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).

(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
- Some Reviews/Overviews
- Models of Neural Development
- Models of Neuronal Integration and Circuitry
- Experimental Results

[there is a pdf of the article at the arXiv site]

[pdf found at above link]

[pdf of article, without supplemental figures] [pdf of all figures, including supplemental figures]

[pdf, with supplement]

[pdf]

[pdf]

[article, pdf] [supplement, pdf]

[pdf]

[pdf] [unrefereed supplementary materials]

[pdf (paper + supplement)]

[pdf of PRE article] [arXiv site, from which you can download pdf]

[pdf (paper + supplement)]

[pdf]

[pdf] [supplemental materials, pdf]

[pdf] [supplemental materials, pdf]

[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.)

[pdf file]

[pdf file]

[pdf file]

[pdf file] [supplemental materials, pdf]

[pdf file]

[pdf file] [corrected supplemental materials, pdf]

[pdf file] [supplemental materials, pdf]

[pdf file]

[pdf file] [web supplement, pdf file]

(see also supplementary videos at Nature site).

- 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] - Miller, K.D. (2016).
*Canonical computations of cerebral cortex.*Current Opinion in Neurobiology 37:75-84.

[pdf] - Miller, K.D. (2003).
*Understanding Layer 4 of the Cortical Circuit: A Model Based on Cat V1*. Cerebral Cortex 13, 73-82.

[pdf file]

A review of our modeling of the circuitry of layer 4 (the input-recipient layer) of cat V1 and of related experiments, and of the implications for understanding cortical layer 4 more generally. This is an updated version of the portions of the Miller, Simons and Pinto (2001) review dealing with V1, but without the discussion found in that review of somatosensory cortex and the parallels between the two. - Miller, K.D., D.J. Simons and D.J. Pinto (2001).
*Processing in Layer 4 of the Neocortical Circuit: New Insights From Visual and Somatosensory Cortex*. Current Opinion in Neurobiology 11, 488-497.

[pdf file]

A review of common features of layer 4 (the input-recipient layer) of visual and somatosensory cortex, focusing on the role of strong feedforward inhibition.

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. - Ferster, D. and K.D. Miller (2000).
*Neural Mechanisms of Orientation Selectivity in the Visual Cortex*. Annual Reviews of Neuroscience 23, 441-471.

[pdf file (0.3 MB)] [compressed postscript (0.4 MB)] [view on web (HTML)]

A review of experimental findings and modeling, including our own modeling, characterizing the mature circuitry underlying orientation-tuned responses. (See papers below on Models of Neuronal Integration and Circuitry). - Miller, K.D., E. Erwin and A. Kayser (1999).
*Is the Development of Orientation Selectivity Instructed by Activity?*. Journal of Neurobiology 41, 44-57.

[pdf file (0.4 MB)] [compressed postscript (1.1 MB)]

An overview of our modeling of the development of orientation selectivity and related experimental findings. (See papers below on Models of Neural Development). - Miller, K.D. (1996a).
*Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns.*In*Models of Neural Networks III,*E. Domany, J.L. van Hemmen, and K. Schulten, Eds. (Springer-Verlag, NY), pp. 55--78.

[compressed postscript] [postscript]

An earlier review of our developmental modeling, presenting more details of the models and covering ocular dominance as well as orientation, but not covering our more recent results, e.g. those in the Erwin and Miller and Kayser and Miller papers below (Models of Neural Development).

- 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)] - 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] - 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] - Kayser, A.S. and K.D. Miller (2002).
*Opponent inhibition: A developmental model of layer 4 of the neocortical circuit*. Neuron 33, 131-142.

[pdf file (3 MB)] - Miller, K.D. and E. Erwin (2001).
*Effects of monocular deprivation and reverse suture on orientation maps can be explained by activity-instructed development of geniculocortical connections*. Visual Neuroscience 18, 821-834.

[postscript file (4 MB)] [compressed postscript (1.2 MB)] - Song, S., K.D. Miller and L.F. Abbott (2000).
*Competitive Hebbian Learning Through Spike-Timing-Dependent Synaptic Plasticity*. Nature Neuroscience 3, 919-926.

