A multisensory approach for understanding decision circuits

Despite numerous experiments on perceptual decision-making, fundamental questions about the underlying neural circuits remain unanswered. Specifically, little is known about circuits that weigh

different sources of information and integrate them to guide decisions. Some individual neurons have been associated with these computations, but most of our knowledge comes from decisions about a single, isolated sensory modality. Further, most decision studies are carried out in non-human primates, providing limited access to powerful tools for neural circuit dissection compared to rodents. As a result, little is known about how unisensory and multi sensory information is transformed across decision microcircuits spanning multiple areas. Almost nothing is understood about local microcircuits and how they support within-area computations that are fundamental to decision-making. My lab aims to define the neural circuits that allow animals to integrate evidence across time and sensory modalities to guide decisions. To achieve this, we join two previously separate fields, decision-making and multisensory integration, and bring this combined approach to rodents. Our current focus has been on neurons in the posterior parietal cortex (PPC). PPC receives diverse inputs and is involved in a dizzying array of behaviors. These many behaviours 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. Newly designed 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 present needs. To test for an additional signature of a dynamic network, we compared moments when behavioral demands differed: decision and movement. Our new 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.