Efficient codes and balanced networks

Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex

Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680 · see citing works

Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

Maass, W., Natschläger, T., & Markram, H. (2002) · Neural Computation · doi:10.1162/089976602760407955 · Cited by 4,025 · see citing works

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 · see citing works

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 · see citing works

Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons

Brunel, N. (2000) · Journal of Computational Neuroscience · doi:10.1023/a:1008925309027 · Cited by 1,733 · see citing works

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 · see citing works

How Inhibition Shapes Cortical Activity

Isaacson, J. S., & Scanziani, M. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.027 · Cited by 1,678 · see citing works

Neural population dynamics during reaching

Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012) · Nature · doi:10.1038/nature11129 · Cited by 1,636 · see citing works

Modulation of visual responses by behavioral state in mouse visual cortex

Niell, C. M., & Stryker, M. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.033 · Cited by 1,483 · see citing works

Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex

Wehr, M., & Zador, A. M. (2003) · Nature · doi:10.1038/nature02116 · Cited by 1,363 · see citing works

Noise, neural codes and cortical organization

Shadlen, M. N., & Newsome, W. T. (1994) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(94)90059-0 · Cited by 1,240 · see citing works

Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex

Ohki, K., Chung, S., Ch'ng, Y. H., Kara, P., & Reid, R. C. (2005) · Nature · doi:10.1038/nature03274 · Cited by 1,191 · see citing works

Pattern recognition computation using action potential timing for stimulus representation

Hopfield, J. J. (1995) · Nature · doi:10.1038/376033a0 · Cited by 1,163 · see citing works

Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model

Compte, A. (2000) · Cerebral Cortex · doi:10.1093/cercor/10.9.910 · Cited by 1,143 · see citing works

The Asynchronous State in Cortical Circuits

Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., & Harris, K. D. (2010) · Science · doi:10.1126/science.1179850 · Cited by 1,106 · see citing works

The high-conductance state of neocortical neurons in vivo

Alain Destexhe, A., Rudolph, M., & Paré, D. (2003) · Nature reviews. Neuroscience · doi:10.1038/nrn1198 · Cited by 1,077 · see citing works

Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex

Amit, D. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.3.237 · Cited by 1,061 · see citing works

Turning on and off recurrent balanced cortical activity

Shu, Y., Hasenstaub, A., & McCormick, D. A. (2003) · Nature · doi:10.1038/nature01616 · Cited by 1,054 · see citing works

Measuring and interpreting neuronal correlations

Cohen, M. R., & Kohn, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2842 · Cited by 1,032 · see citing works

Neocortical Network Activity<i>In Vivo</i>Is Generated through a Dynamic Balance of Excitation and Inhibition

Haider, B., Duque, A., Hasenstaub, A. R., & McCormick, D. A. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.5297-05.2006 · Cited by 962 · see citing works

Choice-specific sequences in parietal cortex during a virtual-navigation decision task

Harvey, C. D., Coen, P., & Tank, D. W. (2012) · Nature · doi:10.1038/nature10918 · Cited by 916 · see citing works

Network anatomy and in vivo physiology of visual cortical neurons

Bock, D. D., Lee, W.-C. A., Kerlin, A., Andermann, M. L., Hood, G., Wetzel, A. W., Yurgenson, S., Soucy, E. R., Kim, H. S., & Reid, R. C. (2011) · Nature · doi:10.1038/nature09802 · Cited by 851 · 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

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Vogels, T. P., Sprekeler, H., Zenke, F., Claudia Clopath, A., & Gerstner, W. (2011) · Science · doi:10.1126/science.1211095 · Cited by 779 · see citing works

Oxytocin enables maternal behaviour by balancing cortical inhibition

Marlin, B. J., Mitre, M., D’amour, J. A., Chao, M. V., & Froemke, R. C. (2015) · Nature · doi:10.1038/nature14402 · Cited by 744 · see citing works

Equalizing excitation-inhibition ratios across visual cortical neurons

Xue, M., Atallah, B. V., & Scanziani, M. (2014) · Nature · doi:10.1038/nature13321 · Cited by 722 · see citing works

The impulses produced by sensory nerve‐endings

Adrian, E. D., & Zotterman, Y. (1926) · The Journal of Physiology · doi:10.1113/jphysiol.1926.sp002281 · Cited by 713 · see citing works

Chaotic Balanced State in a Model of Cortical Circuits

van Vreeswijk, C., & Sompolinsky, H. (1998) · Neural Computation · doi:10.1162/089976698300017214 · Cited by 711 · see citing works

Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities

Okun, M., & Lampl, I. (2008) · Nature Neuroscience · doi:10.1038/nn.2105 · Cited by 701 · see citing works

Statistically optimal perception and learning: from behavior to neural representations

Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672 · see citing works

Broadly tuned response properties of diverse inhibitory neuron subtypes in mouse visual cortex

Kerlin, A. M., Andermann, M. L., Berezovskii, V. K., & Reid, R. C. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.002 · Cited by 613 · see citing works

