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
Recent Selected Publications
- What is the dynamical regime of cerebral cortex?
Ahmadian, Y. and K.D. Miller
arXiv:1908.10101 [q-bio.NC]. 2019
- How biological attention mechanisms improve task performance in a large-scale visual system model.
Lindsay, G.W. and K.D. Miller
eLife 7:e38105 DOI: 10.7554/eLife.38105. 2018
The dynamical regime of sensory cortex: Stable dynamics around a single stimulus-tuned attractor account for patterns of noise variability.
Hennequin, G., Y. Ahmadian, D.B. Rubin, M. Lengyel and K.D. Miller.
Neuron 98:846-860. 2018
- A unifying motif for spatial and directional surround suppression.
Liu, L.D. and K.D. Miller and C.C. Pack
J. Neurosci. 38:989-999. 2018
- Coupling between One-Dimensional Networks Reconciles Conflicting Dynamics in LIP and Reveals Its Recurrent Circuitry.
Zhang W, Falkner AL, Krishna BS, Goldberg ME, Miller KD
Neuron 93:221-234. 2017
- Parallel processing by cortical inhibition enables context-dependent behavior.
Kuchibhotla, K.V., J.V. Gill, G.W. Lindsay, E.S. Papadoyannis, R.E. Field, T.A. Sten, K.D. Miller and R.C. Froemke.
Nature Neurosci 20:62-71. 2017
- Canonical computations of cerebral cortex.
Curr Opin Neurobiol 37:75-84. 2016
- The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex.
Rubin DB, Van Hooser SD, Miller KD
Neuron 85:402-417. 2015
- Modeling the dynamic interaction of Hebbian and homeostatic plasticity.
Toyoizumi T, Kaneko M, Stryker MP, Miller KD
Neuron 84:497-510. 2014
- Analysis of the stabilized supralinear network.
Ahmadian, Y., D.B. Rubin, and K.D. Miller.
Neural Computation 25:1994-2037; arXiv:1202.6670 [q-bio.NC]. 2013
- A Theory of the Transition to Critical Period Plasticity: Inhibition Selectively Suppresses Spontaneous Activity.
Toyoizumi, T., H. Miyamoto, Y. Yazaki-Sugiyama, N. Atapour, T.K. Hensch and K.D. Miller.
Neuron 80:51-63. 2013.
- Balanced amplification: A new mechanism of selective amplification of neural activity patterns.
Murphy, B.K. and K.D. Miller.
Neuron 61:635-648. 2009.
- Inhibitory stabilization of the cortical network underlies visual surround suppression.
Ozeki H, Finn IM, Schaffer ES, Miller KD, Ferster D
Neuron 61:635-648. 2009
Links of Interest
- Math Notes
- Center for Theoretical Neuroscience
- Neurobiology and Behavior Ph.D. Program
- Neuroscience at Columbia
- A few links/thoughts on politics, history, scientific ethics ...
- If you're just getting started, check out this Talk by Uri Alon
- Melissa's beautiful paintings!
- My op-ed, "Will you ever be able to upload your brain?"
ken [at] neurotheory [dot] columbia [dot] edu
Dept. of Neuroscience
3227 Broadway, L6-070
Mail Code 9864
New York, NY 10027
Room 70, 6th Floor
Jerome Green Science Building
(between 129th and 130th on W. Side of Broadway; near 125th St. stop of 1 train.)