The posterior parietal cortex (PPC) receives diverse inputs and is involved in a dizzying array of behaviors. that PPC neurons constitute a dynamic network that is decoded according to the animal��s current needs. To test for an additional signature of a dynamic network we compared moments when behavioral demands differ: decision and movement. Our novel state-space analysis revealed that the network explored different dimensions during decision and movement. These observations suggest that a single network of neurons can support the evolving behavioral demands of decision-making. Introduction Individual neurons are often seen as members of highly specialized categories with response properties making them suitable for particular classes of computations1 2 This view has been fruitful for understanding early sensory areas where single neurons can be strongly tuned for task parameters such as direction of motion3 or disparity4. The assumption of neural categories is reflected in many experimental designs and analysis methods even those focusing on neural structures far downstream of early sensory areas. This assumption can be evident in the way neurons are sampled: sometimes neurons must meet certain response criteria to be included for study such as responsiveness to certain stimuli or activity during a delay period5-8. Implicit WAY-600 in this approach is the idea that the cell��s response during one stimulus identifies it as a member of the category being examined. The assumption of categories can also be evident during analysis: pie charts a common way of summarizing populace data9-11 explicitly assign neurons to categories. Another way of summarizing a populace response averaging over many neurons likewise reflects the assumption that each neuron is an exemplar of a category different from other category members mainly because of noise. An alternative hypothesis is that neurons reflect random combinations of parameters leading to neural populations in which neurons�� responses defy categorization. Theoretical work suggests a major advantage for category free populations: when parameters are distributed randomly across neurons an arbitrary group of them can be linearly combined to estimate the parameter needed at a given moment12-14. This obviates the need for precisely pre-patterned connections between neurons and their downstream targets and also means that all information is transmitted. This latter house could allow the same network to participate in multiple behaviors simply by using different readouts of the neurons. Experimental work has not tested directly whether neural populations are category-free but many observations are broadly consistent with this possibility. Specifically recent studies have exhibited that neurons in parietal15-17 and frontal18 areas have ��mixed selectivity��: individual neurons are modulated by multiple task parameters. Mixed selectivity would be expected if neurons reflect random WAY-600 mixtures of parameters but also might exist under other assumptions. Other experimental work has probed for the presence LYN antibody of neural categories defined by the timing of a neuron��s response19. That work argued against categories but only tested for categories defined by response sequence. A more general test is usually thus required. Further because neurons in that study responded sparsely it WAY-600 was not possible to test whether the same neurons participated statically or dynamically in the network as the behavioral demands evolved from decision to movement. Here we developed a multisensory decision task rich enough to expose the functional organization of a neural populace both at a single moment and over the course of a complex choice with evolving behavioral demands. WAY-600 Our data suggest that in the posterior parietal cortex (PPC) the population is usually category-free: response features are randomly distributed across neurons. A possible explanation for this configuration is usually that it confers flexibility allowing the brain to use the same neurons in different ways depending on the current requires of the animal. In keeping with this WAY-600 explanation we found that the population can be decoded instantaneously to estimate multiple task parameters and that populace activity explored different dimensions as the animal��s needs evolved from decision formation to.