A synaptic memory trace for cortical receptive field plasticity

Froemke, R. C., Merzenich, M. M., & Schreiner, C. E. (2007) · Nature · doi:10.1038/nature06289 · Cited by 600 · see citing works

Instantaneous Modulation of Gamma Oscillation Frequency by Balancing Excitation with Inhibition

Atallah, B. V., & Scanziani, M. (2009) · Neuron · doi:10.1016/j.neuron.2009.04.027 · Cited by 588 · see citing works

Representations of Odor in the Piriform Cortex

Stettler, D. D., & Axel, R. (2009) · Neuron · doi:10.1016/j.neuron.2009.09.005 · Cited by 583 · see citing works

Inhibition dominates sensory responses in the awake cortex

Haider, B., Häusser, M., & Carandini, M. (2012) · Nature · doi:10.1038/nature11665 · Cited by 541 · see citing works

Dense Inhibitory Connectivity in Neocortex

Fino, E., & Yuste, R. (2011) · Neuron · doi:10.1016/j.neuron.2011.02.025 · Cited by 532 · see citing works

Orientation Tuning of Input Conductance, Excitation, and Inhibition in Cat Primary Visual Cortex

Anderson, J. S., Carandini, M., & Ferster, D. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.84.2.909 · Cited by 499 · see citing works

Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex

London, M., Roth, A., Beeren, L., Häusser, M., & Latham, P. (2010) · Nature · doi:10.1038/nature09086 · Cited by 494 · see citing works

Odor Representations in Olfactory Cortex: “Sparse” Coding, Global Inhibition, and Oscillations

Poo, C., & Isaacson, J. S. (2009) · Neuron · doi:10.1016/j.neuron.2009.05.022 · Cited by 469 · see citing works

Membrane potential dynamics of GABAergic neurons in the barrel cortex of behaving mice

Gentet, L. J., Avermann, M., Matyas, F., Staiger, J. F., & Petersen, C. C. H. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.006 · Cited by 429 · see citing works

A Scalable Population Code for Time in the Striatum

Mello, G. B. M. E., Soares, S., & Paton, J. J. (2015) · Current Biology · doi:10.1016/j.cub.2015.02.036 · Cited by 393 · see citing works

Orientation and Direction Selectivity of Synaptic Inputs in Visual Cortical Neurons

Monier, C., Chavane, F., Baudot, P., Graham, L. J., & Frégnac, Y. (2003) · Neuron · doi:10.1016/s0896-6273(03)00064-3 · Cited by 356 · see citing works

Role of the cortical neuron: integrator or coincidence detector?

Abeles, M. (1982) · PubMed · Cited by 356 · see citing works

Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements

Hennequin, G., Vogels, T. P., & Gerstner, W. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.045 · Cited by 348 · see citing works

Balanced Amplification: A New Mechanism of Selective Amplification of Neural Activity Patterns

Murphy, B. K., & Miller, K. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.02.005 · Cited by 338 · see citing works

Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex

Zhou, M., Liang, F., Xiong, X. R., Li, L., Li, H., Xiao, Z., Tao, H. W., & Zhang, L. I. (2014) · Nature Neuroscience · doi:10.1038/nn.3701 · Cited by 316 · see citing works

Developmental sensory experience balances cortical excitation and inhibition

Dorrn, A. L., Yuan, K., Barker, A. J., Schreiner, C. E., & Froemke, R. C. (2010) · Nature · doi:10.1038/nature09119 · Cited by 296 · see citing works

Dynamics of excitation and inhibition underlying stimulus selectivity in rat somatosensory cortex

Wilent, W. B., & Contreras, D. (2005) · Nature Neuroscience · doi:10.1038/nn1545 · Cited by 288 · see citing works

Lateral Sharpening of Cortical Frequency Tuning by Approximately Balanced Inhibition

Wu, G. K., Arbuckle, R., Liu, B., Tao, H. W., & Zhang, L. I. (2008) · Neuron · doi:10.1016/j.neuron.2008.01.035 · Cited by 276 · see citing works

Tone-Evoked Excitatory and Inhibitory Synaptic Conductances of Primary Auditory Cortex Neurons

Tan, A. Y. Y., Zhang, L. I., Merzenich, M. M., & Schreiner, C. E. (2004) · Journal of Neurophysiology · doi:10.1152/jn.01020.2003 · Cited by 272 · see citing works

Predictive Coding of Dynamical Variables in Balanced Spiking Networks

Martin, B., Machens, C. K., & Denève, S. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003258 · Cited by 257 · see citing works

Response features of parvalbumin-expressing interneurons suggest precise roles for subtypes of inhibition in visual cortex

Runyan, C. A., Schummers, J., Van Wart, A., Kuhlman, S. J., Wilson, N. R., Huang, Z. J., & Sur, M. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.006 · Cited by 244 · see citing works

