Some Properties of Neural Representations

When a stimulus reaches our sensory system, it evokes a perceptual schema. This schema, subsequently, produces a central neiral representation of the stimulus. I want to investigate what properties these neural representations must have to support complex cognitive processes. To do this, I present a description of neural networks that makes it possible to bridge the gap between the neuronal and the conceptual level of description. Using this high level description , I state a hypothesis about neural representations that I have called the continuity hypothesis. It makes it possible to use knowledge of processes at the conceptual level at the neuronal level. Some properties of the representation of a stimulus are presented and it is shown that the central representation can be the result either of a categorisation or an associative process. Two minimal conditions on categorisation are suggested. These conditions are used to construct a new type of competitive learning that support representations that cohere with these conditions. Finally, I discuss the representation of relations between stimuli. It is suggested that the temporal domain is necessary to represent arbitrary relations among stimuli.