Research Interests
Theoretical Neuroscience
My lab's interests focus on understanding the cerebral cortex. We use theoretical and computational methods to unravel the circuitry of the cerebral cortex, the rules by which this circuitry develops or "self-organizes", and the computational functions of this circuitry. Our guiding hypothesis -- motivated by the stereotypical nature of cortical circuitry across sensory modalities and, with somewhat more variability, across motor and "higher-order" cortical areas as well -- is that there are fundamental computations done by the cortical circuit that are invariant across highly varying input signals. In some way that does not strongly depend on the specific content of the input, cortex extracts invariant structures from its input and learns to represent these structures in an associative, relational manner. We (and many others) believe the atomic element underlying these computations is likely to be found in the computations done by a roughly 1mm-square chunk of the cortical circuit. To understand this element, we have focused on one of the best-studied cortical systems, primary visual cortex, and also have interest in any cortical system in which the data gives us a foothold (including rodent whisker barrel cortex, studied here at Columbia by Randy Bruno; monkey area LIP, studied here by Mickey Goldberg, Jackie Gottlieb and Mike Shadlen; and the primate ventral visual stream, studied here by Elias Issa and Niko Kriegeskorte).
The function of this element depends both on its mature pattern of circuitry and on the developmental and learning rules by which this circuitry is shaped by the very inputs that it processes. Thus we focus both on understanding how the mature circuitry creates cortical response properties (see lab publications on Models of Neuronal Integration and Circuitry) and on how this circuitry is shaped by input activity during development and learning (see lab publications on Models of Neural Development).
While I was at UCSF, I also had an experimental component to my lab, focused on the study of neuronal responses in cat visual cortex and LGN (the nucleus providing visual input to cortex); see lab publications on Experimental Results.
Appointments at Columbia
College of Physicians and Surgeons
Peter Taylor Professor of Neuroscience
The Kavli Institute for Brain Science
Member
Center for Theoretical Neuroscience
Codirector
Doctoral Program in Neurobiology and Behavior
Codirector
Courses Taught
Introduction to Theoretical Neuroscience
G4360
Advanced Topics in Theoretical Neuroscience
G6040
Student Journal Club: Neural Circuits
G4990
Responsible Conduct of Research
G6001
Publications
Also see my google scholar page and the Center for Theoretical Neuroscience Publications.
Reverse Chronological Order, since 2006
-
Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability
Xia, J., Miller, K.D.
iScience 27:110512, 2024 -
The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations
Holt, C., Miller, K.D., Ahmadian, Y.
PLOS Comput. Biol. 20(6): e1012190, 2024 -
Unifying model for three forms of contextual modulation including feedback input from higher visual areas
Di Santo, S., Dipoppa, M., Keller, A., Roth, M., Scanziani, M., Miller, K.D.
bioRxiv. 2024. -
Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys.
*Sanzeni A., *Palmigiano A., *Nguyen T.H., Luo J., Nassi J.J., Reynolds J.H., Histed M.H., +Miller K.D., +Brunel N. (*,+: authors contributed equally)
Neuron 111:4102-4115, 2023. -
Mechanisms for spontaneous symmetry breaking in developing visual cortex
Fumarola, F., Hein, B., Miller, K.D.
Physical Review X 12, 031024, 2022. -
Divisive Feature Normalization Improves Image Recognition Performance in AlexNet
Miller, M., Chung, S.-Y., Miller, K.D.
International Conference on Learning Representations (ICLR) 2022 -
Antagonistic inhibitory subnetworks control cooperation and competition across cortical space
Mossing, D.P., Veit, J., Palmigiano, A., Miller, K.D., Adesnik, H.
bioRxiv, Dec. 2021 -
A human-specific modifier of cortical connectivity and circuit function
Schmidt, E.R.E., Zhou, H.T., Park, J.M., Dipoppa, M., Monsalve-Mercado, M., Dahan, J.B., Rodgers, C.C., Lejeune, A., Hillman, E.M.C., Miller, K.D., Bruno, R.M., Polleux, F.
Nature 599:640-644, October 2021 -
Learning of biased representations in LIP through interactions between recurrent connectivity and Hebbian plasticity
Zhang, W., Gottlieb, J., Miller, K.D.
bioRxiv. September 2021 -
What is the dynamical regime of cerebral cortex?
Ahmadian, Y., Miller, K.D.
Neuron. August 2021. -
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.
eLife. July 2021. -
Stabilized Supralinear Network: Model of Layer 2/3 of the Primary Visual Cortex
Obeid, D., Miller, K.D.
bioRxiv. January 2021. -
A disinhibitory circuit for contextual modulation in primary visual cortex
Keller, A.J., Dipoppa, M., Roth, M.M., Caudill, M.S., Ingrosso, A., Miller, K.D., Scanziani, M.
