A central challenge in neuroscience is to understand how the connectivity patterns and dynamics of local and long-range synaptic inputs enable behaviorally-relevant computations in individual neurons. A fundamental computation that all mammals perform is determining the value of different outcomes, including their valence, or whether they are perceived as a gain or a loss. Behavioral economics provides a useful quantitative framework for describing how people and animals subjectively assign value to outcomes, and use those value estimates to make decisions. Here, we aim to understand the multi-regional neural circuit mechanisms by which economic variables driving decision-making are computed and represented by neurons in the brain. A hallmark of economic choice behavior is that people exhibit ?reference dependence,? wherein they evaluate outcomes as gains or losses relative to an internal reference point. A related phenomenon, called ?loss aversion,? refers to the observation that most people are more sensitive to losses than to equivalent gains. This proposal will combine state-of-the-art viral and transgenic approaches for circuit dissection, in vivo paired recordings of long-range synaptically connected neurons whose responses have been characterized during behavior, novel techniques for neurochemical sensing, high-throughput behavioral training of rats, and quantitative behavioral modeling to identify how neural representations of quantifiable cognitive variables -the reference point and loss aversion- derive from dynamics and patterns of local and long range synapses. Specifically, the proposed work will delineate the thalamocortical circuitry supporting reference-dependent computations, determine the circuit mechanisms of arithmetic subtraction of the reference point from value signals, and identify neuromodulatory systems driving individual variability in loss aversion. The results will bridge cellular, circuit, and systems-level descriptions of neural mechanisms underlying consequential economic judgments, while revealing general neural circuit motifs supporting arithmetic computations including summation, subtraction, and multiplication.
The goal of the proposed work is to understand how neural representations of cognitive variables are generated by multi-regional circuit and synaptic interactions. Here, we will integrate state of the art viral, transgenic, and circuit dissection approaches in rats, including characterization of long-range synaptic connectivity in vivo, with detailed behavioral modeling and a paradigm designed to evaluate hallmarks of economic choice behavior in humans. We will relate granular descriptions of long-range connectivity to neural dynamics driving decision-making behaviors, and in the process, characterize general circuit motifs supporting arithmetic computations in neural circuits, including summation, subtraction, and multiplication.
