Network Models of the Lateral Intraparietal Area

The monkey lateral intraparietal area (LIP) is involved in visual attention and eye movements. It has traditionally been studied using extracellular recording, where often a single neuron is recorded at a time. Thus we have a wealth of correlational knowledge of what LIP neurons do, but not how or why, i.e. we do not know the circuit mechanisms and functions of the observed LIP activity. In this thesis, we have aimed to uncover the circuit mechanisms underlying LIP activity by building tightly constrained computational models. In Part 1, we found that during two versions of a delayed-saccade task, beneath similar population average firing patterns across time lie radically different network dynamics. When neurons are not influenced by stimuli outside their receptive fields (RFs), dynamics of the high-dimensional LIP network lie predominantly in one multi-neuronal dimension, as predicted by an earlier model. However, when activity is suppressed by stimuli outside the RF, LIP dynamics markedly deviate from a single dimension. The conflicting results can be reconciled if two LIP local networks, each dominated by a single multi-neuronal activity pattern, are suppressively coupled to each other. These results demonstrate the low dimensionality of LIP local dynamics and suggest active involvement of LIP recurrent circuitry in surround suppression and, more generally, in processing attentional and movement priority and in related cognitive functions. In Part 2, we examine the mechanisms of learning in LIP. When monkeys learn to group visual stimuli into arbitrary categories, LIP neurons become category-selective. Surprisingly, the representations of learned categories are overwhelmingly biased: while different categories are behaviorally equivalent, nearly all LIP neurons in a given animal prefer the same category. We propose that Hebbian plasticity, at the synapses to LIP from prefrontal cortex and from lower sensory areas, could lead to the development of biased representations. In our model, LIP category selectivity arises due to competition between inputs encoding different categories, and bias develops due to excitatory lateral interactions among LIP neurons. This model reproduces the different levels of category selectivity and bias observed in multiple experiments. Our results suggest that the connectivity of LIP allows it to learn the behavioral importance of stimuli in order to guide attention.