Long-term modification of cortical synapses improves sensory perception

Froemke, R. C., Carcea, I., Barker, A. J., Yuan, K., Seybold, B., Martins, A. R. O., Zaika, N., Bernstein, H. L., Wachs, M. A., Levis, P. A., et al. (2012) · Nature Neuroscience · doi:10.1038/nn.3274 · Cited by 212 · see citing works

Balanced cortical microcircuitry for maintaining information in working memory

Lim, S., & Goldman, M. S. (2013) · Nature Neuroscience · doi:10.1038/nn.3492 · Cited by 208 · see citing works

Inhibitory and Excitatory Spike-Timing-Dependent Plasticity in the Auditory Cortex

D’amour, J. A., & Froemke, R. C. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.014 · Cited by 201 · 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 198 · see citing works

Stimulus feature selectivity in excitatory and inhibitory neurons in primary visual cortex

Cardin, J. A., Palmer, L. A., & Contreras, D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.1692-07.2007 · Cited by 185 · see citing works

Broad Inhibition Sharpens Orientation Selectivity by Expanding Input Dynamic Range in Mouse Simple Cells

Liu, B.-H., Li, Y.-T., Ma, W.-P., Pan, C.-J., Zhang, L. I., & Tao, H. W. (2011) · Neuron · doi:10.1016/j.neuron.2011.06.017 · Cited by 173 · see citing works

Visual Receptive Field Structure of Cortical Inhibitory Neurons Revealed by Two-Photon Imaging Guided Recording

Liu, B., Li, P., Li, Y.-T., Sun, Y. J., Yanagawa, Y., Obata, K., Zhang, L. I., & Tao, H. W. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1915-09.2009 · Cited by 145 · see citing works

In vitro and in vivo measures of evoked excitatory and inhibitory conductance dynamics in sensory cortices

Monier, C., Fournier, J., & Frégnac, Y. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.11.008 · Cited by 143 · see citing works

Short-Term Plasticity Explains Irregular Persistent Activity in Working Memory Tasks

Hansel, D., & Mato, G. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3455-12.2013 · Cited by 135 · see citing works

Orientation Selectivity of Synaptic Input to Neurons in Mouse and Cat Primary Visual Cortex

Tan, A. Y. Y., Brown, B. D., Scholl, B., Mohanty, D., & Priebe, N. J. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2039-11.2011 · Cited by 121 · see citing works

Laminar processing of stimulus orientation in cat visual cortex

Martı́nez, L. M., Alonso, J., Reid, R. C., & Hirsch, J. A. (2002) · The Journal of Physiology · doi:10.1113/jphysiol.2001.012776 · Cited by 114 · see citing works

Dynamical Entropy Production in Spiking Neuron Networks in the Balanced State

Monteforte, M., & Wolf, F. (2010) · Physical Review Letters · doi:10.1103/physrevlett.105.268104 · Cited by 112 · see citing works

Spike-Based Population Coding and Working Memory

Martin, B., & Denève, S. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001080 · Cited by 104 · see citing works

Synaptic Mechanisms of Tight Spike Synchrony at Gamma Frequency in Cerebral Cortex

Salkoff, D. B., Zagha, E., Yüzgeç, Ö., & McCormick, D. A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0828-15.2015 · Cited by 102 · see citing works

Membrane Potential Synchrony in Primary Visual Cortex during Sensory Stimulation

Yu, J., & Ferster, D. (2010) · Neuron · doi:10.1016/j.neuron.2010.11.027 · Cited by 82 · see citing works

Specialized Inhibitory Synaptic Actions Between Nearby Neocortical Pyramidal Neurons

Ren, M., Yoshimura, Y., Takada, N., Horibe, S., & Komatsu, Y. (2007) · Science · doi:10.1126/science.1135468 · Cited by 79 · see citing works

Balanced tone-evoked synaptic excitation and inhibition in mouse auditory cortex

Tan, A. Y. Y., & Wehr, M. (2009) · Neuroscience · doi:10.1016/j.neuroscience.2009.07.032 · Cited by 77 · see citing works

Axo-dendritic overlap and laminar projection can explain interneuron connectivity to pyramidal cells

Packer, A. M., McConnell, D. J., Fino, E., & Yuste, R. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs210 · Cited by 73 · see citing works

Synaptic Input Correlations Leading to Membrane Potential Decorrelation of Spontaneous Activity in Cortex

Graupner, M., & Reyes, A. D. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0347-13.2013 · Cited by 68 · see citing works

Neural oscillations as a signature of efficient coding in the presence of synaptic delays

Chalk, M., Gutkin, B. S., & Denève, S. (2016) · eLife · doi:10.7554/elife.13824 · Cited by 67 · see citing works

Learning optimal spike-based representations

Bourdoukan, R., Barrett, D. G. T., Denève, S., & Machens, C. K. (2012) · neural information processing systems · Cited by 50 · see citing works

Constructing Precisely Computing Networks with Biophysical Spiking Neurons

Schwemmer, M. A., Fairhall, A. L., Denève, S., & Shea‐Brown, E. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4951-14.2015 · Cited by 47 · see citing works

