Sensorimotor Encoding In Prefrontal CortexKhanna, Sanjeev (2019) Sensorimotor Encoding In Prefrontal Cortex. Doctoral Dissertation, University of Pittsburgh. (Unpublished)
AbstractWhen processing the vast visual world in front of us, eye movements allow us to focus on specific relevant stimuli. To gather information about a stimulus, the brain must determine its location and plan an eye movement to that location. It is not fully understood how populations of neurons store the incoming visual input in a memory signal and then retrieve it to produce a fast and accurate motor output. We sought answers to this question by recording the activity of populations of neurons in cortical regions thought to be involved in the maintenance of working memory and the generation of eye movements. First, we analyzed how populations of neurons coordinated their activity during the period after a visual stimulus was presented, but before an eye movement was made. Specifically, we were interested in identifying the optimal activity profiles for generating a fast eye movement. We recorded from groups of neurons in the frontal eye fields (FEF), an area known to be important for saccade generation. We found neurons change their activity at both the individual level and by covarying their activity with other neurons to generate fast eye movements. We then recorded from neurons in prefrontal cortex (PFC) to determine how visual and motor signals are encoded at the single neuron and population level. For single neurons, we observed rich dynamics, including neurons that encoded the entire visual field, and neurons that shifted their tuning between visual and motor epochs. At the population level, these shifts in tuning created a dynamic population code. These single neuron properties were less likely to be observed in FEF, which resulted in FEF having a more stable population code when compared to PFC. In summary, the visual and motor representations associated with processing a stimulus and preparing an eye movement manifest in the activity of single neurons and populations, and their dynamics over time. These results lead to a richer view of working memory and eye movement planning at the level of populations of neurons than has previously been appreciated. Share
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