[postscript file (0.9 MB)] [compressed postscript (0.2 MB)] - Miller, K.D., E. Erwin and A. Kayser (1999).
*Is the Development of Orientation Selectivity Instructed by Activity?*. Journal of Neurobiology 41, 44-57.

[pdf file (0.4 MB)] [compressed postscript (1.1 MB)] - Erwin, E. and K.D. Miller (1999).
*The subregion correspondence model of binocular simple cells.*Journal of Neuroscience 19, 7212-7229.

[compressed postscript (0.9 MB)] [postscript (8.4 MB)] - Erwin, E. and K.D. Miller (1998).
*Correlation-Based Development of Ocularly-Matched Orientation and Ocular Dominance Maps: Determination of Required Input Activities.*Journal of Neuroscience 18, 9870-9895.

[compressed postscript (0.8 MB)] [postscript (3 MB)] - Miller, K.D. (1998).
*Equivalence of a Sprouting-and-Retraction Model of Neural Development and Correlation-Based Rules with Subtractive Constraints.*Neural Computation 10, 528-547.

[compressed postscript] [postscript] - Wimbauer, S., O.G. Wenisch, K.D. Miller and J.L. van Hemmen
(1997a).
*Development of spatiotemporal receptive fields of simple cells: I. Model Formulation*. Biological Cybernetics 77, 453-461.

[Biological Cybernetics Web site: abstract and pdf file]

[compressed postscript] [postscript] - Wimbauer, S., O.G. Wenisch, J.L. van Hemmen and K.D. Miller
(1997b).
*Development of spatiotemporal receptive fields of simple cells: II. Simulation and Analysis*. Biological Cybernetics 77, 463-477.

[Biological Cybernetics Web site: abstract and pdf file]

[compressed postscript (0.4 MB)] [postscript (2.7 MB)] - Miller,
K.D. (1996b).
*Synaptic Economics: Competition and Cooperation in Synaptic Plasticity.*Neuron 17, 367-370.

[compressed postscript] [postscript]

Warning: corrections in proof were not made by the printer, so there are an obvious error and some funky sentences in the published version; these are corrected in above net version, which also has some additional material. See also Neuron online version of corrected manuscript. - Troyer, T.W., A.J. Doupe and K.D. Miller (1996).
*An Associational Hypothesis for Sensorimotor Learning of Birdsong*. In*Computational Neuroscience: Trends in Research 1995,*J.M. Bower, Ed. (Academic Press), pp. 409-414.

[compressed postscript] [postscript] - Erwin,
E. and K.D. Miller (1996).
*Modeling Joint Development of Ocular Dominance and Orientation Maps in Primary Visual Cortex.*In*Computational Neuroscience: Trends in Research 1995,*J.M. Bower, Ed. (Academic Press), pp. 179-184.

[compressed postscript] [postscript] - Miller, K.D. (1996a).
*Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns.*In*Models of Neural Networks III,*E. Domany, J.L. van Hemmen, and K. Schulten, Eds. (Springer-Verlag, NY), pp. 55--78.

[compressed postscript] [postscript]

A shorter version of this was published as Miller, K.D. (1995).*Ocular Dominance and Orientation Columns.*in*The Handbook of Brain Theory and Neural Networks*, M.A. Arbib, Ed. (MIT Press, Cambridge MA), pp. 660-665. - Miller, K.D (1994).
*A Model for the Development of Simple Cell Receptive Fields and Orientation Columns Through Activity-Dependent Competition Between ON- and OFF-Center Inputs.*Journal of Neuroscience 14, 409-441.

[pdf (12.3 Mbytes)]

Here is a text-only version: [compressed postscript (176 Kbytes)] [postscript (488 Kbyes)]

Here are most of the figures (tar file of gzip-ed (compressed) postscript files: 900 Kbytes, files gunzip to 17 Mbytes). Here are the remaining figures (tar file of gzipped scanned versions of three figures -- 1.6 Mbytes, files gunzip to 2.8 Mbytes). - Miller, K.D. and D.J.C. MacKay (1994).
*The Role of Constraints in Hebbian Learning,*Neural Computation 6, 100-126.