Balanced Cortical Microcircuitry for Spatial Working Memory Based on Corrective Feedback Control

Lim, S., & Goldman, M. S. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4602-13.2014 · Cited by 44 · see citing works

Causal Inference and Explaining Away in a Spiking Network

Moreno‐Bote, R., & Drugowitsch, J. (2015) · Scientific Reports · doi:10.1038/srep17531 · Cited by 40 · see citing works

Enforcing balance allows local supervised learning in spiking recurrent networks

Bourdoukan, R., & Denève, S. (2015) · neural information processing systems · Cited by 28 · see citing works

A spontaneous state of weakly correlated synaptic excitation and inhibition in visual cortex

Tan, A. Y. Y., Andoni, S., & Priebe, N. J. (2013) · Neuroscience · doi:10.1016/j.neuroscience.2013.05.037 · Cited by 19 · see citing works

Firing rate predictions in optimal balanced networks

Barrett, D. G. T., Denève, S., & Machens, C. K. (2013) · Cited by 18 · see citing works

Optimal compensation for neuron deathPreprint

Barrett, D. G. T., Denève, S., & Machens, C. K. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/029512 · Cited by 3 · see citing works

Hippocampal GABAergic Inhibitory Interneurons

Pelkey, K. A., Chittajallu, R., Craig, M. T., Tricoire, L., Wester, J. C., & McBain, C. J. (2017) · Physiological Reviews · doi:10.1152/physrev.00007.2017 · Cited by 889 · see citing works

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

Zenke, F., & Ganguli, S. (2018) · Neural Computation · doi:10.1162/neco_a_01086 · Cited by 533 · see citing works

Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics

Adam, Y., Kim, J. J., Lou, S., Zhao, Y., Xie, M. E., Brinks, D., Wu, H., Mostajo-Radji, M. A., Kheifets, S., Parot, V., et al. (2019) · Nature · doi:10.1038/s41586-019-1166-7 · Cited by 327 · see citing works

Motor learning promotes remyelination via new and surviving oligodendrocytes

Bacmeister, C. M., Barr, H. J., McClain, C. R., Thornton, M. A., Nettles, D., Welle, C. G., & Hughes, E. G. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0637-3 · Cited by 310 · see citing works

With or without you: predictive coding and Bayesian inference in the brain

Aitchison, L., & Lengyel, M. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.08.010 · Cited by 308 · see citing works

Inhibition decorrelates visual feature representations in the inner retina

Franke, K., Berens, P., Schubert, T., Bethge, M., Euler, T., & Baden, T. (2017) · Nature · doi:10.1038/nature21394 · Cited by 300 · see citing works

The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks

Cramer, B., Stradmann, Y., Schemmel, J., & Zenke, F. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3044364 · Cited by 295 · see citing works

A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

Auge, D., Hille, J., Mueller, E., & Knoll, A. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10562-2 · Cited by 251 · see citing works

The spatial structure of correlated neuronal variability

Rosenbaum, R., Smith, M. A., Kohn, A., Rubin, J. E., & Doiron, B. (2016) · Nature Neuroscience · doi:10.1038/nn.4433 · Cited by 236 · see citing works

Internal models in biological control

McNamee, D., & Wolpert, D. M. (2019) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105206 · Cited by 225 · 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 198 · see citing works

Inhibitory stabilization and cortical computation

Sadeh, S., & Clopath, C. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-020-00390-z · Cited by 183 · see citing works

Historical pitfalls and new directions in the neuroscience of emotion

Barrett, L. F., & Satpute, A. B. (2019) · Neuroscience Letters · doi:10.1016/j.neulet.2017.07.045 · Cited by 180 · see citing works

Synaptic E-I Balance Underlies Efficient Neural Coding

Zhou, S., & Yu, Y. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00046 · Cited by 166 · see citing works

Principles of large-scale neural interactions

Vinck, M. A., Uran, C., Spyropoulos, G., Onorato, I., Broggini, A. C., Schneider, M., & Canales‐Johnson, A. (2023) · Neuron · doi:10.1016/j.neuron.2023.03.015 · Cited by 160 · see citing works

Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders

Lord, L.-D., Stevner, A. B. A., Deco, G., & Kringelbach, M. L. (2017) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2016.0283 · Cited by 161 · see citing works

Synaptic scaling rule preserves excitatory–inhibitory balance and salient neuronal network dynamics

Barral, J., & Reyes, A. D. (2016) · Nature Neuroscience · doi:10.1038/nn.4415 · Cited by 156 · see citing works

Are hallucinations due to an imbalance between excitatory and inhibitory influences on the brain?