Neuron. December 2020. -
Structure and variability of optogenetic responses identify the operating regime of cortex
Palmigiano, A., Fumarola, F., Mossing, D.P., Kraynyukova, N., Adesnik, H., Miller, K.D.
bioRxiv. November 2020. PDF. -
Generalized paradoxical effects in excitatory/inhibitory networks
Miller, K.D., Palmigiano, A.
bioRxiv. October 2020. PDF. -
A unified circuit model of attention: Neural and behavioral effects
Lindsay, G.W., Rubin, D.W., Miller, K.D.
bioRxiv. July 2020. PDF. -
A deep learning framework for neuroscience
Richards, B.A., Lillicrap, T.P., Beaudoin, P. et al.
Nature Neuroscience. October 2019 -
Do biologically-realistic recurrent architectures produce biologically-realistic models?
Lindsay, G, Moskovitz, T., Yang, G.R., Miller, K.D.
Conference on Cognitive Computational Neuroscience. September 2019. PDF. -
Understanding the Functional and Structural Differences Across Excitatory and Inhibitory Neurons
Sun, M., Ji-An, L., Moskovitz, T., Lindsay, G., Miller, K., Dipoppa, M., Yang, G.R.
bioRxiv. June 2019. PDF. -
How biological attention mechanisms improve task performance in a large-scale visual system model
Lindsay, G.W., Miller, K.D.
eLife. October 2018. PDF. -
The dynamical regime of sensory cortex: Stable dynamics around a single stimulus-tuned attractor account for patterns of noise variability.
Hennequin, G., Ahmadian, Y., Rubin, D.B., Lengyel, M., Miller, K.D.
Neuron. April 2018. PDF. -
A Unifying Motif for Spatial and Directional Surround Suppression
Liu, L.D., Miller, K.D., Pack, C.C.
Journal of Neuroscience. January 2018. PDF. -
Coupling between One-Dimensional Networks Reconciles Conflicting Dynamics in LIP and Reveals Its Recurrent Circuitry
Zhang, W., Falkner, A.L., Krishna, B.S., Goldberg, M.E., Miller, K.D.
Neuron. January 2017. PDF. -
Parallel Processing by Cortical Inhibition Enables Context-Dependent Behavior
Kuchibhotla, K.V., Gill, J.V., Lindsay, G.W., Papadoyannis, E.S., Field, R.E., Hindmarsh Sten, T.A., Miller, K.D., Froemke, R.C.
Nature Neuroscience. October 2016 -
Canonical Computations of Cerebral Cortex
Miller, K.D.
Current Opinion in Neurobiology. April 2016 -
Stabilized supralinear network dynamics account for stimulus-induced changes of noise variability in the cortex
Hennequin, G., Ahmadian, Y., Rubin, D.B., Lengyel, M., Miller, K.D.
bioRxiv. December 2016. PDF. -
Neurons in Cat V1 Show Significant Clustering by Degree of Tuning
Ziskind, A.J., Emondi, A.A., Kurgansky, A.V., Rebrik, S.P., Miller, K.D.
Journal of Neurophysiology. April 2015. PDF. -
Properties of Networks with Partially Structured and Partially Random Connectivity
Ahmadian, Y., Fumarola, F., Miller, K.D.
Physical Review E. January 2015 -
The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex
Rubin, D.B., Van Hooser, S.D., Miller, K.D.
Neuron. January 2015. PDF. -
Modeling the dynamic interaction of Hebbian and homeostatic plasticity
Toyoizumi, T., Kaneko, M., Stryker, M.P., Miller, K.D.
Neuron. October 2014. PDF. -
The Effects of Short-Term Synaptic Depression at Thalamocortical Synapses on Orientation Tuning in Cat V1
Cimenser, A., Miller, K.D.
PLOS ONE. August 2014. PDF. -
Spatiotemporal receptive fields of barrel cortex revealed by reverse correlation of synaptic input
Ramirez, A., Pnevmatikakis, E.A., Merel, J., Paninski, L., Miller, K.D., Bruno, R.M.
Nature Neuroscience. May 2014. -
A Theory of the Transition to Critical Period Plasticity: Inhibition Selectively Suppresses Spontaneous Activity
Toyoizumi, T., Miyamoto, H., Yazaki-Sugiyama, Y., Atapour, N., Hensch, T.K., Miller, K.D.
Neuron. October 2013. PDF. -
Analysis of the Stabilized Supralinear Network
Ahmadian, Y., Rubin, D.B., Miller, K.D.
Neural Computation. June 2013. -
Mathematical Equivalence of Two Common Forms of Firing Rate Models of Neural Networks
Fumarola, F., Miller, K.D.
