"The most beautiful thing we can experience is the mysterious;
This was an interesting project; but now it's all done. My conclusion is a bit depressing for the current research; but hey, let's just do it all over again from the beginning :-) Hehe, enjoy!
Andersen, S. M. (2004). A Hybrid Neural Network Model of Binocular Rivalry (Final Report).
I've dedicated a web-page to the IT cortex.
ART FAQ at
http://www.wi.leidenuniv.nl/art/ and the "ART Headquarters" at Boston
Hebbian learning is the other most common variety of unsupervised learning (Hertz, Krogh, and Palmer 1991). Hebbian learning minimizes the same error function as an auto-associative network with a linear hidden layer, trained by least squares, and is therefore a form of dimensionality reduction. From the FAQ.
Connecting the terminology to stats:
The nature of associative memories -- a lecture
The CS41NN - Neural Networks lecture notes might have something to offer?
Pantic L.,Torres J.,Kappen H.J.,Gielen C.C.A.M. (2002).
Neural Network FAQ:
On combining networks (links from the FAQ, part 3)
Sarle, W.S., ed. (1997), Neural Network FAQ, part 3 of 7: Introduction, periodic posting to the Usenet newsgroup comp.ai.neural-nets, URL: ftp://ftp.sas.com/pub/neural/FAQ.html
Christoph M. Friedrich's web page, "Combinations of Classifiers and Regressors Bibliography and Guide to Internet Resources" at http://www.tussy.uni-wh.de/~chris/ensemble/ensemble.html
Tirthankar RayChaudhuri's web page on combining estimators
Not sure about usefulness of these, but here they are, anyway:
Pantic L.,Torres J.,Kappen H.J. (2003).
Kappen H.J. (2001).
I don't know what you guys think about publishing your research proposals here, but here is mine, anyway:
Andersen, S. M. (2003). A Hybrid Neural Network Model of Binocular Rivalry (Proposal).
On the number of Neurons in LGN/V1: An evolutionary Scaling Law for the Primary Visual System and its Basis in Cortical Function (Letters to Nature)
Are there really simple/complex cells? On the classiﬁcation of simple and complex cells
Tong, F. (2003). Primary Visual Cortex and Visual Awareness.
When searching for IT area stuff, be aware that the LOC (lateral occipital complex) area is considered the homolog of macaque IT cortex (Gross et al., 1972; Sary et al., 1993; Kobatake and Tanaka, 1994).
Fusi, S. (2001). Long term memory: encoding and storing strategies of the brain. NEUROCOMPUTING, 38, 1223-1228 JUN 2001
Paolo Del Giudice, Stefano Fusi (2003). Modeling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses. (Found on Computational Neuroscience, University of Berne)
Fusi's PhD thesis.
Storkey, A. (1999). The basins of attraction of a new Hopfield learning rule.
Nicolas Brunel (1996). Hebbian Learning of Context in Recurrent Neural Networks.
Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. In Nature Neuroscience.
LOC -- analysis of shape. (Kanwisher, N., Chun, M. M., McDermott, J. & Ledden, P. J. Brain Res. Cogn. Brain Res. 5, 55-67)
Models of Object Recognition (Tanaka, 2000)
Mechanisms of visual object recognition: monkey and human studies (Tanaka, 1997) In Current Opinion in Neurobiology, 7:523–529.
Inferotemporal Cortex and Object Vision (Tanaka, 1996)
Implementing the Expert Object Recognition Pathway Bruce A. Draper (2), Kyungim Baek and Jeff Boody
A Biological Plausible Expert Object Recognition System Kyungim Baek, Ph.D. thesis, Colorado State University, Fall 2002. A picture from his intoduction gives a broad overview:
Kanwisher, N., Tong, F., & Nakayama, K. (1998).
The effects of
face inversion on the human fusiform face area.
Tong, F. (2001). Competing theories of binocular rivalry: A possible resolution. Brain and Mind, 2, 55-83.
Grossberg's (1999) Laminart model. How does the cerebral cortex work? A diagrammatical figure of LGN - V1 - V2.
Grossberg, S. and Seitz, A., (2003). Laminar Development of Receptive Fields, Maps, and Columns in Visual Cortex: The Coordinating Role of the Subplate. Cerebral Cortex, in press.
Grossberg, S. and Howe, P.D.L. (2002). A laminar cortical model of stereopsis and three-dimensional surface perception. Vision Research, in press
Grossberg, S. and Williamson, J.R. (2001). A neural model of how horizontal and interlaminar connections of visual cortex develop into adult circuits that carry out perceptual groupings and learning. Cerebral Cortex, 11, 37-58.
A Fast, Fully Implicit
Backward Euler Solver for Dendritic Neurons (2000)
Peter Dayan and Larry Abbott have written a book on theoretical neuroscience, and ch. 7, which just happens to concern network models, is available online. Abbott's homepage is also full of links to pdf's of articles he has written. If you want to look at those and can't read .ps-files, you can find them converted to pdf or html by citeseer or google. Dayan's articles are also available.
A look at classical nn's, with MSVSC++ code at generation5, they also have some introductory texts.
Chris found some of BR-related articles at MIT's AI lab.
Robert P. O'Shea at the psych department @ Otago uni, NZ has compiled a bibliography of Binocular Rivalry articles. Ha has also written some himself:
O'Shea, R. P., & Corballis, P. M. (2003). Binocular rivalry in
split-brain observers. Unpublished manuscript. PDF file (224 K).
Blake's homepage also has some intro stuff
to BR, and his collection of PDFs
I haven't read them all yet, though... so do your own quality & relevance check... Download options are on the upper right part of the screen for all these links, you can choose between pdf, dejavu, and so on, and so forth...
All these are by Peter Dayan:
Do we want a supervised or an unsupervised network? Here's an unsupervised (yes, Peter Dayan is co-author):
On brain modelling using networks in general:
Neuronal model for binocular rivalry
I'm not trying to confuse anyone by putting this stuff here; don't look at it if you don't want to.
Caltech's Vision Lab (unsorted)