[pdf (scanned, 0.8 MB)]

[compressed postscript] [postscript]

Here's a 1992 Caltech Tech Report version of this paper:

[compressed postscript] [postscript]

The 1992 version is longer, not as well written, but includes some extra topics. - Miller, K.D., Editor (1992). Seminars in the Neurosciences, Vol. 4,
No. 1: Special Issue on
*The Use of Models in the Neurosciences*. - Miller, K.D. (1992).
*Development of Orientation Columns Via Competition Between ON- and OFF-Center Inputs.*NeuroReport 3, 73-76.

[pdf (scanned, 4.3 MB)] - MacKay, D.J.C. and K.D. Miller (1990a).
*Analysis of Linsker's applications of Hebbian rules to linear networks,*Network 1, 257-298.

[pdf (scanned, 9 MB)]

Here is a text-only version: [compressed postscript] [postscript]

Here are the figures (tar file of gzip-ed tiff or postscript files). - MacKay, D.J.C. and K.D. Miller (1990b).
*Analysis of Linsker's simulations of Hebbian rules,*Neural Computation 2, 169-182 (this is a short preliminary version, the full paper is the above Network paper).

[pdf (scanned, 1 MB)] - Miller, K.D. (1990a).
*Correlation-based models of neural development,*in Neuroscience and Connectionist Theory, M.A. Gluck and D.E. Rumelhart, Eds. (Lawrence Erlbaum Associates, Hillsdale NJ), pp. 267-353.

[pdf (scanned, 14 MB)]

Here is a text-only version (tar file of gzipped postscript files). Figures may be available here in the future. - Miller, K.D. (1990b).
*Derivation of Hebbian equations from a nonlinear model,*Neural Computation 2, 319-331.

[compressed postscript] [postscript] - Miller, K.D. and M.P. Stryker (1990).
*Ocular dominance column formation: Mechanisms and models,*in*Connectionist Modeling and Brain Function: The Developing Interface,*S.J. Hanson and C.R. Olson, Eds. (MIT Press/Bradford), pp. 255-350.

[pdf (scanned, 10.5 MB)] - Miller, K.D. (1989).
*Correlation-Based Mechanisms in Visual Cortex: Theoretical and Experimental Studies.*Ph.D. Thesis, Stanford University, Program in Neurosciences (available from University Microfilms, Ann Arbor).

Here is a text-only version (tar file of gzipped postscript files). Figures may be available here in the future. - Miller, K.D., J.B. Keller and M.P. Stryker (1989).
*Ocular dominance column development: Analysis and simulation.*Science 245, 605-615.

[pdf (scanned, 6 MB)]

- 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.

[pdf] - Lindsay, G.W. and K.D. Miller (2017).
*Understanding biological visual attention using convolutional neural networks.*bioRxiv:https://doi.org/10.1101/233338. - 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.

[pdf] - 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] - 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.*In Press, Physical Review E; arXiv:1311.4672 [q-bio.NC]

[arXiv site, from which you can download pdf] - 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.

[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]

(it's a stretch to put this in the neuronal integration and circuitry category, but didn't want to make a new category just for this ...) - 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] - 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] - 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 (.76 MB)] - Lauritzen, T.Z. and K.D. Miller (2003).
*Different roles for simple- and complex-cell inhibition in V1.*Journal of Neuroscience 23, 10201-10213.

[pdf file (.43 MB)] - Murphy, B.K. and K.D. Miller (2003).
*Multiplicative gain changes are induced by excitation or inhibition alone.*Journal of Neuroscience 23, 10040-10051.

[pdf file (.26 MB)] - Troyer, T.W., A.E. Krukowski and K.D. Miller (2002).
*LGN input to simple cells and contrast-invariant orientation tuning: An analysis.*J Neurophysiol. 87, 2741-2752.

[pdf file (.37 MB)] - Miller, K.D. and T.W. Troyer (2002).
*Neural Noise Can Explain Expansive, Power-Law Nonlinearities in Neural Response Functions*. J Neurophysiol. 87, 653-659.