Neural Computation. January 2012. -
Learning unbelievable marginal probabilities
Pitkow, X., Ahmadian, Y., Miller, K.D.
arXiv. June 2011. PDF. -
Suppression of spontaneous activity by GABA circuit maturation is sufficient for developmental transitions in visual cortical plasticity
Toyoizumi, T., Miyamoto, H., Yazaki-Sugiyama, Y., Atapour, N., Hensch, T.K., Miller, K.D.
Neuroscience Research. 2011 -
π = Visual Cortex
Miller, K.D.
Science. November 2010. -
Maximally Reliable Markov Chains Under Energy Constraints
Escola, S., Eisele, M., Miller, K., Paninski, L.
Neural Computation. June 2009. PDF. -
Inhibitory Stabilization of the Cortical Network Underlies Visual Surround Suppression
Ozeki, H., Finn, I.M., Schaffer, E.S., Miller, K.D., Derster, D.
Neuron. May 2009. PDF. -
Equalization of Ocular Dominance Columns Induced by an Activity-Dependent Learning Rule and the Maturation of Inhibition
Toyoizumi, T., Miller, K.D.
Journal of Neuroscience. May 2009. PDF. -
Balanced Amplification: A New Mechanism of Selective Amplification of Neural Activity Patterns
Murphy, B.K., Miller, K.D.
Neuron. February 2009. PDF. -
On the Importance of Static Nonlinearity in Estimating Spatiotemporal Neural Filters With Natural Stimuli
Sharpee, T.O., Miller, K.D., Stryker, M.P
Journal of Neurophysiology. May 2008. PDF. -
One-Dimensional Dynamics of Attention and Decision Making in LIP
Ganguli, S., Bisley, J.W., Roitman, J.D., Shadlen, M.N., Goldberg, M.E., Miller, K.D.
Neuron. April 2008. PDF. -
Effects of Inhibitory Gain and Conductance Fluctuations in a Simple Model for Contrast-Invariant Orientation Tuning in Cat V1
Palmer, S.E., Miller, K.D.
Journal of Neurophysiology. July 2007. PDF. -
Adaptive filtering enhances information transmission in visual cortex
Sharpee, T.O., Sugihara, H., Kurgansky, A.V., Rebrik, S.P., Stryker, M.P., Miller, K.D.
Nature. February 2006.
-
What is the dynamical regime of cerebral cortex?
Ahmadian, Y., Miller, K.D.
arXiv. August 2019. PDF. -
Canonical Computations of Cerebral Cortex
Miller, K.D.
Current Opinion in Neurobiology. April 2016 -
Understanding Layer 4 of the Cortical Circuit: A Model Based on Cat VI
Miller, K.D.
Cerebral Cortex. January 2003. PDF. -
Processing in Layer 4 of the Neocortical Circuit: New Insights From Visual and Somatosensory Cortex
Miller, K.D., Simons, D.J., Pinto, D.J.
Current Opinion in Neurobiology. August 2001. PDF.
Note: this is the pdf as it appeared in Current Opinion in Neurobiology, copyright 2001 by Elsevier Science, provided here with permission from Elsevier Science. Single copies of this article may be downloaded and printed for the reader's personal research and study. -
Neural Mechanisms of Orientation Selectivity in the Visual Cortex
Ferster, D., Miller, K.D.
Annual Reviews of Neuroscience. 2000 -
Is the Development of Orientation Selectivity Instructed by Activity?
Miller, K.D., Erwin, E., Kayser, A.
Journal of Neurobiology. September 1999. -
Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns
Miller, K.D.
Models of Neural Networks III, Domany, E., van Hemmen, J.L., Schulten, K., Eds (Springer-Verlag, NY), pp. 55-78. 1995
-
Mechanisms for spontaneous symmetry breaking in developing visual cortex
Fumarola, F., Hein, B., Miller, K.D.
Physical Review X 12, 031024, 2022. -
Spontaneously broken symmetries from monotonic correlations in developing visual cortex
Fumarola, F., Hein, B., Miller, K.D.
arXiv. September 2021 -
Modeling the dynamic interaction of Hebbian and homeostatic plasticity
Toyoizumi, T., Kaneko, M., Stryker, M.P., Miller, K.D.
Neuron. October 2014. PDF. -
A Theory of the Transition to Critical Period Plasticity: Inhibition Selectively Suppresses Spontaneous Activity
Toyoizumi, T., Miyamoto, H., Yazaki-Sugiyama, Y., Atapour, N., Hensch, T.K., Miller, K.D.
Neuron. October 2013. PDF. -
Equalization of Ocular Dominance Columns Induced by an Activity-Dependent Learning Rule and the Maturation of Inhibition
Toyoizumi, T., Miller, K.D.
Journal of Neuroscience. May 2009. PDF. -
Opponent inhibition: A developmental model of layer 4 of the neocortical circuit
Kayser, A.S., Miller, K.D.
Neuron. January 2002. PDF. -
Effects of monocular deprivation and reverse suture on orientation maps can be explained by activity-instructed development of geniculocortical connections
Miller, K.D., Erwin, E.
