  The traditional view of neural computation in the cerebral cortex holds that
sensory neurons are specialized, i.e., selective for certain dimensions of
sensory stimuli. This view was challenged by evidence of contextual
interactions between stimulus dimensions in which a neuron's response to one
dimension strongly depends on other dimensions. Here we use methods of
mathematical modeling, psychophysics, and electrophysiology to address
shortcomings of the traditional view. Using a model of a generic cortical
circuit, we begin with the simple demonstration that cortical responses are
always distributed among neurons, forming characteristic waveforms, which we
call neural waves. When stimulated by patterned stimuli, circuit responses
arise by interference of neural waves. Resulting patterns of interference
depend on interaction between stimulus dimensions. Comparison of these modeled
responses with responses of biological vision makes it clear that the framework
of neural wave interference provides a useful alternative to the standard
concept of neural computation.
