The Development of the Primary Visual Cortex
The Development of the Primary Visual Cortex
Introduction
The study of the visual system is of great importance to both robotics and neuroscience. In the former case, much effort has gone into the development of engineering-type approaches towards the goal of functional machine vision systems. In the latter case, a great deal of experimental work has revealed the physical structure and functional organization of the visual systems of various species. We have sought to bridge the gap between these two fields by constructing a method of computer image processing which is based on current knowledge of how the human visual system works. Each field is enriched by the application of knowledge from the other. The field of robotics gains the use of methods employed by natural beings, whose capabilities far exceed robots of today. Neuroscience benefits from the powerful theoretical tools of artificial neural network techniques and image processing, resulting in a better understanding of the principles by which the brain can so effectively process information. In our work, we formulated a model of the development and functioning of the visual system, implemented the model in computer simulations to arrive at a mature, functioning system, and applied the system to images from the robot --camera system. This research is undertaken by Ted Hesselroth, a graduate student in the Physics Department. He joined our group three years ago.
Description
The model incorporates many details of the visual system which have been discovered experimentally. Included in the model are the functions of the retina, the lateral geniculate nucleus (LGN), and the primary visual cortex. The study of models of the primary visual cortex and the LGN have been major topics in our group.
The first area of visual processing is the retina of the eye, which not only collects light through the activity of the photoreceptors, but serves as a filter as well, through the action of center-surround cells. For so-called ON-center cells, light falling near the location of the cell causes excitation of the cell, while light that falls farther away inhibits cell activity. OFF-center cells have the reverse properties. When light of constant intensity illuminates the whole region around the cell, there is no cell activity, indicating that the input through the inhibitory and excitatory areas has been summed by the cell.
Information from the retina is transmitted through the optic nerve to the lateral geniculate nucleus. The LGN contains approximately the same number of neurons as the retina, and it is thought that the connections between the retina and the LGN are more or less one-to-one. For this reason neurons in the LGN also show center-surround receptive field properties.
The primary visual cortex, or area V1, contains neurons which respond to various features of the image. At this stage in the processing some analysis begins to take place. Feature selectivity is accomplished in a way similar to that in the center-surround cells previously described. The excitatory and inhibitory regions of V1 cells alternate and are elongated in a particular direction in retinal coordinates. In this way, the neurons respond most strongly to edges of a particular orientation. This yields a decomposition of the image according to its edges. This edge-detection is realized through feedforward connections from the LGN to V1. There exist also lateral connections between V1 neurons and reciprocal connections from V1 to the LGN. The latter are similar to the forward connections, but are inhibitory.
At the beginning of the simulation, all connection strengths are set to random values, and no coherent image processing can be achieved yet. By applying Hebbian learning to the connections, the neural network develops so as to produce V1 neurons which are orientationally selective as described above. It has been found experimentally that neurons in the V1 layer are organized according to their feature detecting properties. In the macaque monkey, the orientational selectivity of the neurons varies in a continuous way across the cortex, forming a ``feature map". Implementation of the model described produces organization in the V1 layer which closely resembles that found in experiments. The development of the orientational feature map obtained by the algorithm is shown in the figure.
We applied the model visual system to the robot-and-basil image. Shown in the figure is the image as seen via the edge detectors of the model. This illustration shows that the image is grasped by the feature-detecting properties developed in the primary visual cortex. This output is suitable for combining with motor cortex models to produce realistic models of visuo-motor control.