Visual Neuroscience. September 2001. -
Competitive Hebbian Learning Through Spike-Timing-Dependent Synaptic Plasticity
Song, S., Miller, K.D., Abbott, L.F.
Nature Neuroscience. September 2000. -
Is the Development of Orientation Selectivity Instructed by Activity?
Miller, K.D., Erwin, E., Kayser, A.
Journal of Neurobiology. September 1999. -
The Subregion Correspondence Model of Binocular Simple Cells
Erwin, E., Miller, K.D.
Journal of Neuroscience. August 1999. PDF. -
Correlation-Based Development of Ocularly-Matched Orientation and Ocular Dominance Maps: Determination of Required Input Activities
Erwin, E., Miller, K.D.
Journal of Neuroscience. December 1998. PDF. -
Equivalence of a Sprouting-and-Retraction Model of Neural Development and Correlation-Based Rules with Subtractive Constraints
Miller, K.D.
Neural Computation. April 1998. -
Development of spatiotemporal receptive fields of simple cells: I. Model Formulation
Wimbauer, S., Wenisch, O.G., Miller, K.D., van Hemmen, J.L.
Biological Cybernetics. September 1997. -
Development of spatiotemporal receptive fields of simple cells: II. Simulation and Analysis
Wimbauer, S., Wenisch, O.G., Miller, K.D., van Hemmen, J.L.
Biological Cybernetics. September 1997. -
Synaptic Economics: Competition and Cooperation in Synaptic Plasticity
Miller, K.D.
Neuron. September 1996. PDF. -
An Associational Hypothesis for Sensorimotor Learning of Birdsong
Troyer, T.W., Doupe, A.J., Miller, K.D.
Computational Neuroscience: Trends in Research 1995, Bower, J.M., Ed. (Academic Press), pp. 409-414. 1996. -
Modeling Joint Development of Ocular Dominance and Orientation Maps in Primary Visual Cortex
Erwin, E., Miller, K.D.
Computational Neuroscience: Trends in Research 1995, Bower, J.M., Ed. (Academic Press), pp. 179-184. 1996. -
Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns
Miller, K.D.
Models of Neural Networks III, E. Domany, J.L. van Hemmen, and K. Schulten, Eds. (Springer-Verlag, NY), pp. 55--78. 1996. -
A Model for the Development of Simple Cell Receptive Fields and Orientation Columns Through Activity-Dependent Competition Between ON- and OFF-Center Inputs
Miller, K.D.
Journal of Neuroscience. January 1994. PDF. -
The Role of Constraints in Hebbian Learning
Miller, K.D., MacKay, D.J.C.
Neural Computations. January 1994. -
Models of activity-dependent neural development
Miller, K.D.
Seminars in the Neurosciences, Vol. 4, No. 1: Special Issue on The Use of Models in the Neurosciences. 1992. -
Analysis of Linsker's applications of Hebbian rules to linear networks
MacKay, D.J.C., Miller, K.D.
Network: Computation in Neural Systems. January 1990. -
Correlation-based models of neural development
Miller, K.D.
Neuroscience and Connectionist Theory, Gluck, M.A., Rumelhart, D.E. Eds. (Lawrence Erlbaum Associates, Hillsdale NJ), pp. 267-353. 1990. -
Derivation of Hebbian equations from a nonlinear model
Miller, K.D.
Neural Computations. 1990. -
Ocular dominance column formation: Mechanisms and models
Miller, K.D., Stryker, M.P.
Connectionist Modeling and Brain Function: The Developing Interface, Hanson, S.J., Olson, C.R., Eds. (MIT Press/Bradford), pp. 255-350. 1990. -
Correlation-Based Mechanisms in Visual Cortex: Theoretical and Experimental Studies
Miller, K.D.
Ph.D. Thesis, Stanford University, Program in Neurosciences. 1989. -
Ocular dominance column development: Analysis and simulation
Miller, K.D., Keller, J.B., Stryker, M.P.
Science. August 1989.
-
Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability
Xia, J., Miller, K.D.
iScience 27:110512, 2024 -
The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations
Holt, C., Miller, K.D., Ahmadian, Y.
PLOS Comput. Biol. 20(6): e1012190, 2024 -
Unifying model for three forms of contextual modulation including feedback input from higher visual areas
Di Santo, S., Dipoppa, M., Keller, A., Roth, M., Scanziani, M., Miller, K.D.
bioRxiv. 2024. -
Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys.
*Sanzeni A., *Palmigiano A., *Nguyen T.H., Luo J., Nassi J.J., Reynolds J.H., Histed M.H., +Miller K.D., +Brunel N. (*,+: authors contributed equally)
Neuron 111:4102-4115, 2023. -
Mechanisms for spontaneous symmetry breaking in developing visual cortex
Fumarola, F., Hein, B., Miller, K.D.