Jardri, R., Hugdahl, K., Hughes, M., Brunelin, J., Waters, F., Alderson-Day, B., Smailes, D., Sterzer, P., Corlett, P. R., Leptourgos, P., et al. (2016) · Schizophrenia Bulletin · doi:10.1093/schbul/sbw075 · Cited by 157 · see citing works

Inhibitory connectivity defines the realm of excitatory plasticity

Mongillo, G., Rumpel, S., & Loewenstein, Y. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0226-x · Cited by 148 · see citing works

From mechanisms to markers: novel noninvasive EEG proxy markers of the neural excitation and inhibition system in humans

Ahmad, J., Ellis, C. L., Leech, R., Voytek, B., Garcés, P., Jones, E. J. H., Buitelaar, J. K., Loth, E., dos Santos, F. P., Amil, A. F., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02218-z · Cited by 164 · see citing works

The Brain as an Efficient and Robust Adaptive Learner

Denève, S., Alemi, A., & Bourdoukan, R. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.016 · Cited by 140 · see citing works

Neuronal energy consumption: biophysics, efficiency and evolution

Niven, J. E. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.09.004 · Cited by 135 · see citing works

What is the dynamical regime of cerebral cortex?

Ahmadian, Y., & Miller, K. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.031 · Cited by 140 · see citing works

Precise excitation-inhibition balance controls gain and timing in the hippocampus

Bhatia, A., Moza, S., & Bhalla, U. S. (2019) · eLife · doi:10.7554/elife.43415 · Cited by 136 · see citing works

The HTM Spatial Pooler—A Neocortical Algorithm for Online Sparse Distributed Coding

Cui, Y., Ahmad, S., & Hawkins, J. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00111 · Cited by 135 · see citing works

Population-Level Neural Codes Are Robust to Single-Neuron Variability from a Multidimensional Coding Perspective

Montijn, J. S., Meijer, G. T., Lansink, C. S., & Pennartz, C. M. A. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.07.065 · Cited by 124 · see citing works

Energy‐efficient neural information processing in individual neurons and neuronal networks

Yu, L., & Yu, Y. (2017) · Journal of Neuroscience Research · doi:10.1002/jnr.24131 · Cited by 121 · see citing works

How many neurons are sufficient for perception of cortical activity?

Dalgleish, H. W. P., Russell, L. E., Packer, A. M., Roth, A., Gauld, O. M., Greenstreet, F., Thompson, E. J., & Häusser, M. (2020) · eLife · doi:10.7554/elife.58889 · Cited by 121 · see citing works

Where is the error? Hierarchical predictive coding through dendritic error computation

Mikulasch, F. A., Rudelt, L., Wibral, M., & Priesemann, V. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.09.007 · Cited by 128 · see citing works

Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity

Rubin, R., Abbott, L. F., & Sompolinsky, H. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705841114 · Cited by 120 · see citing works

Biologically Realistic Mean-Field Models of Conductance-Based Networks of Spiking Neurons with Adaptation

di Volo, M., Romagnoni, A., Capone, C., & Alain Destexhe, A. (2019) · Neural Computation · doi:10.1162/neco_a_01173 · Cited by 120 · see citing works

The effects of individualised intermittent theta burst stimulation in the prefrontal cortex: A TMS‐EEG study

Chung, S. W., Sullivan, C. M., Rogasch, N. C., Hoy, K. E., Bailey, N. W., Cash, R. F. H., & Fitzgerald, P. B. (2018) · Human Brain Mapping · doi:10.1002/hbm.24398 · Cited by 115 · see citing works

Local Feedback Inhibition Tightly Controls Rapid Formation of Hippocampal Place Fields

Rolotti, S. V., Ahmed, M. S., Szoboszlay, M., Geiller, T., Negrean, A., Blockus, H., Gonzalez, K. C., Sparks, F. T., Canales, A. S. S., Tuttman, A. L., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.12.003 · Cited by 114 · see citing works

Neuronal circuits overcome imbalance in excitation and inhibition by adjusting connection numbers

Sukenik, N., Vinogradov, O., Weinreb, E., Segal, M., Levina, A., & Moses, E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2018459118 · Cited by 109 · see citing works

Concepts dissolve artificial boundaries in the study of emotion and cognition, uniting body, brain, and mind

Hoemann, K., & Barrett, L. F. (2018) · Cognition & Emotion · doi:10.1080/02699931.2018.1535428 · Cited by 111 · see citing works

Mixed emotions in the predictive brain

Hoemann, K., Gendron, M., & Barrett, L. F. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.05.013 · Cited by 108 · see citing works

Interoception as modeling, allostasis as control

Sennesh, E., Theriault, J., Brooks, D., van de Meent, J.-W., Barrett, L. F., & Quigley, K. S. (2022) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108242 · Cited by 107 · see citing works

Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping

Williams, A. H., Poole, B., Maheswaranathan, N., Dhawale, A. K., Fisher, T., Wilson, C. D., Brann, D. H., Trautmann, E. M., Ryu, S., Shusterman, R., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.10.020 · Cited by 102 · see citing works

Regulation of circuit organization and function through inhibitory synaptic plasticity

Wu, Y. K., Miehl, C., & Gjorgjieva, J. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.10.006 · Cited by 102 · see citing works

Precisely timed inhibition facilitates action potential firing for spatial coding in the auditory brainstem

