Dynamic Excitatory and Inhibitory Gain Modulation Can Produce Flexible, Robust and Optimal Decision-making
Open accessAbstract
Behavioural and neurophysiological studies in primates have increasingly shown the involvement of urgency signals during the temporal integration of sensory evidence in perceptual decision-making. Neuronal correlates of such signals have been found in the parietal cortex, and in separate studies, demonstrated attention-induced gain modulation of both excitatory and inhibitory neurons. Although previous computational models of decision-making have incorporated gain modulation, their abstract forms do not permit an understanding of the contribution of inhibitory gain modulation. Thus, the effects of co-modulating both excitatory and inhibitory neuronal gains on decision-making dynamics and behavioural performance remain unclear. In this work, we incorporate time-dependent co-modulation of the gains of both excitatory and inhibitory neurons into our previous biologically based decision circuit model. We base our computational study in the context of two classic motion-discrimination tasks performed in animals. Our model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated. In particular, the model can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range. It also exhibits short-tailed reaction time distributions even when operating near a dynamical bifurcation point. The model further shows that neuronal gain modulation can compensate for weaker recurrent excitation in a decision neural circuit, and support decision formation and storage. Higher neuronal gain is also suggested in the more cognitively demanding reaction time than in the fixed delay version of the task. Using the exact temporal delays from the animal experiments, fast recruitment of gain co-modulation is shown to maximize reward rate, with a timescale that is surprisingly near the experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making.
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A cortical circuit model of decision-making indicates dynamic co-modulation of both excitatory and inhibitory gain produces a more stable and robust network, allowing more flexible and cognitively demanding decision-making, and gain modulation can compensate for weaker recurrent excitation.
“A cortical circuit model of decision-making indicates that dynamic co-modulation of both excitatory and inhibitory gain produces a more stable and robust network, allowing for more flexible and cognitively demanding decision-making, and that gain modulation can compensate for weaker recurrent excitation218.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [218]
Gain increases may optimize signal discrimination in presence of external noise by facilitating attractor dynamics and promoting a winner-take-all mechanism.
“Gain increases may optimize signal discrimination in the presence of external noise by facilitating attractor dynamics218 and promoting a winner-take-all mechanism219,220.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [218]
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Cites
On the Theory of the Brownian Motion
Uhlenbeck, G. E., & Ornstein, L. S. (1930) · Physical Review · doi:10.1103/physrev.36.823 · Cited by 4,618 · 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
An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations
Higham, D. J. (2001) · SIAM Review · doi:10.1137/s0036144500378302 · Cited by 3,203 · see citing works
Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering
Strogatz, S. H., & Fox, R. F. (1995) · Physics Today · doi:10.1063/1.2807947 · Cited by 2,584 · see citing works
(1986) · Cited by 2,022 · see citing works
Bogacz, R., Brown, E., Moehlis, J., Holmes, P. J., & Cohen, J. D. (2006) · Psychological Review · doi:10.1037/0033-295x.113.4.700 · Cited by 1,845 · see citing works
Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey
Shadlen, M. N., & Newsome, W. T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.4.1916 · Cited by 1,755 · see citing works
Synaptic depression and cortical gain control
Abbott, L. F., Varela, J. A., Sen, K., & Nelson, S. B. (1997) · Science · doi:10.1126/science.275.5297.221 · Cited by 1,690 · see citing works
Roitman, J. D., & Shadlen, M. N. (2002) · The Journal of Neuroscience · doi:10.1523/jneurosci.22-21-09475.2002 · Cited by 1,589 · cited 10× · see citing works
“(B,C) Accuracy (B) and mean RT (C) generated by model and in the experiment of [57].”
