A category-free neural population supports evolving demands during decision-making

The posterior parietal cortex (PPC) receives diverse inputs and is involved in a dizzying array of behaviors. These multiple behaviors could rely on distinct categories of neurons specialized to represent particular variables or could rely on a single population of PPC neurons that is leveraged in different ways. To distinguish these possibilities, we evaluated rat PPC neurons recorded during multisensory decisions. Novel tests revealed that task parameters and temporal response features were distributed randomly across neurons, without evidence of categories. This suggests 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.