This research entails studying the information processing that underlies behavior during complex decisions. In order to do this, the researchers train animals (mice, rats, humans) on complex behavioral tasks. From the rats, they record large ensembles of neural activity. The group's datasets tend to be relatively large (1 GB per session is not unusual and a total dataset from multiple animals and multiple days can be 10 GB to 100 GB). Moreover, analyzing an experimental question can be extremely computationally intensive, requiring MSI supercomputer resources.
Typical questions address how information is represented and how it changes as behavior changes. This typically requires a decoding operation in which the researchers calculate the relationshp between neural firing and behavioral variables in one situation and then using this calculation along with a new dataset of neural firing to predict information during behavior. For example, they found that when rats come to choices they sometimes deliberate over those choices, representing the alternate options in one neural structure and evaluating the potential options in another. Other times, when rats come to choices, they use a different neural system to represent the current sensory situation, releasing a well-learned motor sequence. Calculating the decoded operation can be computationally intensive.
The primary question is to address how conflicts between these neural algorithms are resolved. These two neural systems depend on different neural structures. Current hypotheses suggest that a third neural structure mediates conflicts between them, but determining that will require not only the decoding operations, but also relating those decoding to representational changes within the third structure.
Future work may include computational modeling of neural systems. These models entail hundreds or thousands of simulated interacting neurons. The models allow the researchers to test theoretical hyptoheses about how neural systems might be computing information.