Physical Review X 12, 031024, 2022. -
Divisive Feature Normalization Improves Image Recognition Performance in AlexNet
Miller, M., Chung, S.-Y., Miller, K.D.
International Conference on Learning Representations (ICLR) 2022 -
Antagonistic inhibitory subnetworks control cooperation and competition across cortical space
Mossing, D.P., Veit, J., Palmigiano, A., Miller, K.D., Adesnik, H.
bioRxiv, Dec. 2021 -
A human-specific modifier of cortical connectivity and circuit function
Schmidt, E.R.E., Zhou, H.T., Park, J.M., Dipoppa, M., Monsalve-Mercado, M., Dahan, J.B., Rodgers, C.C., Lejeune, A., Hillman, E.M.C., Miller, K.D., Bruno, R.M., Polleux, F.
Nature 599:640-644, October 2021 -
Learning of biased representations in LIP through interactions between recurrent connectivity and Hebbian plasticity
Zhang, W., Gottlieb, J., Miller, K.D.
bioRxiv. September 2021 -
What is the dynamical regime of cerebral cortex?
Ahmadian, Y., Miller, K.D.
Neuron. August 2021. -
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.
eLife. July 2021. -
Stabilized Supralinear Network: Model of Layer 2/3 of the Primary Visual Cortex
Obeid, D., Miller, K.D.
bioRxiv. January 2021. -
A disinhibitory circuit for contextual modulation in primary visual cortex
Keller, A.J., Dipoppa, M., Roth, M.M., Caudill, M.S., Ingrosso, A., Miller, K.D., Scanziani, M.
Neuron. December 2020. -
Structure and variability of optogenetic responses identify the operating regime of cortex
Palmigiano, A., Fumarola, F., Mossing, D.P., Kraynyukova, N., Adesnik, H., Miller, K.D.
bioRxiv. November 2020. PDF. -
Generalized paradoxical effects in excitatory/inhibitory networks
Miller, K.D., Palmigiano, A.
bioRxiv. October 2020. PDF. -
A unified circuit model of attention: Neural and behavioral effects
Lindsay, G.W., Rubin, D.W., Miller, K.D.
bioRxiv. July 2020. PDF. -
A disinhibitory circuit for contextual modulation in primary visual cortex
Keller, A.J., Dipoppa, M., Roth, M.M., Caudill, M.S., Ingrosso, A., Miller, K.D., Scanziani, M.
bioRxiv. May 2020. PDF. -
A deep learning framework for neuroscience
Richards, B.A., Lillicrap, T.P., Beaudoin, P. et al.
Nature Neuroscience. October 2019 -
What is the dynamical regime of cerebral cortex?
Ahmadian, Y., Miller, K.D.
arXiv. August 2019. PDF. -
Do biologically-realistic recurrent architectures produce biologically-realistic models?
Lindsay, G, Moskovitz, T., Yang, G.R., Miller, K.D.
Conference on Cognitive Computational Neuroscience. September 2019. PDF. -
How biological attention mechanisms improve task performance in a large-scale visual system model
Lindsay, G.W., Miller, K.D.
eLife. October 2018. PDF. -
The dynamical regime of sensory cortex: Stable dynamics around a single stimulus-tuned attractor account for patterns of noise variability.
Hennequin, G., Ahmadian, Y., Rubin, D.B., Lengyel, M., Miller, K.D.
Neuron. April 2018. PDF. -
A Unifying Motif for Spatial and Directional Surround Suppression
Liu, L.D., Miller, K.D., Pack, C.C.
Journal of Neuroscience. January 2018. PDF. -
Coupling between One-Dimensional Networks Reconciles Conflicting Dynamics in LIP and Reveals Its Recurrent Circuitry
Zhang, W., Falkner, A.L., Krishna, B.S., Goldberg, M.E., Miller, K.D.
Neuron. January 2017. PDF. -
Parallel Processing by Cortical Inhibition Enables Context-Dependent Behavior
Kuchibhotla, K.V., Gill, J.V., Lindsay, G.W., Papadoyannis, E.S., Field, R.E., Hindmarsh Sten, T.A., Miller, K.D., Froemke, R.C.
Nature Neuroscience. October 2016 -
The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex
Rubin, D.B., Van Hooser, S.D., Miller, K.D.
Neuron. January 2015. PDF. -
Properties of Networks with Partially Structured and Partially Random Connectivity
Ahmadian, Y., Fumarola, F., Miller, K.D.
Physical Review E. January 2015 -
The Effects of Short-Term Synaptic Depression at Thalamocortical Synapses on Orientation Tuning in Cat V1
Cimenser, A., Miller, K.D.
PLOS ONE. August 2014. PDF. -
Analysis of the Stabilized Supralinear Network
Ahmadian, Y., Rubin, D.B., Miller, K.D.
