# Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems
## 2 The Human Visual System  
It is well known that the feature hierarchies in neural networks are often compared with the structure of the primate visual system, which has been shown to use a hierarchy of features of increasing complexity, from simple local features in the primary visual cortex to complex shapes and object views in higher cortical areas. Until recently, the leading analysis of the object recognition process in the human cortex was focused on the ventral visual pathway that runs from the primary visual cortex, V1, over extra-striate visual areas, V2 and V4, to the inferotemporal cortex [4]. While most neurons show specificity for a certain object view or lighting condition [5], the ITC cells show robustness in their firing to stimulus transformations, such as scale, position changes, and light conditions. In addition, the ITC has been seen as a major source of input to the Prefrontal Cortex (PFC), which is seen as \"the center of cognitive control\" involved in linking perception to memory. This traditional bottom-up view of the human visual system strongly resembles the nature of neural networks, showing strong specificity in the first layers and more robust and complex activations in the higher layers. Nevertheless, this bottom-up view arises from the fact that most of the early research activities were focused on the low-level parts of the human visual system, due to the complexity involved in analyzing the higher-level parts of the human visual system. Extensive research in recent years that used advanced techniques, such as accurate fMRI, shows the main role of the high-level parts of the visual system, which integrate top-down signals with the bottom-up signals of the ventral pathway. In general, the human visual system has two main pathways that participate in the continuous processing of the visual input. The ventral stream, which is a bottom-up feedforward visual features analysis that plays a major role in the perceptual identification of objects, and the dorsal stream, which governs the analysis of objects' spatial location and mediates the required sensorimotor transformations [6]. In addition to these pathways, there are two additional top-down projections: (1) The object prediction projection, which brings the low spatial information of the object to the orbitofrontal cortex that in turn uses this information to create fast gross object recognition; and (2) the scene prediction projection, which brings the low spatial information of the entire field of view to the parahippocampal cortex that in turn uses this information for scene recognition and contextual hypothesis [7; 8]. These two top-down projections merge with the bottom-up ventral pathway in the inferotemporal cortex, as illustrated in Figure 2. The inferotemporal cortex integrates the three inputs and uses the top-down hypotheses to bias the recognition process. Figure 2: Schematic description of pathways in the human visual system. Furthermore, recent studies on the motor control in humans and animals suggests that predictions play a key role in the visual learning process. Differences between the predictions and the visual feedback are used to evoke learning process. For example, when reaching the hand to grasp an object, the brain processes the visual feedback in order to compare the object location and the actual position of our limb in order to modify our motion. Studies that applied Electroencephalography (EEG) to analyze the temporal nature of visual cue utilization during movement planning have found that visual observation of a movement error evokes an Event-Related brain Potential (ERP) waveform with a timing and topography consistent with the Feedback Related Negativity (FRN) [9]. In cases where there is no predetermined target, but rather an intrinsic motivation to learn the environment, information gain serves as the intrinsic goal, and novelty is reached by detecting errors in predictions [10].