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==The limits of feed-forward processing and visual attention==

===Limitations in feed-forward recognition===
Feed-forward architectures provide powerful vision systems to solve challenging recognition problems. Although many aspects of immediate object recognition can be explained on the basis of feed-forward architectures, there is much more to vision than immediate recognition. More challenging tasks involving complex backgrounds, multiple objects and occlusion require more processing time (e.g. [110-112]). A recent study has quantified the limits of feed-forward architectures in complex recognition problems [98] and the problem has also been justified based on complexity theory arguments [113].

===Backprojections, attention and visual object recognition===
The circuitry of the cortex involves a massive amount of backprojections that convey information from higher areas back to the lower areas. The anatomy has been more extensively studied in the visual system where it is clear that feedforward connections constitute only a small fraction of the total connectivity (see for example [7, 16, 17, 103]). In spite of the anatomical evidence, most computational efforts such as the ones highlighted in Sections 6.1 and 6.2, have focused on feedforward processing of information. Feedback using backprojections provides the opportunity to use previous knowledge, memory and task dependent expectations.

Information conveyed by backprojections can be generally thought of in terms of modulating and constraining the interpretation of sensory information [114]. As emphasized by several authors [115-120], Bayesian inference offers a useful framework to understand the relative contributions of bottom-up and top-down processing mediated by the feedforward and backprojections. At any given stage in cortex, we are interested in finding the (unknown) activity <math>z</math> that is compatible with the input and the context. Let us represent the input observations by <math>x_{obs}\ .</math> This could correspond to the sensory input from the retina or the input from earlier cortical areas. Let us represent the context by <math>x_{context}\ .</math> This context can take the form of information from memory, tasks, attention and expectations. The goal of the visual system is to infer the hidden variable or set of hypothesizes <math>z</math> (e.g., the category of the object stimulus presented) given the current observation <math>x_{obs}</math> (e.g., the pattern of neural activity in IT) and the context <math>x_{context}</math> (e.g. the pattern of neural activity in PFC). This requires estimating the conditional probability of all hypotheses given the observation <math>x_{obs}</math> and the context <math>x_{context}\ .</math> This conditional probability can be estimated from:
:<math>
P(z|x_{obs},x_{context}) = \frac{P(x_{obs}|z,x_{context})P(z|x_{context})}{P(x_{obs}|x_{context})}
</math>
The factor <math>P(z|x_{context})</math>  is where backprojections exert their influence (e.g., top-down influence from pre-frontal cortex to IT) and reflects prior constraints (e.g., knowledge about the task or other information pertinent to the current conditions). This could take the form of indicating that the probability of finding a lion inside an office is very small or it could represent the memory for specific features of a face being searched for in a crowd. 

A particular focus of several models that include backprojections has been the deployment of attentional resources to specific locations or features [88, 112, 121-123]. This could well be accounted within the above framework by making  <math>P(z|x_{context})</math> represent, for example, an attentional enhancement factor that biases neuronal competition towards a particular location in space (see [118, 120] for biologically plausible implementations). Indeed it has been suggested that neurons may act as probabilistic integrators of bottom-up and top-down signals and that attention may be used by the visual system for reducing uncertainty in complex scenes. Thus, this framework can include previous models of attention while at the same time providing flexibility for other roles of backprojections [114].

This approach can also be related to the ''reverse hierarchy routine'' [124] and also to the ''shifter'' circuit of Olshaussen and colleagues [89] . Briefly, a ''program'' running in pre-frontal cortex decides, depending on the initial feedforward categorization, the next question to ask in order to resolve ambiguity or improve accuracy. Typically, answering this question involves ''zooming in'' on a particular subregion of the image at the appropriate level and using appropriate units (for instance at the C1 level in the model presented in Fig. 4) and calling a specific classifier – out of a repertoire – to provide the answer. This framework involves a flavor of the ''20 questions'' game and the use of ''reverse hierarchy routines'' which control access to lower level units.

Gating information through attention has received considerable evidence as a mechanism to focus resources on the most salient or task-relevant aspects of an image [121, 125-128]. In its simplest formulation (spatial attention), a particular location within the image is salient (e.g. through its color, motion or other physical aspect) or is particularly important for a given task (e.g. a subject is instructed to attend to a particular location or a relevant event is expected to occur in a given location). Under these circumstances, bottom-up or top-down mechanisms could enhance information coming from the attended location at the expense of other parts of the image. Multiple experiments have shown that neuronal responses throughout visual cortex can be modulated by spatial attention (e.g. [113, 117, 118]). Alternatively, in feature-based attention may also enhance specific aspects of the image (as opposed to specific locations). For example, upon searching for a person wearing a red dress in a crowd, it may be useful to enhance the processing of red objects. Recently, physiological evidence has suggested that feature-based attention modulates the processing of specific simple features such as color or orientation in a parallel manner in early visual areas [119]. Backprojections could play an important role in using task-relevant information (perhaps coming from frontal cortex) to gate or reroute the information that reaches higher cortical stages.