Neural Computation. June 2013. -
Mathematical Equivalence of Two Common Forms of Firing Rate Models of Neural Networks
Fumarola, F., Miller, K.D.
Neural Computation. January 2012. -
Learning unbelievable marginal probabilities
Pitkow, X., Ahmadian, Y., Miller, K.D.
arXiv. June 2011. PDF. -
Inhibitory Stabilization of the Cortical Network Underlies Visual Surround Suppression
Ozeki, H., Finn, I.M., Schaffer, E.S., Miller, K.D., Derster, D.
Neuron. May 2009. PDF. -
Balanced Amplification: A New Mechanism of Selective Amplification of Neural Activity Patterns
Murphy, B.K., Miller, K.D.
Neuron. February 2009. PDF. -
Maximally Reliable Markov Chains Under Energy Constraints
Escola, S., Eisele, M., Miller, K., Paninski, L.
Neural Computation. June 2009. PDF. -
One-Dimensional Dynamics of Attention and Decision Making in LIP
Ganguli, S., Bisley, J.W., Roitman, J.D., Shadlen, M.N., Goldberg, M.E., Miller, K.D.
Neuron. April 2008. PDF. -
Effects of Inhibitory Gain and Conductance Fluctuations in a Simple Model for Contrast-Invariant Orientation Tuning in Cat V1
Palmer, S.E., Miller, K.D.
Journal of Neurophysiology. July 2007. PDF. -
Different roles for simple- and complex-cell inhibition in V1
Lauritzen, T.Z., Miller, K.D.
Journal of Neuroscience. November 2003. PDF. -
Multiplicative gain changes are induced by excitation or inhibition alone
Murphy, B.K., Miller, K.D.
Journal of Neuroscience. November 2003. PDF. -
LGN input to simple cells and contrast-invariant orientation tuning: An analysis
Troyer, T.W., Krukowski, A.E., Miller, K.D.
Journal of Neurophysiology. June 2002. PDF. -
Neural Noise Can Explain Expansive, Power-Law Nonlinearities in Neural Response Functions
Miller, K.D., Troyer, T.W.
Journal of Neurophysiology. February 2002. PDF. -
Opponent inhibition: A developmental model of layer 4 of the neocortical circuit
Kayser, A.S., Miller, K.D.
Neuron. January 2002. PDF. -
Local correlation-based circuitry can account for responses to multi-grating stimuli in a model of cat V1
Lauritzen, T.Z., Krukowski, A.E., Miller, K.D.
Journal of Neurophysiology. October 2001. PDF. -
Contrast-dependent nonlinearities arise locally in a model of contrast-invariant orientation tuning
Kayser, A., Priebe, N.J., Miller, K.D.
Journal of Neurophysiology. May 2001. PDF. -
Thalamocortical NMDA conductances and intracortical inhibition can explain cortical temporal tuning
Krukowski, A.E., Miller, K.D.
Nature Neuroscience. April 2001. -
Neural Mechanisms of Orientation Selectivity in the Visual Cortex
Ferster, D., Miller, K.D.
Annual Reviews of Neuroscience. 2000 -
The Subregion Correspondence Model of Binocular Simple Cells
Erwin, E., Miller, K.D.
Journal of Neuroscience. August 1999. PDF. -
Increased pyramidal neuronal excitability and enhanced NMDA conductance can account for post-traumatic epileptogenesis without disinhibition: a computational model
Bush, P.C., Prince, D.A., Miller, K.D.
Journal of Neurophysiology. October 1999. PDF. -
Contrast-Invariant Orientation Tuning in Visual Cortex: Thalamocortical Input Tuning and Correlation-Based Intracortical Connectivity
Troyer, T.W., Krukowski, A.E., Priebe, N.J., Miller, K.D.
Journal of Neuroscience. August 1998. PDF. -
Integrate-and-Fire Neurons Matched to Physiological F-I Curves Yield High Input Sensitivity and Wide Dynamic Range
Troyer, T.W., Miller, K.D.
Computational Neuroscience: Trends in Research 1997, Bower, J.M., Ed. (Plenum Press, NY), pp. 197-201. 1997. -
Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell
Troyer, T.W., Miller, K.D.
Neural Computation. July 1997. -
An Associational Hypothesis for Sensorimotor Learning of Birdsong
Troyer, T.W., Doupe, A.J., Miller, K.D.
Computational Neuroscience: Trends in Research 1995, Bower, J.M., Ed. (Academic Press), pp. 409-414. 1996.
-
Spatiotemporal receptive fields of barrel cortex revealed by reverse correlation of synaptic input
Ramirez, A., Pnevmatikakis, E.A., Merel, J., Paninski, L., Miller, K.D., Bruno, R.M.
