Population Codes and Their Correlates In Decision Making

<p>This dissertation was organized in two parts: in part 1, we discussed Neural Correlates of Perceptual Accuracy” and in part 2 we discussed “Strategy encoding in Prefrontal Cortex”.</p> <p><em>Abstract of part 1</em>_The accurate transmission of electrical signals within neocortex is central to sensory perception and cognition. Theoretical studies have long proposed that the temporal coordination of cortical spiking activity controls signal transmission and cognitive function. In reality, whether and how the precise temporal coordination in neuronal populations during wakefulness influences perception remains a mystery. Here, we simultaneously recorded populations of neurons in early and mid-level visual cortex (areas V1 and V4) to discover that the precise temporal coordination between the spikes of three or more neurons carries information about perceptual reports in the absence of firing rate modulation. Perceptual accuracy was correlated with higher-order spiking coordination within V4, but not V1, and with the feedforward coordination between V1 and V4 activity. Our results indicate that while stimulus encoding is related to the discharge rates of neurons, perceptual accuracy is correlated with the precise spiking coordination within visual cortical populations.</p> <p><em>Abstract of part 2_</em>Foraging animals explore the environment to earn valuable resources, at the lowest cost. Previous experiments on various species suggest that they simply maximize the current flow of rewards without predicting the future outcome of their actions. We found that monkeys are able to predict the reward outcomes to plan ahead, when allowed to forage freely in an interactive environment. In uncertain environments, the prediction of outcome requires access to a model of the reward structure. We recorded the activity of a population of individual neurons in dorso-lateral prefrontal cortex (dlPFC) and found the representation of an internal reward model in this area. We singled out the component in dlPFC activity that represents the reward model and showed that this component predicts the next action. We think our naturalistic experimental setting as well as multi-dimensional analysis of the neural activity was the key to this finding and perhaps can shift the paradigm in studying neural correlates of complex behaviors.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation (PhD)

Year dc:date.available

2018

Authors dc:creator

• Shahidi, Neda
• <p>0000-0003-1961-4611</p>

Contributors dc:contributor

• Valentin Dragoi
• Harel Shouval
• Xaq Pitkow

• population codes
• decision making
• electrophysiology
• coordination
• foraging
• free-moving
• wireless
• neuroscience
• Cognitive Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Repository record dc:identifier

https://digitalcommons.library.tmc.edu/utgsbs_dissertations/888

OAI identifier oai:identifier

oai:digitalcommons.library.tmc.edu:utgsbs_dissertations-1934