BackgroundMethodResultInfluentialNeural Mechanisms for Interacting with a World Full of Action Choices
Cisek, P., & Kalaska, J. F. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135409 · Cited by 1,543 · see citing works
Modeling Response Times for Two-Choice Decisions
Ratcliff, R., & Rouder, J. N. (1998) · Psychological Science · doi:10.1111/1467-9280.00067 · Cited by 1,478 · see citing works
Feature-based attention influences motion processing gain in macaque visual cortex
Treue, S., & Trujillo, J. C. M. (1999) · Nature · doi:10.1038/21176 · Cited by 1,465 · see citing works
The Normalization Model of Attention
Reynolds, J. H., & Heeger, D. J. (2009) · Neuron · doi:10.1016/j.neuron.2009.01.002 · Cited by 1,457 · see citing works
Correlated neuronal activity and the flow of neural information
Salinas, E., & Sejnowski, T. J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35086012 · Cited by 1,333 · see citing works
Intentional Maps in Posterior Parietal Cortex
Andersen, R. A., & Buneo, C. A. (2002) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.25.112701.142922 · Cited by 1,294 · cited 4× · see citing works
“ts (NMDA-mediated) synaptic gating variable time constant 100 ms [1,2,3,4]”
BackgroundInfluentialSimulating, Analyzing, and Animating Dynamical Systems
Ermentrout, B. (2002) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898718195 · Cited by 1,271 · see citing works
Probabilistic decision making by slow reverberation in cortical circuits
Wang, X.-J. (2002) · Neuron · doi:10.1016/s0896-6273(02)01092-9 · Cited by 1,242 · see citing works
Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4
McAdams, C. J., & Maunsell, J. H. R. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-01-00431.1999 · Cited by 1,214 · see citing works
Attentional modulation of visual processing
Reynolds, J. H., & Chelazzi, L. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131039 · Cited by 1,158 · see citing works
Amit, D. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.3.237 · Cited by 1,061 · see citing works
Attention, intention, and priority in the parietal lobe
Bisley, J. W., & Goldberg, M. E. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-060909-152823 · Cited by 1,043 · see citing works
A Recurrent Network Mechanism of Time Integration in Perceptual Decisions
Wong, K.-F., & Wang, X.-J. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3733-05.2006 · Cited by 1,029 · see citing works
Gain Modulation from Background Synaptic Input
Chance, F. S., Abbott, L. F., & Reyes, A. D. (2002) · Neuron · doi:10.1016/s0896-6273(02)00820-6 · Cited by 1,012 · see citing works
On the theory of brownian motion
Mazur, P. (1959) · Physica · doi:10.1016/s0031-8914(59)91931-7 · Cited by 933 · see citing works
The neural systems that mediate human perceptual decision making
Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008) · Nature reviews. Neuroscience · doi:10.1038/nrn2374 · Cited by 877 · see citing works
Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory
Wang, X.-J. (1999) · The Journal of Neuroscience · doi:10.1523/jneurosci.19-21-09587.1999 · Cited by 859 · see citing works
A Network Model of Catecholamine Effects: Gain, Signal-to-Noise Ratio, and Behavior
Servan‐Schreiber, D., Printz, H., & Cohen, J. D. (1990) · Science · doi:10.1126/science.2392679 · Cited by 770 · see citing works
The neural basis of the speed–accuracy tradeoff
Bogacz, R., Wagenmakers, E., Forstmann, B. U., & Nieuwenhuis, S. (2009) · Trends in Neurosciences · doi:10.1016/j.tins.2009.09.002 · Cited by 721 · see citing works
Intention, Action Planning, and Decision Making in Parietal-Frontal Circuits
Andersen, R. A., & Cui, H. (2009) · Neuron · doi:10.1016/j.neuron.2009.08.028 · Cited by 707 · see citing works
Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making
Frank, M. J. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.03.006 · Cited by 706 · see citing works
Decision-making with multiple alternatives
Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008) · Nature Neuroscience · doi:10.1038/nn.2123 · Cited by 671 · see citing works
Differential attention-dependent response modulation across cell classes in macaque visual area V4
Mitchell, J. F., Sundberg, K. A., & Reynolds, J. H. (2007) · Neuron · doi:10.1016/j.neuron.2007.06.018 · Cited by 665 · see citing works
Doya, K. (2008) · Nature Neuroscience · doi:10.1038/nn2077 · Cited by 642 · see citing works
