The distribution of neuronal population activation (DPA) as a tool to study interaction and integration in cortical representations

Abstract

In many cortical areas, simple stimuli or task conditions activate large populations of neurons. We hypothesize that such populations support processes of interaction within parametric representations and integration of multiple sources of input and we propose to study these processes using distributions of population activation (DPAs) as a tool. Such distributions can be viewed as neuronal representations of continuous stimulus or task parameters. They are built from basis functions contributed by each neuron. These functions may be explicitly chosen based on tuning curves or receptive field profiles. Or they may be determined by minimizing the distance between chosen target distributions and the constructed DPAs. In both cases, construction of the DPA is based on a set of reference conditions in which the stimulus or task parameters are sampled experimentally. In a second step, basis functions are kept fixed, and the DPAs are used to explore time dependent processing, interaction and integration of information. For instance, stimuli which simultaneously specify multiple parameter values can be used to study interactions within the parametric representation. We review an experiment, in which the representation of retinal position is probed in this way, revealing fast excitatory interactions among neurons representing similar retinal positions and slower inhibitory interactions among neurons representing dissimilar retinal positions. Similarly, DPAs can be used to analyze different sources of input that are fused within a parametric representation. We review an experiment in which the representation of the direction of goal-directed arm movements in motor and premotor cortex is studied when prior and current information about upcoming movement tasks are integrated.