory

Trepka, E. B., Spitmaan, M., Qi, X.-L., Constantinidis, C., & Soltani, A. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.2442-21.2023 · Cited by 9

Distributed and dynamical communication: a mechanism for flexible cortico-cortical interactions and its functional roles in visual attention

Ni, S. Y. K., Harris, B., & Gong, P. (2024) · Communications Biology · doi:10.1038/s42003-024-06228-z · Cited by 10

Aperiodic spectral slope tracks the effects of brain state on saliency responses in the human auditory cortex

Mocchi, M. M., Bartoli, E., Magnotti, J. F., de Gee, J. W., Metzger, B. A., Pascuzzi, B., Mathura, R. K., Pulapaka, S., Goodman, W. K., Sheth, S. A., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-80911-3 · Cited by 9

The Pair-Replica-Mean-Field Limit for Intensity-based Neural Networks

Baccelli, F., & Taillefumier, T. O. (2021) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/20m1331664 · Cited by 9

How inhibitory neurons increase information transmission under threshold modulation

Hsu, W.-M. M., Kastner, D. B., Baccus, S. A., & Sharpee, T. O. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109158 · Cited by 9

Modeling the diverse effects of divisive normalization on noise correlations

Weiss, O., Bounds, H. A., Adesnik, H., & Coen-Cagli, R. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011667 · Cited by 10

A Formalism for Evaluating Analytically the Cross-Correlation Structure of a Firing-Rate Network Model

Fasoli, D., Faugeras, O. D., & Panzeri, S. (2015) · The Journal of Mathematical Neuroscience · doi:10.1186/s13408-015-0020-y · Cited by 9

Speed versus accuracy in visual search: Optimal performance and neural architecture

Chen, B., & Perona, P. (2015) · Journal of Vision · doi:10.1167/15.16.9 · Cited by 10

Testing the odds of inherent vs. observed overdispersion in neural spike counts

Taouali, W., Benvenuti, G., Wallisch, P., Chavane, F., & Perrinet, L. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00194.2015 · Cited by 10

Interpretation of correlated neural variability from models of feed-forward and recurrent circuits

Pernice, V., & da Silveira, R. A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005979 · Cited by 9

Adjusted regularization in latent graphical models: Application to multiple-neuron spike count data

Vinci, G., Ventura, V., Smith, M. A., & Kass, R. E. (2018) · The Annals of Applied Statistics · doi:10.1214/18-aoas1190 · Cited by 9

The Redemption of Noise: Inference with Neural Populations

Echeveste, R., & Lengyel, M. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.09.003 · Cited by 9

“Fast” versus “slow” word integration of visual and olfactory objects: EEG biomarkers of decision speed variability.

Olofsson, J., Syrjänen, E., Ekström, I., Larsson, M. U., & Wiens, S. (2018) · Behavioral Neuroscience · doi:10.1037/bne0000266 · Cited by 9

Trial-by-trial variability in cortical responses exhibits scaling in spatial correlations predicted from critical dynamicsPreprint

Ribeiro, T. L., Yu, S. P., Mártin, D. A., Winkowski, D. E., Kanold, P. O., Chialvo, D. R., & Plenz, D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.01.182014 · Cited by 9

A universal probabilistic spike count model reveals ongoing modulation of neural variabilityPreprint

Liu, D., & Lengyel, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.27.450063 · Cited by 9

Task-specific invariant representation in auditory cortex

Heller, C. R., Hamersky, G. R., & David, S. V. (2023) · eLife · doi:10.7554/elife.89936 · Cited by 9

A doubly stochastic renewal framework for partitioning spiking variability

Aghamohammadi, C., Chandrasekaran, C., & Engel, T. A. (2025) · Nature Communications · doi:10.1038/s41467-025-63821-4 · Cited by 9

Top-down perceptual inference shaping the activity of early visual cortex

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