Shunting Inhibition Modulates Neuronal Gain during Synaptic Excitation
Mitchell, S. J., & Silver, R. A. (2003) · Neuron · doi:10.1016/s0896-6273(03)00200-9 · Cited by 602 · see citing works
Andersen, R., & Mountcastle, V. B. (1983) · Journal of Neuroscience · doi:10.1523/jneurosci.03-03-00532.1983 · Cited by 594 · see citing works
Responses of neurons in macaque MT to stochastic motion signals
Britten, K. H., Shadlen, M. N., Newsome, W. T., & Movshon, J. A. (1993) · Visual Neuroscience · doi:10.1017/s0952523800010269 · Cited by 594 · see citing works
The Cost of Accumulating Evidence in Perceptual Decision Making
Drugowitsch, J., Moreno-Bote, R., Churchland, A. K., Shadlen, M. N., & Pouget, A. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.4010-11.2012 · Cited by 577 · see citing works
Huk, A. C., & Shadlen, M. N. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.4684-04.2005 · Cited by 538 · 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
Decisions in Changing Conditions: The Urgency-Gating Model
Cisek, P., Puskas, G. A., & El-Murr, S. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1844-09.2009 · Cited by 521 · see citing works
Cortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks
Lo, C.-C., & Wang, X.-J. (2006) · Nature Neuroscience · doi:10.1038/nn1722 · Cited by 505 · see citing works
Kiani, R., Hanks, T. D., & Shadlen, M. N. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4761-07.2008 · Cited by 500 · see citing works
From Thought to Action: The Parietal Cortex as a Bridge between Perception, Action, and Cognition
Gottlieb, J. P. (2007) · Neuron · doi:10.1016/j.neuron.2006.12.009 · Cited by 499 · see citing works
Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models
Salinas, E., & Sejnowski, T. J. (2000) · The Journal of Neuroscience · doi:10.1523/jneurosci.20-16-06193.2000 · Cited by 472 · see citing works
The influence of urgency on decision time
Reddi, B. A. J., & Carpenter, R. H. S. (2000) · Nature Neuroscience · doi:10.1038/77739 · Cited by 411 · see citing works
Salinas, E., & Thier, P. (2000) · Neuron · doi:10.1016/s0896-6273(00)00004-0 · Cited by 400 · see citing works
Ratcliff, R., Cherian, A., & Segraves, M. A. (2003) · Journal of Neurophysiology · doi:10.1152/jn.01049.2002 · Cited by 374 · see citing works
Cisek, P. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.5605-05.2006 · Cited by 344 · see citing works
A model of multiplicative neural responses in parietal cortex.
Salinas, E., & Abbott, L. F. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.21.11956 · Cited by 319 · see citing works
An integrated theory of attention and decision making in visual signal detection.
Smith, P. L., & Ratcliff, R. (2009) · Psychology Review · doi:10.1037/a0015156 · Cited by 315 · see citing works
Barrages of Synaptic Activity Control the Gain and Sensitivity of Cortical Neurons
Shu, Y., Hasenstaub, A., Badoual, M., Bal, T., & McCormick, D. A. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-32-10388.2003 · Cited by 298 · see citing works
Attentional modulation in visual cortex depends on task timing
Ghose, G. M., & Maunsell, J. H. R. (2002) · Nature · doi:10.1038/nature01057 · Cited by 297 · see citing works
Decision making by urgency gating: theory and experimental support
Thura, D., Beauregard-Racine, J., Fradet, C.-W., & Cisek, P. (2012) · Journal of Neurophysiology · doi:10.1152/jn.01071.2011 · Cited by 267 · see citing works
Salinas, E., & B, T. J. S. A. (2001) · The Neuroscientist · doi:10.1177/107385840100700512 · Cited by 264 · see citing works
Dynamics of a recurrent network of spiking neurons before and following learning
Amit, D. J., & Brunel, N. (1997) · Network Computation in Neural Systems · doi:10.1088/0954-898x_8_4_003 · Cited by 241 · see citing works
Stochastic models of decisions about motion direction: Behavior and physiology
Ditterich, J. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.05.042 · Cited by 233 · see citing works
Evidence for time‐variant decision making
Ditterich, J. (2006) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2006.05221.x · Cited by 229 · see citing works
Perceptual decision making in less than 30 milliseconds
Stanford, T. R., Shankar, S., Massoglia, D. P., Costello, M. G., & Salinas, E. (2010) · Nature Neuroscience · doi:10.1038/nn.2485 · Cited by 228 · see citing works
Gain control of firing rate by shunting inhibition: Roles of synaptic noise and dendritic saturation