Nature Neuroscience. May 2014. -
On the Importance of Static Nonlinearity in Estimating Spatiotemporal Neural Filters With Natural Stimuli
Sharpee, T.O., Miller, K.D., Stryker, M.P
Journal of Neurophysiology. May 2008. PDF. -
Adaptive filtering enhances information transmission in visual cortex
Sharpee, T.O., Sugihara, H., Kurgansky, A.V., Rebrik, S.P., Stryker, M.P., Miller, K.D.
Nature. February 2006. -
Tracking neurons recorded from tetrodes across time
Emondi, A.A., Rebrik, S.P., Kurgansky, A.V., Miller, K.D.
Journal of Neuroscience Methods. May 2004. -
Variability and information in a neural code of the cat lateral geniculate nucleus
Liu, R.C.,Tzonev, S., Rebrik, S., Miller, K.D.
Journal of Neurophysiology. December 2001. PDF. -
Cross Channel Correlations In Tetrode Recordings: Implications For Spike-Sorting
Wright, B.D., Rebrik, S., Emondi, A.A., Miller, K.D.
Neurocomputing. June 1999. -
Spike-Sorting of Tetrode Recordings in Cat LGN and Visual Cortex: Cross-Channel Correlations, Thresholds, and Automatic Methods
Wright, B.D., Rebrik, S., Miller, K.D.
Society for Neuroscience Abstracts. 1998 -
Analysis of Tetrode Recordings in Cat Visual System
Rebrik, S., Tzonev, S., Miller, K.D.
Proceedings of CNS97 (Computation and Neural Systems Meeting, Big Sky Montana, July 1997), Bower, J.M., Ed. (Plenum Press). 1998. -
Response Specificity of Lateral Geniculate Nucleus Neurons
Tzonev, S., Rebrik, S., Miller, K.D.
Society for Neuroscience Abstracts. 1997 -
Responses of cells in cat visual cortex depend on NMDA receptors
Miller, K.D., Chapman, B., Stryker, M.P.
PNAS. 1989.
Links of Interest
Links of Interest
Linear Algebra for Theoretical Neuroscience
These are some notes I've written to try to teach linear algebra and related aspects of linear differential equations to students of theoretical neuroscience. I've also included a nice set of notes written by Philip (Flip) Sabes of UCSF when we were co-teaching a course; these are best read after Part 3. Most neuroscience students seem to find they never make it through Part 4, which is on the Fourier transform -- too much detail and too little motivation -- but the rest seems to work reasonably well in getting the conceptual ideas across to motivated biologists who don't have much background. Part 4 stands alone, and can be omitted, but read it if you'd like to better understand the Fourier transform and in particular understand that it is just another coordinate transformation (a particular one that diagonalizes a particular set of matrices or linear operators and hence is used particularly often).
Although these notes work reasonably well -- particularly if they're used in or followed by a course in which the ideas are used in the context of real biological problems -- they also leave a lot to be desired. They need many more figures, many more neuroscience examples, and more and better problems. I'd also like to include an introductory chapter reminding people of basics of 1-dimensional linear differential equations and the exponential function, before heading into multiple dimensions (Jan. 2019: this has now been added, it is part 0). I'd like to include some chapters on probability, and in particular on Poisson and Gaussian distributions (and the linear algebra leads naturally to understanding multi-dimensional Gaussian distributions). At that point, it would probably become "Mathematics for Theoretical Neuroscience" rather than "Linear Algebra for Theoretical Neuroscience". Part 3, which deals with non-normal matrices -- matrices that do not have a complete orthonormal basis of eigenvectors -- needs to be completely rewritten: since it was written, I've learned that non-normal matrices have many features not predicted by the eigenvalues that are of great relevance in neurobiology and in biology more generally, and the notes don't deal with this (this is discussed in our paper Balanced amplification: A new mechanism of selective amplification of neural activity patterns, Neuron 61:635-648, and also in a paper by Mark Goldman in the same issue of Neuron; a beautiful book on the mathematical aspects is L.N. Trefethen and M. Embree, Spectra and Pseudospectra: The Behavior of Nonnormal Matrices and Operators. Princeton University Press, 2005). And Part 4 runs out of steam where I start talking about the connections between vectors and functions, matrices and linear operators, Kronecker deltas and Dirac deltas, and even more where it talks about multi-dimensional Fourier transforms. This all needs more work. Just haven't had the time. If you are interested in taking on any of these projects, particularly (but not limited to) figures, examples, or problems, or in adding other useful pieces of mathematics, let me know. Perhaps we can collaboratively build a useful resource.
All feedback on making these notes better will be appreciated. I'm not certain when I'll have time to implement them, but hope to.
I'd be very happy if you linked to this page. I'd prefer you link rather than posting the material yourself, both because (1) that way the creative commons license stays with the material and (2) that way people are always pointed to the latest versions, in case I should find the time to update. Here are the notes:
And here's Flip Sabes' notes on linear algebraic equations, SVD, and the pseudo-inverse:
Linear Algebra for Theoretical Neuroscience by Kenneth D. Miller is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Linear Algebraic Equations, SVD, and the Pseudo-Inverse by Phillip N. Sabes is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
See here.
