Prefrontal Cortical Circuitry that Supports Learning in a Complex and Dynamic Environment

Abstract

Counterfactual learning refers to the ability to adapt our behavior in response to observations or inferences about the outcomes of choices that we did not make. By allowing us to consider many possible actions and their outcomes, even in the absence of direct experience, counterfactual thinking is a fundamental component of decision making, especially in complex and changing environments. However, we know very little about the neural circuitry underlying this type of learning. Although previous work has implicated the prefrontal cortex (PFC) in the ability to learn from counterfactual information, we do not know which neurons within the PFC are required for counterfactual learning. Nor do we understand how PFC populations interact with downstream structures to support this behavior. To dissect the neural circuit mechanisms of counterfactual learning, we propose to develop a novel counterfactual learning paradigm for mice using virtual reality. We will then apply computational modeling and optogenetic inactivation to determine which PFC regions are necessary for counterfactual learning. Once we identify the PFC structures causally involved in this task, we will investigate how these regions interact with downstream areas to encode counterfactual information, performing two-photon, cellular-resolution calcium imaging from populations of PFC neurons identified by their projection target. We can then optogenetically stimulate these projection-specific populations to test their role in counterfactual learning. Thus, by combining novel mouse behavioral paradigms, targeted inactivation and activation of neural activity and large-scale single-neuron calcium imaging, this proposal aims to reveal the neural circuit mechanisms of counterfactual learning. Identifying and characterizing the neural circuits that underlie this important cognitive function is not only vital for understanding how we learn in the real world, but will be important for understanding and treating the impaired counterfactual information processing that is a marker of several prevalent psychiatric diseases.

Document Details

Document Type
DoD Grant Award
Publication Date
May 07, 2018
Source ID
W911NF1710554

Entities

People

  • Ilana Witten

Organizations

  • Army Contracting Command
  • Princeton University
  • United States Army

Tags

Fields of Study

  • Biology

Readers

  • Neural Network Machine Learning.
  • Neuroscience

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks