Kenneth D. Miller, Ph.D.

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 (such as rodent whisker barrel cortex, studied here at Columbia by Randy Bruno, and monkey area LIP, studied here by Mickey Goldberg, Jackie Gottlieb and Mike Shadlen).

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, below) and on how this circuitry is shaped by input activity during development and learning (see lab publications on Models of Neural Development, below).

While I was at UCSF, I also had an , focused on the study of the simultaneous activity of many neurons in visual cortex using the "tetrode" method of recording (see lab publications on Experimental Results, below). Experiments applied these methods in cat visual cortex and LGN (the nucleus providing visual input to cortex).


Listing of my publications on
Google scholar here.

(Below: some files are postscript or gzipped (.gz) postscript. Here's info on how to download and view these files.)

Pubs are organized in 5 overlapping categories:

Reverse Chronological Order, since 2006:

  • Ahmadian, Y. and K.D. Miller (2019). What is the dynamical regime of cerebral cortex? arXiv:1908.10101 [q-bio.NC]
    [there is a pdf of the article at the arXiv site]
  • Lindsay, G., T. Moskovitz, G.R. Yang, K.D. Miller (2019). Do biologically-realistic recurrent architectures produce biologically-realistic models? 2019 Conference on Cognitive Computational Neuroscience
    [pdf found at above link]
  • Lindsay, G.W. and K.D. Miller (2018). How biological visual attention mechanisms improve task performance in a large-scale visual system model. eLife 7:e38105.
    [pdf of article, without supplemental figures] [pdf of all figures, including supplemental figures]
  • Hennequin, G., Y. Ahmadian, D.B. Rubin, M. Lengyel and K.D. Miller (2018). The dynamical regime of sensory cortex: Stable dynamics around a single stimulus-tuned attractor account for patterns of noise variability. Neuron 98:846-860.
    [pdf, with supplement]
  • Liu, L.D., K.D. Miller and C.C. Pack (2018). A unifying motif for spatial and directional surround suppression. J. Neurosci:38:989-999.
  • Zhang, W., A.L. Falkner, B.S. Krishna, M.E. Goldberg and K.D. Miller (2017). Coupling between One-Dimensional Networks Reconciles Conflicting Dynamics in LIP and Reveals Its Recurrent Circuitry. Neuron 93:221-234.
  • 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 (2017). Parallel processing by cortical inhibition enables context-dependent behavior. Nature Neurosci 20:62-71.
    [article, pdf] [supplement, pdf]
  • Miller, K.D. (2016). Canonical computations of cerebral cortex. Current Opinion in Neurobiology 37:75-84.
  • Ziskind, A.J., A.A. Emondi, A.V. Kurgansky, S.P. Rebrik and K.D. Miller (2015). Neurons in cat V1 show significant clustering by degree of turning. Journal of Neurophysiology, In Press.
    [pdf] [unrefereed supplementary materials]
  • Rubin, D.B., S.D. Van Hooser and K.D. Miller (2015). The stabilized supralinear network: A unifying circuit motif underlying multi-input integration in sensory cortex. Neuron 85:402-417.
    [pdf (paper + supplement)]
  • Ahmadian, Y., F. Fumarola and K.D. Miller (2015). Properties of networks with partially structured and partially random connectivity. Physical Review E 91:012820; arXiv:1311.4672 [q-bio.NC]
    [pdf of PRE article] [arXiv site, from which you can download pdf]
  • Toyoizumi, T., M. Kaneko, M.P. Stryker and K.D. Miller (2014). Modeling the dynamic interaction of Hebbian and homeostatic plasticity. Neuron 84:497-510.
    [pdf (paper + supplement)]
  • Cimenser, A. and K.D. Miller (2014). The effects of short-term synaptic depression at thalamocortical synapses on orientation tuning in cat V1. PLOS One 9:e106046.
  • Ramirez, A., E.A. Pnevmatikakis, J. Merel, L. Paninski, K.D. Miller and R.M. Bruno (2014). Spatiotemporal receptive fields of barrel cortex revealed by reverse correlation of synaptic input. Nature Neuroscience 17:866-875.
    [pdf] [supplemental materials, pdf]
  • Toyoizumi, T., H. Miyamoto, Y. Yazaki-Sugiyama, N. Atapour, T.K. Hensch and K.D. Miller (2013). A Theory of the Transition to Critical Period Plasticity: Inhibition Selectively Suppresses Spontaneous Activity. Neuron 80:51-63.
    [pdf] [supplemental materials, pdf]