Beiderbeck, B., Myoga, M. H., Müller, N. I. C., Callan, A. R., Friauf, E., Grothe, B., & Pecka, M. (2018) · Nature Communications · doi:10.1038/s41467-018-04210-y · Cited by 100 · see citing works

Catecholamines alter the intrinsic variability of cortical population activity and perception

Pfeffer, T. A., Avrămiea, A.-E., Nolte, G., Engel, A. K., Linkenkaer‐Hansen, K., & Donner, T. H. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2003453 · Cited by 97 · see citing works

A biophysical account of multiplication by a single neuron

Groschner, L. N., Malis, J. G., Zuidinga, B., & Borst, A. (2022) · Nature · doi:10.1038/s41586-022-04428-3 · Cited by 94 · see citing works

Increased connectivity of hub networks and cognitive impairment in multiple sclerosis

Meijer, K. A., Eijlers, A. J. C., Douw, L., Uitdehaag, B. M. J., Barkhof, F., Geurts, J. J. G., & Schoonheim, M. M. (2017) · Neurology · doi:10.1212/wnl.0000000000003982 · Cited by 94 · see citing works

Single synaptic inputs drive high-precision action potentials in parvalbumin expressing GABA-ergic cortical neurons in vivo

Jouhanneau, J.-S., Kremkow, J., & Poulet, J. F. A. (2018) · Nature Communications · doi:10.1038/s41467-018-03995-2 · Cited by 94 · see citing works

The logic of recurrent circuits in the primary visual cortex

Oldenburg, I. A., Hendricks, W. D., Handy, G., Shamardani, K., Bounds, H. A., Doiron, B., & Adesnik, H. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01510-5 · Cited by 91 · see citing works

Species-conserved SYNGAP1 phenotypes associated with neurodevelopmental disorders

Kılınç, M., Creson, T. K., Rojas, C., Aceti, M., Ellegood, J., Vaissière, T., Lerch, J. P., & Rumbaugh, G. (2018) · Molecular and Cellular Neuroscience · doi:10.1016/j.mcn.2018.03.008 · Cited by 81 · see citing works

Adult-born dentate granule cells promote hippocampal population sparsity

McHugh, S. B., Lopes‐dos‐Santos, V., Gava, G. P., Hartwich, K., Tam, S. K. E., Bannerman, D. M., & Dupret, D. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01176-5 · Cited by 89 · see citing works

Complexity and 1/f slope jointly reflect brain states

Medel, V., Irani, M., Crossley, N. A., Ossandón, T., & Boncompte, G. (2023) · Scientific Reports · doi:10.1038/s41598-023-47316-0 · Cited by 85 · see citing works

Atomic Scale Dynamics Drive Brain-like Avalanches in Percolating Nanostructured Networks

Pike, M. D., Bose, S. K., Mallinson, J. B., Acharya, S. K., Shirai, S., Galli, E., Weddell, S. J., Bones, P. J., Arnold, M. D., & Brown, S. A. (2020) · Nano Letters · doi:10.1021/acs.nanolett.0c01096 · Cited by 83 · see citing works

Adaptive coding for dynamic sensory inference

Młynarski, W. F., & Hermundstad, A. M. (2018) · eLife · doi:10.7554/elife.32055 · Cited by 81 · see citing works

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

Gilra, A., & Gerstner, W. (2017) · eLife · doi:10.7554/elife.28295 · Cited by 89 · see citing works

Simple framework for constructing functional spiking recurrent neural networks

Kim, R., Li, Y., & B, T. J. S. A. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905926116 · Cited by 81 · see citing works

Functional specificity of recurrent inhibition in visual cortex

Znamenskiy, P., Kim, M.-H., Muir, D. R., Iacaruso, M. F., Hofer, S. B., & Mrsic‐Flogel, T. D. (2024) · Neuron · doi:10.1016/j.neuron.2023.12.013 · Cited by 81 · see citing works

Predictive coding is a consequence of energy efficiency in recurrent neural networks

Ali, A. G., Ahmad, N., de Groot, E., van Gerven, M. A. J., & Kietzmann, T. C. (2022) · Patterns · doi:10.1016/j.patter.2022.100639 · Cited by 83 · see citing works

More than just summed neuronal activity: how multiple cell types shape the BOLD response

Howarth, C., Mishra, A., & Hall, C. N. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0630 · Cited by 78 · see citing works

Computation noise in human learning and decision-making: origin, impact, function

Findling, C., & Wyart, V. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.018 · 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

Transition from Asynchronous to Oscillatory Dynamics in Balanced Spiking Networks with Instantaneous Synapses

di Volo, M., & Torcini, A. (2018) · Physical Review Letters · doi:10.1103/physrevlett.121.128301 · Cited by 75 · see citing works

Lognormal firing rate distribution reveals prominent fluctuation–driven regime in spinal motor networks

Petersen, P. C., & Berg, R. W. (2016) · eLife · doi:10.7554/elife.18805 · Cited by 76 · see citing works

What Is Excitation/Inhibition and How Is It Regulated? A Case of the Elephant and the Wisemen

