nant perceptual binding is undesirable. There are two key kinds of information available to bind sensorymechanisms: location coding and correlated activity. Some studies suggest that binding exploits locationcoding; for example, two cells in different cortical areas,each responding to a different image feature (e. g., colorand orientation), are likely to synchronize if both areactive and both are coded for the same retinotopic location (for review, see Finkel & Edelman, 1989). This is plausible because of the substantial retinotopicallyspecific connections between cortical areas. We do notdenigrate the role of retinotopic mapping in binding,but because large receptive fields in the extrastriatecortex degrade localization, it is useful to have anindependent supplemental source of information forbinding. Moreover, supplementing location coding should speed and improve binding (time is at a premi-um when segmentation/binding takes only 50-200 msec;von der Malsburg, 1999). We suggest that a type ofstimulus-specific common neural input may provideinformation useful for binding. THEORETICAL BUILDING BLOCKS A Role for Stimulus-induced Correlations in Oscillatory Binding A tenet of binding theory is that binding is driven by (and makes manifest) correlations between mechanismsresponding to common image features. In principle, aninitial weak correlation between oscillatory mechanisms is amplified into mutual synchronization (von der Mals-burg, 1981). However, what guarantees that synchroni-zation will be correctly linked to real stimulus features?To avoid binding everything, competitive networkscould select the strongest correlations to be synchro-nized. Correlated activity in neural mechanisms is in-duced by causal relations between temporal events (e. g.,by perception-action interactions) and by common neu-ral inputs (Feng, 2000; von der Malsburg, 1999). One particularly promising source of correlated activity—crosstalk—is both stimulus-driven and induced bycommon neural inputs. Crosstalk is the unintendedportion of a selectively filtered signal. For example, Pcells carry both hue and luminance information (De Valois & De Valois, 1988). A cortical cell that filters the P-cell input to extract luminance may also extract a portion of the chromatic signal as well (Billock, 1995). This crosstalk—a nuisance in visual parallel processingtheory—has been neglected in binding theory. Theclosest exception is Horn, Sagi, and Usher's (1991)segmentation/binding network, which uses correlatednoise to synchronize neural oscillators responding todifferent visual modalities. Consider two such systems(E 1,E2) coupled through a common set of inhibitory (I) neurons. d E1=dt¼/C0E1þ Ft ða E1/C0b I/C0/C18E/C0c R1þ INPUT 1þ Q1;2Þ d R1=dt¼ð1=d/C01ÞR1þ E1 d E2=dt¼/C0E2þ Ft ða E2/C0b I/C0/C18E/C0c R2þ INPUT 2þ Q1;2Þ d R2=dt¼ð1=d/C01ÞR2þ E2 d I=dt¼/C0Iþ Fðf E1þg E2/C0h I/C0/C18IÞ Here E1,E2are firing rates driven by different visual features (INPUT 1, INPUT 2), which in this article could be achromatic contrast, color, or motion. R1,R2are dy-namic thresholds; a,b,c,d,g,u,tare constants; and Ft(x) = 1/(1+ e/C0x/t). Key attributes of Equation 1 are that E1and E2oscillate (fire) in response to their sensory inputs and can synchronize those oscillations. The Q1,2 term is an input common to both systems which speeds and strengthens synchronization—a result likely genericto coupled systems. In Horn et al. 's (1991) model, it re-presents correlated noise. Because Horn et al. considerbinding of color and form, they speculate that somenoisy ''early mixed representation of shape and color information exists in the input layer. '' Another model uses a cell class tuned along two stimulus dimensionsand couples it to a cell class that has a common stimulusdimension (Roelfsema, Engel, Ko ¨nig, & Singer, 1996; e. g., oriented cells for chromatic edges coupled to similarlyoriented cells for achromatic contrast). The correlatednoise and common dimension approaches are moresimilar than they seem. An ''early mixed representation'' is an apt description of retinogeniculate multiplexing. Attempts to create cortical cells tuned along (and labeledfor) one stimulus dimension inevitably induce stimulus-specific crosstalk from other stimulus dimensions (e. g.,Billock, 1995). Below, we quantify this crosstalk for twospecific sensory interactions. Fragmentation and Fading—Characteristic Failure Modes of Segmentation Networks Generally, within a sensory modality, segmentation in-volves cooperation between mechanisms that agree andcompetition between mechanisms that disagree. Thesemethods are generic and apply whether an image is to be segmented on the basis of achromatic form, color, motion, depth, and so forth (Levine, 2000; Wang &Terman, 1997; Grossberg & Wyse, 1992; von der Mals-berg & Buhmann, 1992). Competitive networks definethe location of edges and borders. Cooperative net-works fill in areas between borders and reinforce theresponse of retinotopically neighboring cells signaling(1) Billock and Tsou 1037