A computational model of fMRI activity in the intraparietal sulcus that supports visual working memory

A computational model was developed to explain a pattern of results of fMRI activation in the intraparietal sulcus (IPS) supporting
visual working memory for multiobject scenes. The model is based on the hypothesis that dendrites of excitatory neurons are
major computational elements in the cortical circuit. Dendrites enable formation of a competitive queue that exhibits a gradient
of activity values for nodes encoding different objects, and this pattern is stored in working memory. In the model, brain
imaging data are interpreted as a consequence of blood flow arising from dendritic processing. Computer simulations showed
that the model successfully simulates data showing the involvement of inferior IPS in object individuation and spatial grouping
through representation of objects’ locations in space, along with the involvement of superior IPS in object identification
through representation of a set of objects’ features. The model exhibits a capacity limit due to the limited dynamic range
for nodes and the operation of lateral inhibition among them. The capacity limit is fixed in the inferior IPS regardless of
the objects’ complexity, due to the normalization of lateral inhibition, and variable in the superior IPS, due to the different
encoding demands for simple and complex shapes. Systematic variation in the strength of self-excitation enables an understanding
of the individual differences in working memory capacity. The model offers several testable predictions regarding the neural
basis of visual working memory.