  • Ahmadian, Y., D.B. Rubin, and K.D. Miller (2013). Analysis of the stabilized supralinear network. Neural Computation 25:1994-2037; arXiv:1202.6670 [q-bio.NC]
    [arXiv site, from which you can download pdf] (the arxiv pdf is identical to the Neural Computation paper, except that it has all figures in color - 2 are b&w in neural computation.)
  • K.D. Miller and F. Fumarola (2012). Mathematical equivalence of two common forms of firing rate models of neural networks. Neural Computation 24:25-31.
    [pdf file]
  • Pitkow, X., Y. Ahmadian and K.D. Miller (2011). Learning unbelievable marginal probabilities. In Advances in Neural Information Processing Systems 24, J. Shawe-Taylor, R.S. Zemel, P. Bartlett, F. Pereira, and K.Q. Weinberger, Eds.; arXiv:1106.0483v1 [cs.AI].
    [pdf file]
  • Miller, K.D. (2010). π = Visual Cortex. Science 330:1059-1060. (News and views about this article)
    [pdf file]
  • Ozeki H., I.M. Finn, E.S. Schaffer, K.D. Miller and D. Ferster (2009). Inhibitory stabilization of the cortical network underlies visual surround suppression. Neuron 62:578-592.
    [pdf file] [supplemental materials, pdf]
  • Toyoizumi T. and Miller K.D. (2009). Equalization of ocular dominance columns induced by an activity-dependent learning rule and the maturation of inhibition. Journal of Neuroscience 29:6514-25.
    [pdf file]
  • Murphy, B.K. and K.D. Miller (2009). Balanced amplification: A new mechanism of selective amplification of neural activity patterns. Neuron 61:635-648.
    [pdf file] [corrected supplemental materials, pdf]
  • Escola, S., M. Eisele, K.D. Miller and L. Paninski (2009). Maximally reliable Markov chains under energy constraints. Neural Computation 21:1863-1912. [pdf file]
  • Ganguli, S., J.W. Bisley, J.D. Roitman, M.N. Shadlen, M.E. Goldberg, and K.D. Miller (2008). One-dimensional dynamics of attention and decision making in LIP. Neuron 58:15-25.
    [pdf file] [supplemental materials, pdf]
  • Sharpee, T.O., K.D. Miller KD and M.P. Stryker (2008). On the importance of the static nonlinearity in estimating spatiotemporal neural filters with natural stimuli. Journal of Neurophysiology 99:2496-2509. [pdf file]
  • Palmer, S.E. and K.D. Miller (2007). Effects of Inhibitory Gain and Conductance Fluctuations in a Simple Model for Contrast-Invariant Orientation Tuning in Cat V1. Journal of Neurophysiology 98: 63-78.
    [pdf file]
  • Sharpee, T.O., H. Sugihara, A.V. Kurgansky, S.P. Rebrik, M.P. Stryker and K.D. Miller (2006). Adaptive Filtering Enhances Information Transmission in Visual Cortex. Nature 439, 936-942.
    [pdf file] [web supplement, pdf file]
    (see also supplementary videos at Nature site).

    Some Reviews/Overviews:

    Models of Neural Development:

    If you're just getting started: here's a link to a
    guided tour through the papers related to models of visual cortical development.

    Models of Neuronal Integration and Circuitry:

    Experimental Results:

    How to view postscript and gzipped files

    (Note: these instructions are very old and I haven't checked to see that the links are current. If not, google should get you there.)

    Can't read postscript? Pick up ghostscript/ghostview; this link includes pointers to Mac and PC as well as Unix versions.

    To read compressed files: It's easy to install gzip/gunzip on your system:
    Click here to find Mac and Dos executables for gzip/gunzip, as well as source code that should compile on any Unix machine. Web browsers can be easily configured to automatically gunzip .gz files; talk to your system manager, or see Los Alamos faq, described below. Windows users: compressed (gzipped) files can also be unpacked with winzip.

    Terrific general information about getting started with postscript and gzip, including how to get your browser to automatically uncompress and display gzipped postscript, is here at the faq of the Los Alamos physics e-print archives.

    Guided tour of cortical development papers:

    If you wish to get started reading the papers on models of cortical development, I recommend the following path (for postscript files, I link here to the compressed versions; links to the uncompressed versions are also available, above):

    See Also: