Convolutional neural network and visual cortex
A convolutional neural network (CNN) is an artificial neural network used for image and video recognition and other visual tasks. CNN’s are designed to process data with a grid-like topology, such as an image, which allows them to learn spatial hierarchies of features from input data. They consist of an input layer, multiple hidden layers, and an output layer. The hidden layers typically include convolutional, pooling, and activation layers. The convolutional layers apply a convolution operation to the input data, which extracts features from the data. In contrast, the pooling layers reduce the spatial dimensions of the data to reduce the computational cost. The output layer produces the final result of the CNN.
The visual cortex
The visual cortex is the part of the brain responsible for processing visual information from the eyes. It is located in the occipital lobe at the back of the brain. The visual cortex is organized into several functional areas, each responsible for different aspects of visual processing.
V1 (also known as the primary visual cortex) is the first stage of visual processing, where the visual information is transformed into a neural representation. It is organized into a retinotopic map, where different regions of V1 correspond to different areas in the visual field.
V2, V3, V4, and V5/MT (middle temporal area) are also known as extrastriate areas, which are responsible for more complex visual processing tasks such as color, form, and motion perception. These areas are also organized hierarchically, with V2 receiving input from V1 and feeding into V4.
IT (inferotemporal) area is responsible for object recognition and face recognition.
The visual cortex is also connected to other areas of the brain, such as the parietal cortex, which is responsible for spatial attention and visuospatial tasks, and the prefrontal cortex, which is involved in decision-making and planning.
visual cortex and CNN
The organization of the visual cortex is hierarchical, with early visual areas processing low-level visual features such as edges and lines and later visual areas processing more complex features such as objects and faces.
Convolutional neural networks (CNNs) are inspired by the organization of the visual cortex in the brain, which is responsible for processing visual information. The visual cortex is organized into several functional areas, each responsible for different aspects of visual processing. Similarly, CNNs are composed of several layers, each responsible for learning various features from the input data.
In the visual cortex, the primary visual cortex (V1) is the first stage of visual processing, where the visual information is transformed into a neural representation. It is organized into a retinotopic map, where different regions of V1 correspond to different regions of the visual field. Similarly, in a CNN, the first layers, such as the convolutional layers, are responsible for learning local features, such as edges and lines, from the input data and creating a spatial hierarchy of features.
As said before, the visual cortex also includes extrastriate areas such as V2, V3, V4, and V5/MT, which are responsible for more complex visual processing tasks such as color, form, and motion perception. Similarly, in a CNN, deeper layers, such as the fully connected layers, are responsible for learning more complex features, such as objects and faces, from the input data.
The organization of the visual cortex is hierarchical, with early visual areas processing low-level visual features and later visual areas processing more complex features. Similarly, in a CNN, the features learned by the early layers are used as input for the later layers, creating a hierarchical representation of the input data.
Overall, the organization of a CNN is inspired by the organization of the visual cortex in the brain, with layers learning local and complex features and creating a hierarchical representation of the input data.
