Object recognition takes place in the ventral visual system in the cortex. This system runs from the visual V1 area to IT. From there, there are connections with the PFC that ensure that perception and memory are connected. The further down the path, the more specific the neurons are and the greater their receptive fields. Plasticity and learning are probably present in all phases of object recognition.

It is not known what feedback will be given from the phases. The hypothesis is that the basic processes of information go feedforward and are supported by short time limits that are required for specific responses. However, this hypothesis does not exclude feedback loops. The feedforward architecture is a reasonable starting point for the theory of the visual cortex aimed at explaining direct object recognition. The recognition phase can also play a role.

Model based on the feedforward theory

The model used here is a direct extension of the Wiesel and Hubel model. The model is a feedforward theory of visual processes. Every feedforward theory distinguishes between two types of cells; simple and complex cells. This creates a distinction between selectivity and invariance. This model uses an input of 7 × 7 boxes. The boxes are first analyzed by a multidimensional row of simple S1 units that respond best to lines and angles. The S1 units can be on and off if the figure they represent is present. The next level is C1. C1 looks like complex cells from the striatum. Every complex C1 unit receives information from a unit of S1 cells with the same orientation. However, there may be slight differences and C1 can receive multiple S1 units, making the C1 unit less sensitive to position and size.

At the C level, complex cells are thus converted into a two-dimensional image by combining the afferent S cells. The results are calculated with firing neurons. This creates a complex model with how a representation is created in the brain of a visual image. Reversing a C level is also called a MAX operation.

Not all features of a complete feedforward network can be found in this model. That would be too complicated. Every model tries to describe and explain a piece. Because we still have little knowledge of how everything works exactly, this remains very complicated. So you also have to put the different theories (and articles) next to each other and try to understand and apply them to each other. Just as in this model the MAX returns.

Results

The above data was presented and measured with a task where test subjects had to indicate whether or not an animal was presented. The showing of the pictures was very short. Yet people can then quickly and accurately decide whether or not there is an animal. This means that our object recognition works very quickly. If the image was displayed a little shorter this also had little effect. The model was used to view the similarities with people.

A comparison between human performance and the feedforward model in animal tasks was measured with (d'); a monotonic function of the performance of the observers. The results showed that people and the model responded about the same and that there were similar reactions. Recent studies have even shown that animal recognition also works if, for example, the image is shown upside down. People were a bit slower when using a concealment component.

Discussion

This model improves the old model on two important points.

1. A learning phase has been added. The hierarchy of the visual system is viewed better. Learning takes place in two independent phases. First during development with unsupervised learning, then at task-specific circuits.

2. The new model is closer to the anatomy of the visual cortex. This model also suggests (along with other theories) that an initial feedforward step is driven in bottom-up processes for a basic representation, built from a basic dictionary with general figures.