He, H., & Cline, H. T. (2019) · Journal of Experimental Neuroscience · doi:10.1177/1179069519859371 · Cited by 75 · see citing works

Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortexPreprint

Znamenskiy, P., Kim, M.-H., Muir, D. R., Iacaruso, M. F., Hofer, S. B., & Mrsic‐Flogel, T. D. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/294835 · Cited by 72 · see citing works

Learning prediction error neurons in a canonical interneuron circuit

Hertäg, L., & Sprekeler, H. (2020) · eLife · doi:10.7554/elife.57541 · Cited by 71 · see citing works

Learning Nonlinear Dynamics in Efficient, Balanced Spiking Networks Using Local Plasticity Rules

Alemi, A., Machens, C. K., Denève, S., & Slotine, J.-J. E. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11320 · Cited by 69 · see citing works

Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells

Voelker, A. R., & Eliasmith, C. (2017) · Neural Computation · doi:10.1162/neco_a_01046 · Cited by 65 · see citing works

Circular inference: mistaken belief, misplaced trust

Denève, S., & Jardri, R. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.04.001 · Cited by 65 · see citing works

Muscarinic Receptors, from Synaptic Plasticity to its Role in Network Activity

de Sevilla, D. F., Núñez, Á., & Buño, W. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.04.005 · Cited by 65 · see citing works

Optimal compensation for neuron loss

Barrett, D. G. T., Denève, S., & Machens, C. K. (2016) · eLife · doi:10.7554/elife.12454 · Cited by 63 · see citing works

Self-healing codes: How stable neural populations can track continually reconfiguring neural representations

Rule, M. E., & O’Leary, T. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106692119 · Cited by 67 · see citing works

From the statistics of connectivity to the statistics of spike times in neuronal networks

Ocker, G. K., Hu, Y., Buice, M. A., Doiron, B., Josić́, K., Rosenbaum, R., & Shea‐Brown, E. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.07.011 · Cited by 58 · see citing works

Learning excitatory-inhibitory neuronal assemblies in recurrent networks

Mackwood, O., Naumann, L., & Sprekeler, H. (2021) · eLife · doi:10.7554/elife.59715 · Cited by 64 · see citing works

Neural encoding of sensory and behavioral complexity in the auditory cortex

Kuchibhotla, K. V., & Bathellier, B. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.04.002 · Cited by 57 · see citing works

Precise Synaptic Balance in the Zebrafish Homolog of Olfactory Cortex

Rupprecht, P., & Friedrich, R. W. (2018) · Neuron · doi:10.1016/j.neuron.2018.09.013 · Cited by 57 · see citing works

Cortical reliability amid noise and chaos

Nolte, M., Reimann, M. W., King, J. G., Markram, H., & Muller, E. B. (2019) · Nature Communications · doi:10.1038/s41467-019-11633-8 · Cited by 56 · see citing works

Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible With Various Temporal Codes

Hong, C., Wei, X., Wang, J., Deng, B., Yu, H., & Che, Y. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2919662 · Cited by 55 · see citing works

The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks

DePasquale, B., Sussillo, D., Abbott, L. F., & Churchland, M. M. (2023) · Neuron · doi:10.1016/j.neuron.2022.12.007 · Cited by 57 · see citing works

Strength and Diversity of Inhibitory Signaling Differentiates Primate Anterior Cingulate from Lateral Prefrontal Cortex

Medalla, M., Gilman, J. P., Wang, J., & Luebke, J. I. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3757-16.2017 · Cited by 54 · see citing works

A general principle of dendritic constancy: A neuron’s size- and shape-invariant excitability

Cuntz, H., Bird, A. D., Mittag, M., Beining, M., Schneider, M., Mediavilla, L., Hoffmann, F. Z., Deller, T., & Jedlička, P. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.028 · Cited by 56 · see citing works

Efficient and robust coding in heterogeneous recurrent networks

Zeldenrust, F., Gutkin, B. S., & Denève, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008673 · Cited by 51 · see citing works

Operating in a Reverberating Regime Enables Rapid Tuning of Network States to Task Requirements

Wilting, J., Dehning, J., Neto, J. P., Rudelt, L., Wibral, M., Zierenberg, J., & Priesemann, V. (2018) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00055 · Cited by 50 · see citing works

Reliability of an interneuron response depends on an integrated sensory state

Dobosiewicz, M., Liu, Q., & Bargmann, C. I. (2019) · eLife · doi:10.7554/elife.50566 · Cited by 51 · see citing works

Connectivity concepts in neuronal network modeling

Senk, J., Kriener, B., Djurfeldt, M., Voges, N., Jiang, H.-J., Schüttler, L., Gramelsberger, G., Diesmann, M., Pleßer, H. E., & van Albada, S. J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010086 · Cited by 46 · see citing works

Sparse Computation in Adaptive Spiking Neural Networks

Zambrano, D., Nusselder, R., Scholte, H. S., & Bohté, S. M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00987 · Cited by 49 · see citing works