[pdf file (.25 MB)] - Kayser, A.S. and K.D. Miller (2002).
*Opponent inhibition: A developmental model of layer 4 of the neocortical circuit*. Neuron 33, 131-142.

[pdf file (3 MB)] - Lauritzen, T.Z., A.E. Krukowski and K.D. Miller (2001).
*Local correlation-based circuitry can account for responses to multi-grating stimuli in a model of cat V1*. Journal of Neurophysiology 86, 1803-1815.

[pdf file (1.1 MB)] [compressed postscript (1.3 MB)] - Kayser, A., N.J. Priebe and K.D. Miller
(2001).
*Contrast-dependent nonlinearities arise locally in a model of contrast-invariant orientation tuning*. Journal of Neurophysiology 85, 2130-2149.

[pdf file (0.75 MB)] [compressed postscript (1.26 MB)] - Krukowski, A.E. and K.D. Miller (2001).
*Thalamocortical NMDA conductances and intracortical inhibition can explain cortical temporal tuning*. Nature Neuroscience 4, 424-430.

[pdf file (0.33 MB)]

[web supplement to this paper (pdf, 0.2 MB)] - Ferster, D. and K.D. Miller (2000).
*Neural Mechanisms of Orientation Selectivity in the Visual Cortex*. Annual Reviews of Neuroscience, 23:441-471.

[pdf file (0.3 MB)] [compressed postscript (0.4 MB)] [view on web (HTML)] - Erwin, E. and K.D. Miller (1999).
*The subregion correspondence model of binocular simple cells.*Journal of Neuroscience 19, 7212-7229.

[compressed postscript (0.9 MB)] [postscript (8.4 MB)] - Bush, P.C., D.A. Prince and K.D. Miller (1999).
*Increased pyramidal neuronal excitability and enhanced NMDA conductance can account for post-traumatic epileptogenesis without disinhibition: a computational model.*Journal of Neurophysiology 82:1748-1758.

[compressed postscript (0.3 MB)] [postscript (2.7 MB)] - Troyer, T.W., A.E. Krukowski, N.J. Priebe and K.D. Miller (1998).
*Contrast-Invariant Orientation Tuning in Visual Cortex: Thalamocortical Input Tuning and Correlation-Based Intracortical Connectivity.*Journal of Neuroscience, 18, 5908-5927.

[compressed postscript] [postscript]

Warning: The last two pages of this paper contain dense figures that can take many minutes to load in a postscript viewer or to print. You can also get these separately:

All but last two pages: [compressed postscript] [postscript]

Last two pages: [compressed postscript] [postscript]

The paper has only one color page (P. 26). Here it is separately, in case that helps you with printing:

[P. 26, compressed postscript] [P. 26, postscript]

- Troyer, T. and K.D. Miller (1997).
*Integrate-and-Fire Neurons Matched to Physiological F-I Curves Yield High Input Sensitivity and Wide Dynamic Range.*In*Computational Neuroscience: Trends in Research 1997,*J.M. Bower, Ed. (Plenum Press, NY), pp. 197-201.

[compressed postscript] [postscript] - Troyer, T.W. and K.D. Miller (1997).
*Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell.*Neural Computation 9, 971-983.

[compressed postscript] [postscript] - Troyer, T.W., A.J. Doupe and K.D. Miller (1996).
*An Associational Hypothesis for Sensorimotor Learning of Birdsong*. In*Computational Neuroscience: Trends in Research 1995,*J.M. Bower, Ed. (Academic Press), pp. 409-414.

[compressed postscript] [postscript]

- 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] - 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] - 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 (0.4 MB)] [web supplement, pdf file (0.4 MB)]

(see also supplementary videos at Nature site). - Emondi, A.A., S.P. Rebrik, A.V. Kurgansky and K.D. Miller (2004).
*Tracking neurons recorded from tetrodes across time.*Journal of Neuroscience Methods 135, 95-105.

[pdf file (0.3 MB)] - Liu, R.C., S. Tzonev, S. Rebrik and K.D. Miller (2001).
*Variability and information in a neural code of the cat lateral geniculate nucleus.*Journal of Neurophysiology 86, 2789-2806.

