Decoding the neural algorithms that underlie behavior
In order to understand how the brain enables complex behaviors, a step-by-step account of how information is transformed from sensory input to motor output is needed. To gain insight into such neural algorithms, I have developed ‘population decoding’ data analysis methods that can be used to accurately track what information is in a brain region and how information is coded in neural activity. In this talk I will describe how, in collaboration with experimental neuroscientists, I have applied this method to spiking activity in macaque monkeys to examine: 1) how information is transformed from sensory signals into more abstract representations that are useful for behavior 2) how such representations are modified by task demands (i.e., attention), 3) how high level brain regions that receive this input (i.e., the prefrontal cortex) only selectively represents task relevant information, and 4) how the flow of information flow can be precisely tracked in a simple pop-out attention task. I will also briefly describe a set of tools that can be used to analyze a range of neural signals in order to gain further insight into the algorithms that brain uses to solve tasks.