Neurosciences & Imagerie cérébrale Libre accès

Abstrait

Rethinking how the Nervous system registers and identifies shapes

Ernest Greene

Most current theories of shape recognition propose that lines and edges (contours) are registered as elemental building blocks, with identification of a shape being accomplished by an enumeration of those elements. The critical role of contours has been assumed by artists and philosophers for centuries, and by psychologists since the inception of our discipline. Many of the early concepts were not clearly delineated, but the advent of computers provided a better understanding of how to specify the task as mechanistic steps. Both Selfridge (1959) and Marr (1982) advanced conceptual models for how the lines and edges of shapes could be registered and then combined to allow for identification. Further, the Nobel Prize winning research of Hubel & Wiesel (1959; 1962) appeared to provide neural substrates for registering contour attributes, and a plausible theory for how those attributes could be combined by the nervous system. These investigators found that individual neurons in primary visual cortex (V1) were selectively activated by elongated bars, with the degree of activation being determined by the orientation and location of a given stimulus. Their model for how the neurons manifested this selectivity was based on precise anatomical mapping of connections from retina, through lateral geniculate nucleus, to cortex, wherein an aligned set of retinal ganglion cells provided the stimulation to a given “orientation selective” neuron in V1. The various contours comprising a given shape would be expected to drive activity in a specific subset of V1 neurons. It seemed plausible that the output from neurons in that subset could converge on a higher order neuron, such that the receiving neuron would be activated only by the contours of that particular shape.