Prescott, S. A., & De Koninck, Y. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0337591100 · Cited by 226 · see citing works
Wong, K.-F. (2007) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.006.2007 · Cited by 204 · see citing works
Gutkin, B. S., Laing, C. R., Colby, C. L., Chow, C. C., & Ermentrout, B. (2001) · Journal of Computational Neuroscience · doi:10.1023/a:1012837415096 · Cited by 172 · see citing works
Acquisition of decision making criteria: reward rate ultimately beats accuracy
Balcı, F., Simen, P., Niyogi, R. K., Saxe, A., Hughes, J. A., Holmes, P. J., & Cohen, J. D. (2010) · Attention Perception & Psychophysics · doi:10.3758/s13414-010-0049-7 · Cited by 165 · see citing works
Self-citationInhibitory synchrony as a mechanism for attentional gain modulation
Tiesinga, P. H. E., Fellous, J., Salinas, E., José, J. V., & B, T. J. S. A. (2004) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2005.09.002 · Cited by 157 · see citing works
Drivers and modulators from push-pull and balanced synaptic input
Abbott, L. F., & Chance, F. S. (2005) · Progress in brain research · doi:10.1016/s0079-6123(05)49011-1 · Cited by 153 · see citing works
Gail, A., & Andersen, R. A. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.1570-06.2006 · Cited by 146 · see citing works
Multiplicative Gain Changes Are Induced by Excitation or Inhibition Alone
Murphy, B. K., & Miller, K. D. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-31-10040.2003 · Cited by 144 · see citing works
Similarity Effect and Optimal Control of Multiple-Choice Decision Making
Furman, M., & Wang, X. (2008) · Neuron · doi:10.1016/j.neuron.2008.12.003 · Cited by 143 · see citing works
Meister, M. L. R., Hennig, J. A., & Huk, A. C. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2984-12.2013 · Cited by 143 · see citing works
Smith, P. L., Ratcliff, R., & Wolfgang, B. J. (2004) · Vision Research · doi:10.1016/j.visres.2004.01.002 · Cited by 141 · see citing works
Attention as a decision in information space
Gottlieb, J. P., & Balan, P. F. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.03.001 · Cited by 125 · see citing works
Higgs, M. H., Slee, S. J., & Spain, W. J. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.1792-06.2006 · Cited by 124 · see citing works
Rapid decision threshold modulation by reward rate in a neural network
Simen, P., Cohen, J. D., & Holmes, P. J. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.05.038 · Cited by 116 · see citing works
Subtractive and Divisive Inhibition: Effect of Voltage-Dependent Inhibitory Conductances and Noise
Doiron, B., Longtin, A., Berman, N. J., & Maler, L. (2001) · Neural Computation · doi:10.1162/089976601300014691 · Cited by 110 · see citing works
Can Monkeys Choose Optimally When Faced with Noisy Stimuli and Unequal Rewards?
Feng, S. F., Holmes, P. J., Rorie, A. E., & Newsome, W. T. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000284 · Cited by 105 · see citing works
An Integrated Microcircuit Model of Attentional Processing in the Neocortex
Ardid, S., Wang, X., & Compte, A. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1145-07.2007 · Cited by 105 · see citing works
SIMPLE NEURAL NETWORKS THAT OPTIMIZE DECISIONS
Brown, E., Gao, J., Holmes, P. J., Bogacz, R., Gilzenrat, M. S., & Cohen, J. D. (2005) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127405012478 · Cited by 101 · see citing works
Fast Remapping of Sensory Stimuli onto Motor Actions on the Basis of Contextual Modulation
Salinas, E. (2004) · The Journal of Neuroscience · doi:10.1523/jneurosci.4569-03.2004 · Cited by 77 · see citing works
Arsiero, M., Lüscher, H.-R., Lundstrom, B. N., & Giugliano, M. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.4937-06.2007 · Cited by 75 · see citing works
Compte, A., & Wang, X. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhj021 · Cited by 71 · see citing works
A Fluctuation-Driven Mechanism for Slow Decision Processes in Reverberant Networks
Marti, D. N., Deco, G., Mattia, M., Gigante, G., & Giudice, P. D. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002534 · Cited by 68 · see citing works
Optimality and Robustness of a Biophysical Decision-Making Model under Norepinephrine Modulation
Eckhoff, P. A., Wong‐Lin, K., & Holmes, P. J. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5024-08.2009 · Cited by 67 · see citing works
Self-citationThe encoding of alternatives in multiple-choice decision making