See here.
- Marek Edelman's stunning account of the Warsaw Ghetto Uprising.
And another short but incredibly moving account of the Warsaw Ghetto. - Richard Feynmann on Cargo Cult Science: "... a kind of scientific integrity, a principle of scientific thought that corresponds to a kind of utter honesty--a kind of leaning over backwards...to show how you're maybe wrong, that you ought to have when acting as a scientist ..."
- [July 2011: The following references to the Bush administration are obviously old and getting older; alternatively, think "Fox News" or "Republicans". The main ideas below are ageless.]
Just a gentle reminder that the problems we face in the age of Bush are as old as the hills. The struggle for what is good and decent in the face of these forces is a never-ending one, perhaps nothing less than the human condition:- "Woe to those who make unjust laws, to those who issue oppressive decrees, to deprive the poor of their rights and withhold justice from the oppressed of my people, making widows their prey and robbing the fatherless."
Isaiah 10:1-2, The Bible, written around 700 B.C. - "To think of the future and wait was merely another way of saying one was a coward; any idea of moderation was just another attempt to disguise one's unmanly character; ability to understand the question from all sides meant that one was totally unfitted for action; fanatical enthusiasm was the mark of a real man... Anyone who held violent opinions could always be trusted, and anyone who objected to them became a suspect."
-- Thucydides, the Father of History, writing about the Greek Civil Wars of 427 B.C. (full source) - "Naturally, the common people don't want war; neither in Russia nor in England nor in America, nor for that matter in Germany. That is understood. But, after all, it is the leaders of the country who determine the policy and it is always a simple matter to drag the people along, whether it is a democracy or a fascist dictatorship or a Parliament or a Communist dictatorship ... the people can always be brought to the bidding of the leaders. That is easy. All you have to do is tell them they are being attacked and denounce the pacifists for lack of patriotism and exposing the country to danger. It works the same way in any country."
-- Herman Goering, Nazi leader, while being held in Nuremberg jail during the war crimes trials. (full source) - "There was no point in seeking to convert the intellectuals. For intellectuals would never be converted and would anyway always yield to the stronger, and this will always be `the man in the street.' Arguments must therefore be crude, clear and forcible, and appeal to emotions and instincts, not the intellect. Truth was unimportant and entirely subordinate to tactics and psychology."
-- Josef Goebbels, Nazi leader (quoted in this article on the process by which the US gov't made the decision to launch the war on Iraq and persuaded the country to follow; see also this excellent article on the same subject). - "`[Bush] was thinking about invading Iraq in 1999,' said author and journalist Mickey Herskowitz [KM: Herskowitz was working in 1999 as ghost-writer of Bush's autobiography and had around 20 meetings with him; he was later replaced]. `It was on his mind. He said to me: 'One of the keys to being seen as a great leader is to be seen as a commander-in-chief.' And he said, 'My father had all this political capital built up when he drove the Iraqis out of Kuwait and he wasted it.' He said, 'If I have a chance to invade....if I had that much capital, I'm not going to waste it. I'm going to get everything passed that I want to get passed and I'm going to have a successful presidency.''
...
"Bush and his advisers were sold on the idea that it was difficult for a president to accomplish an electoral agenda without the record-high approval numbers that accompany successful if modest wars."
(Source)
"First, we simply do not defeat an incumbent president in wartime. After wars surely, but never in their midst. Republicans have been spinning this fact for months, and they are correct."
-- Mark Mellman, Kerry pollster, in an analysis written two days before the Nov. 2004 election that accurately predicted Bush's vote to 0.1%.
(KM adds: and of course it's not just the war in Iraq. The key part of the strategy is to preside over an eternal and never-ending "War on Terror".)
- "Woe to those who make unjust laws, to those who issue oppressive decrees, to deprive the poor of their rights and withhold justice from the oppressed of my people, making widows their prey and robbing the fatherless."
See her website.
See my Op-Ed here.
Lab Members & Co-Conspirators
Lab Members & Co-Conspirators
All current Center for Theoretical Neuroscience members can be found on the People page.
All former Center for Theoretical Neuroscience members can be found on the Alumni page.
Page Last Updated: 10/20/2023
Contact Ken
Ken Miller
E-mail
[email protected]
Phone
212-853-1086
Twitter
@kendmil
Mailing Address:
Dept. of Neuroscience
3227 Broadway, L6-070
Mail Code 9864
New York, NY 10027
Office Address
Room 70, 6th Floor
Jerome Green Science Building
3227 Broadway
(between 129th and 130th on W. Side of Broadway; near 125th St. stop of 1 train.)