[pdf file (0.5 MB)] - Wright, B.D., S. Rebrik, A.A. Emondi and K.D. Miller (1999).
*Cross Channel Correlations In Tetrode Recordings: Implications For Spike-Sorting.*Neurocomputing 26-27:1033-1038 (in special 2-volume issue containing proceedings of CNS98, the 1998 Computation and Neural Systems meeting).

[HTML] [postscript] - Wright, B.D., S. Rebrik and K.D. Miller (1998).
*Spike-Sorting of Tetrode Recordings in Cat LGN and Visual Cortex: Cross-Channel Correlations, Thresholds, and Automatic Methods.*Society for Neuroscience Abstracts, 24:895. (354.6)

[HTML of full poster] - Rebrik, S., S. Tzonev and K.D. Miller (1998).
*Analysis of Tetrode Recordings in Cat Visual System.*In*Proceedings of CNS97 (Computation and Neural Systems Meeting, Big Sky Montana, July 1997)*, J.M Bower, Ed. (Plenum Press).

[HTML] - Tzonev, S., S. Rebrik, and K.D. Miller (1997).
*Response Specificity of Lateral Geniculate Nucleus Neurons*. Society for Neuroscience Abstracts, 23:450. (177.1)

[HTML of full poster] - Miller, K.D., B. Chapman and M.P. Stryker (1989).
*Responses of cells in cat visual cortex depend on NMDA receptors,*Proc. Nat. Acad. Sci. USA 86, 5183-5187.

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- (1) Read
Miller et al., 1999 for a biologically- rather than
theoretically-oriented review of the models of
orientation selectivity, including 1998 work on combined development
of orientation and ocular dominance and a preview of 2002 Kayser and Miller
work on development of a complete
intracortical circuit (vs. just feedforward connections).

Read Miller, 1996a for a more theoretically-oriented overview, but only through the separate models of orientation and of ocular dominance.

Also, you might read the very short Miller, 1996b, which is a minireview focusing on the biological nature of synaptic competition. The existence of this competition is assumed in our modeling, but we do not model its mechanism.

UPDATE: see- Song et al., 2000, for a model in which competition naturally arises. Miller and MacKay, 1994 (see point (5) below) analyzed the methods by which competition had previously been imposed on models.
- Kayser and Miller, 2002, showing how intracortical as well as feedforward circuitry can develop.

- (2) Read Miller, 1990a (text-only version) for a detailed introduction to the ocular dominance model and the mathematics underlying the models. While this focuses only on the ocular dominance model, the mathematics of the orientation model is identical. Miller, Keller and Stryker, 1989 is the original reference, but Miller, 1990a is a better introduction.
- (3) Read Miller, 1994 (text, most figures, other figures) for details of the orientation model.
- (4) Read Erwin and Miller,
1996 (short conference version) or
1998 (full paper) to see how the orientation and ocular
dominance models are merged into one model.

Also read Erwin and Miller, 1999 to see the implications of this combined orientation/ocular dominance model for the binocular organization and disparity selectivity of mature simple cells, and how this compares with experimental data.

See also Wimbauer et al., 1997a and 1997b, in which this same formal model is used to study the development of direction selectivity in oriented simple cells through competition of lagged and non-lagged inputs. - (5) Read Miller and MacKay, 1994 and MacKay and Miller, 1990a (text, figures) for a full understanding of the mathematics of a single-output-cell model.
- (6) Further excursions:

(i) Read Miller, 1990b to see how the framework can be derived from a more fully nonlinear starting point.

(ii) Read Miller, 1998 to see how the framework can be used to analyze models based on synaptic sprouting and retraction as well as on modification of the strengths of anatomically fixed synapses.

(iii) Read the Ph.D. thesis (Miller, 1989) (text-only), chapters 5 and 6, for the fullest available mathematical analysis of the full (many-output-cell) model. Alternatively, Miller and Stryker, 1990 has somewhat more mathematical detail than Miller, 1990a, but less than the thesis. The full thesis includes all the material in both Miller, 1990a and Miller and Stryker, 1990.