Conceptual and technical advances define a key moment for theoretical neuroscience

Churchland, A. K., & Abbott, L. F. (2016) · Nature Neuroscience · doi:10.1038/nn.4255 · Cited by 47 · see citing works

Progress and challenges for understanding the function of cortical microcircuits in auditory processing

Blackwell, J. M., & Geffen, M. N. (2017) · Nature Communications · doi:10.1038/s41467-017-01755-2 · Cited by 46 · see citing works

Balancing Flexibility and Interference in Working Memory

Buschman, T. J. (2021) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100419-104831 · Cited by 43 · see citing works

Perturbing low dimensional activity manifolds in spiking neuronal networks

Wärnberg, E., & Kumar, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007074 · Cited by 51 · see citing works

Learning recurrent dynamics in spiking networks

Kim, C. M., & Chow, C. C. (2018) · eLife · doi:10.7554/elife.37124 · Cited by 45 · see citing works

Hopf Bifurcation in Mean Field Explains Critical Avalanches in Excitation-Inhibition Balanced Neuronal Networks: A Mechanism for Multiscale Variability

Liang, J., Zhou, T., & Zhou, C. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.580011 · Cited by 45 · see citing works

Synaptic balance due to homeostatically self-organized quasicritical dynamics

Girardi‐Schappo, M., Brochini, L., Costa, A. D. A., Carvalho, T. T. A., & Kinouchi, O. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.012042 · Cited by 45 · see citing works

A circuit model of auditory cortex

Park, Y., & Geffen, M. N. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008016 · Cited by 45 · see citing works

Does the brain behave like a (complex) network? I. Dynamics

Papo, D., & Buldú, J. M. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.12.006 · Cited by 42 · see citing works

Computational Account of Spontaneous Activity as a Signature of Predictive Coding

Koren, V., & Denève, S. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005355 · Cited by 44 · see citing works

Extrinsic and default mode networks in psychiatric conditions: Relationship to excitatory-inhibitory transmitter balance and early trauma

Allen, P., Sommer, I. E., Jardri, R., Eysenck, M. W., & Hugdahl, K. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.02.004 · Cited by 41 · see citing works

Large-scale interactions in predictive processing: oscillatory versus transient dynamics

Vinck, M. A., Uran, C., Dowdall, J. R., Rummell, B., & Canales‐Johnson, A. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.09.013 · Cited by 42 · see citing works

Excitation–Inhibition Balance, Neural Criticality, and Activities in Neuronal Circuits

Liang, J., Yang, Z., & Zhou, C. (2024) · The Neuroscientist · doi:10.1177/10738584231221766 · Cited by 42 · see citing works

Regulating firing rates in a neural circuit by activating memristive synapse with magnetic coupling

Wu, F., Zhang⋆, Y., & Zhang, X. (2019) · Nonlinear Dynamics · doi:10.1007/s11071-019-05239-4 · Cited by 41 · see citing works

Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks

Luboeinski, J., & Tetzlaff, C. (2021) · Communications Biology · doi:10.1038/s42003-021-01778-y · Cited by 41 · see citing works

Synaptic patterning and the timescales of cortical dynamics

Duarte, R., Seeholzer, A., Zilles, K., & Morrison, A. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.02.007 · Cited by 36 · see citing works

Is impaired information processing speed a matter of structural or functional damage in MS?

Meijer, K. A., van Geest, Q., Eijlers, A. J. C., Geurts, J. J. G., Schoonheim, M. M., & Hulst, H. E. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.09.021 · Cited by 39 · see citing works

The functional organization of excitation and inhibition in the dendrites of mouse direction-selective ganglion cells

Jain, V., Murphy‐Baum, B. L., deRosenroll, G., Sethuramanujam, S., Delsey, M., Delaney, K. R., & Awatramani, G. B. (2020) · eLife · doi:10.7554/elife.52949 · Cited by 37 · see citing works

Balanced Excitatory and Inhibitory Synaptic Currents Promote Efficient Coding and Metabolic Efficiency

Sengupta, B., Laughlin, S. B., & Niven, J. E. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003263 · Cited by 92 · see citing works

Synaptic E-I Balance Underlies Efficient Neural Coding

Zhou, S., & Yu, Y. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00046 · Cited by 166 · see citing works

Synaptic Excitatory-Inhibitory Balance Underlying Efficient Neural Coding

Zhou, S., & Yu, Y. (2018) · Advances in neurobiology · doi:10.1007/978-3-319-94593-4_5 · Cited by 4 · see citing works

How the layer-dependent ratio of excitatory to inhibitory cells shapes cortical coding in balanced networksPreprint

Alizadeh, A., Englitz, B., & Zeldenrust, F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.28.625852 · Cited by 3 · see citing works

Structure, dynamics, coding and optimal biophysical parameters of efficient excitatory-inhibitory spiking networksPreprint

Koren, V., Malerba, S. B., Schwalger, T., & Panzeri, S. (2024) · eLife · doi:10.7554/elife.99545.1