neity in the functional properties of neurons in different cortical areas. A natural explanation is that each cortical area conducts calculations of the same form but that the inputs to each area are different and distinctive. Certainly there is ample evidence that the neurons carrying output signals from one cortical area to another are quite inhomogeneous in their properties and distribution, and it is unusual to find individual cortical neurons projecting to more than one cortical target area (42). Our models suggest a particular computational architecture that can be applied successfully to at least two cortical areas, based only on differences in their inputs. We hope in the future to show that this architecture can be applied to other cortical areas, differing in each case only in the nature of the input signals that each area receives.

Available Implementation. A simulation program for Macintosh computers that implements these two models (as well as the linear model of simple cell receptive fields) is available on the World Wide Web at the URL http://white.stanford.edu/ or via anonymous ftp from white.stanford.edu, in directory ∼/v1-mt-model/.

We are grateful to Matteo Carandini for helpful discussions. The work described in this paper was supported by grants from the National Institutes of Health (MH50228 and EY02017) and by the Howard Hughes Medical Institute. D.J.H. is an Alfred P. Sloan Research Fellow.

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Front Matter (R1-R4)

Vision: From photon to perception: An Introduction (1-3)

How photons start vision (4-9)

Gain and kinetics of activation in the G-protein cascade of phototransduction (10-14)

The biology of vision in Drosophila (15-20)

Primate photopigments and primate color vision (21-25)

Circuitry for color coding in the primate retina (26-32)

Cell fate determination in the vertebrate retina (33-39)

Molecular biology of retinal ganglion cells (40-45)

Emergence of order in visual system development (46-52)

Multineuronal codes in retinal signaling (53-58)

Spatial integration and cortical dynamics (59-66)

Computational models of cortical visual processing (67-71)

Motion perception: Seeing and deciding (72-77)

Binocular visual surface perception (78-83)