Kenneth Miller, PhD
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 — is that there are fundamental computations done by the circuits of sensory cortex that are invariant across highly varying input signals. This commonality is likely to extend in important ways to motor and "higher-order" cortex as well, although these structures show more prominent circuit differences with sensory cortex, consistent with their role in producing internally generated activity as well as in integrating their inputs. In some way that does not strongly depend on the specific content of the input, cortical circuits extract invariant structures from their input and learn 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 and Jackie Gottlieb).
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 and on how this circuitry is shaped by input activity during development and learning. We also use theoretical methods to analyze sensory cortical data to infer functional properties.
- PhD, Neuroscience, Stanford University
Member, The Kavli Institute for Brain Science
- 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.
- Ahmadian, Y., F. Fumarola and K.D. Miller (2015). Properties of networks with partially structured and partially random connectivity. Physical Review E 91:012820.
- 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.
- 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.
- Ahmadian, Y., D.B. Rubin and K.D. Miller (2013). Analysis of the stabilized supralinear network. Neural Computation 25:1994-2037.
- 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.
- 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.
- Murphy, B.K. and K.D. Miller (2009). Balanced amplification: A new mechanism of selective amplification of neural activity patterns. Neuron 61:635-648.
- 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.
- 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.
For a complete list of publications, please visit PubMed.gov