The handbook of brain theory and neural networks

A circular cribbage board having a circular base plate on which a circular counter disc, bearing a circular scale having 122 divisions numbered consecutively from 0, is mounted for rotation. A transparent cover plate which is fixedly mounted on the base plate over the counter plate and through which the counter disc scale can be viewed has an arcuate slot the length of which exposes thirty holes of a row of holes in the counter plate adjacent the circular scale thereon. A circular scale numbered from 0 to 30 is displayed on the cover plate adjacent the slot thereon so that a player to record his score, extends a pointed instrument into a hole of the counter disc adjacent a selected number on the cover plate and rotates the counter disc, until the instrument meets the end of the slot, the cumulative score of the player being indicated by comparing a registration mark on the cover plate with the counter disc scale.

[1]  D. B. Bender,et al.  Visual properties of neurons in inferotemporal cortex of the Macaque. , 1972, Journal of neurophysiology.

[2]  J. Besag Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[3]  Steven A. Orszag,et al.  CBMS-NSF REGIONAL CONFERENCE SERIES IN APPLIED MATHEMATICS , 1978 .

[4]  J. Laurie Snell,et al.  Markov Random Fields and Their Applications , 1980 .

[5]  A P Georgopoulos,et al.  On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[6]  Rama Chellappa,et al.  Estimation and choice of neighbors in spatial-interaction models of images , 1983, IEEE Trans. Inf. Theory.

[7]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Haluk Derin,et al.  Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  E. Halgren,et al.  Neural encoding of individual words and faces by the human hippocampus and amygdala , 1988, Nature.

[10]  Anil K. Jain,et al.  Random field models in image analysis , 1989 .

[11]  Geoffrey E. Hinton,et al.  Distributed Representations , 1986, The Philosophy of Artificial Intelligence.

[12]  Keiji Tanaka,et al.  Coding visual images of objects in the inferotemporal cortex of the macaque monkey. , 1991, Journal of neurophysiology.

[13]  Federico Girosi,et al.  Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Minami Ito,et al.  Columns for visual features of objects in monkey inferotemporal cortex , 1992, Nature.

[15]  M. Stryker Elements of visual perception , 1992, Nature.

[16]  M. Young,et al.  Sparse population coding of faces in the inferotemporal cortex. , 1992, Science.

[17]  Anand Rangarajan,et al.  A continuation method for emission tomography , 1992 .

[18]  M. Young,et al.  An analysis at the population level of the processing of faces in the inferotemporal cortex. , 1993 .

[19]  P. Földiák,et al.  The ‘Ideal Homunculus’: Statistical Inference from Neural Population Responses , 1993 .

[20]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[21]  N. Birbaumer,et al.  Semantic memory impairment in Alzheimer's disease. , 1996, Journal of clinical and experimental neuropsychology.