Albantakis, L., & Deco, G. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901621106 · Cited by 65 · see citing works
Standage, D., You, H., Wang, D., & Dorris, M. C. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00007 · Cited by 65 · see citing works
Temporal dynamics of decision-making during motion perception in the visual cortex
Grossberg, S., & Pilly, P. K. (2008) · Vision Research · doi:10.1016/j.visres.2008.02.019 · Cited by 65 · see citing works
Herrington, T. M., & Assad, J. A. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.6025-09.2010 · Cited by 63 · see citing works
Local Computation of Decision-Relevant Net Sensory Evidence in Parietal Cortex
Bollimunta, A., & Ditterich, J. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr165 · Cited by 50 · see citing works
Gain Control in CA1 Pyramidal Cells Using Changes in Somatic Conductance
Fernandez, F. R., & White, J. A. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.3995-09.2010 · Cited by 47 · see citing works
Feedback-induced gain control in stochastic spiking networks
Sutherland, C., Doiron, B., & Longtin, A. (2009) · Biological Cybernetics · doi:10.1007/s00422-009-0298-5 · Cited by 42 · see citing works
Mechanism of gain modulation at single neuron and network levels
Brozović, M., Abbott, L. F., & Andersen, R. (2008) · Journal of Computational Neuroscience · doi:10.1007/s10827-007-0070-6 · Cited by 31 · see citing works
Eckhoff, P. A., Wong‐Lin, K., & Holmes, P. J. (2011) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/090770096 · Cited by 28 · see citing works
Self-citationA Common Cortical Circuit Mechanism for Perceptual Categorical Discrimination and Veridical Judgment
Liu, F., & Wang, X. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000253 · Cited by 28 · see citing works
Doya, K., & Shadlen, M. N. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.10.003 · Cited by 19 · see citing works
Closed-Form Approximations of First-Passage Distributions for a Stochastic Decision-Making Model
Broderick, T., Wong‐Lin, K., & Holmes, P. J. (2010) · Applied Mathematics Research eXpress · doi:10.1093/amrx/abp008 · Cited by 16 · see citing works
Self-citationBayesian decision making in two-alternative forced choices
Denève, S. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374620-7.00021-2 · Cited by 5 · see citing works
Time-varying gain modulation on neural circuit dynamics and performance in perceptual decisions
Wong‐Lin, K. (2010) · Frontiers in Neuroscience · doi:10.3389/conf.fnins.2010.03.00132 · Cited by 3 · see citing works
Self-citationNiyogi, R. K. (2009) · Cited by 1 · see citing works
Self-citationWong‐Lin, K. (2009) · Cited by 1 · see citing works
Self-citation
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Research works printed but not linked: 1
- [5]Shadlen MN, Gold JI (2004) The neurophysiology of decision-making as a window on cognition. In: Gazzaniga MS, editor, The Cognitive Neurosciences, 3rd edition. MIT Press.
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Mechanisms underlying gain modulation in the cortex
Ferguson, K. A., & Cardin, J. A. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0253-y · Cited by 327 · see citing works
- Authors
- Niyogi, R. K., & Wong-Lin, K.
- Affiliations
- Gatsby Computational Neuroscience Unit (GB)
- Corresponding author
- Niyogi, Ritwik K.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-06-27
- Volume
- 9
- Issue
- 6
- Pages
- e1003099
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3694816
- OpenAlex
- W2037631770
- Cited by
- 47
- References
- 97
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 3 · 2023: 4 · 2022: 2 · 2021: 6 · 2020: 4 · 2019: 5 · 2018: 7 · 2017: 1 · 2016: 2 · 2015: 6
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Ulster University Research Portal (Ulster University) (acceptedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Princeton University
- Gatsby Charitable Foundation
- National Institute of Mental Health (MH62196, P50MH062196)
- Air Force Research Laboratory (FA9550-07-1-0537, FA9550)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- perceptual decision making, gain modulation, excitatory and inhibitory neurons, winner-take-all dynamics, motion discrimination, reaction time distribution, dynamical bifurcation, recurrent excitation, parietal cortex
- MeSH terms
- Decision Making, Humans, Models, Theoretical, Reaction Time, Task Performance and Analysis
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