The ability to associate images is the basis for learning relationships involving vision, hearing, tactile sensation, and kinetic motion. A new architecture is described that has only local, recurrent connections, but can directly form global image associations. This architecture has many similarities to the structure of the cerebral cortex, including the division into Brodmann areas, the distinct internal and external lamina, and the pattern of neuron interconnection. The images are represented as neurotransmitter fields, which differ from neural fields in the underlying principle that the state variables are not the neuron action potentials, but the chemical concentration of neurotransmitters in the extracellular space. The neurotransmitter cloud hypothesis, which asserts that functions of space, time and frequency, are encoded by the density of identifiable molecules, allows the abstract mathematical power of cellular processing to be extended by incorporating a new chemical model of computation. This makes it possible for a small number of neurons, even a single neuron, to establish an association between arbitrary images. A single layer of neurons, in effect, performs the computation of a two-layer neural network.
Analogous to the bits in an SR flip-flop, two arbitrary images can hold each other in place in an association processor and thereby form a short-term image memory. Just as the reciprocal voltage levels in a flip-flop can produce a dynamical system with two stable states, reciprocal-image pairs can generate stable attractors thereby allowing the images to serve as symbols. Spherically symmetric wavelets, identical to those found in the receptive fields of the retina, enable efficient image computations. Noise reduction in the continuous wavelet transform representations is possible using an orthogonal projection based on the reproducing kernel. Experimental results demonstrating stable reciprocalimage attractors are presented.