# CRCNS Research Proposal: Cortico-amygdalar substrates of adaptive learning > **NIH NIH R01** · DARTMOUTH COLLEGE · 2021 · $62,324 ## Abstract PROJECT DESCRIPTION 1. BACKGROUND AND SIGNIFICANCE Learning from feedback in the real w'orld is limited by constant fluctuations in reward outcomes associated with choosing certain options or actions. Some of these fluctuations are caused by fundamental changes in the reward values of those options/actions that necessitate dramatic adjustments to the current learning strategies, like in epiphany learning or one-shot learning [Chen & Krajbich, 2017; Lee et al. 2015]. Other changes represent inherent stochasticity in an otherwise stable environment and should be tolerated and ignored to maintain stable choice preferences. In other words, learning in dynamic environments is bounded by a tradeoff between being adaptable (i.e. respond quickly to changes in the environment) and being precise (i.e. update slowly after each feedback to be more accurate), which we refer to as the adaptability-precision tradeoff [Farashahi et al., 2017; Khorsand & Soltani, 2017]. Therefore, distinguishing meaningful changes in the environment from natural fluctuations can greatly enhance adaptive learning, indicating that adaptive learning depends on interactions between multiple brain areas. To date, most computational models of learning under uncertainty are very high-level and/or descriptive [Behrens et al., 2007; Costa et al., 2015; ligaya, 2016; Jang et al., 2015; Nassar et al., 201 O; Payzan-LeNestour & Bossaerts, 2011] and therefore, do not provide specific testable predictions. On the other hand, neural mechanisms of uncertainty monitoring for adaptive learning have been predominantly investigated in humans, and in a few cases monkeys, both of which are limited in terms of circuit-level manipulations. However, interactions between brain areas unfold on short timescales and can be specific to certain cell types. These properties have severely limited the ability of functional MRI [Logothetis, 2003] or MEG [Dale et al., 2000; Mostert et al., 2015] to reveal the microcircuit mechanisms within brain regions and fine-grained contributions between brain regions. To overcome these limitations and reveal neural mechanisms underlying adaptive learning under uncertainty, we propose a combination of detailed computational modeling, imaging of stable neuronal ensembles, and precise system-level manipulation of interactions between multiple brain areas in rodents. The latter is possible in part due to powerful circuit- dissection techniques in rodents that allow manipulations of genetically-tractable cell types and thus, specific projections between brain regions. Combined with decoding of neuronal activity in cortex and guided by mechanistic computational modeling, this approach enables us to investigate both microcircuit and system-level mechanisms of adaptive learning under uncertainty. We have recently proposed a mechanistic model for adaptive learning under uncertainty [Farashahi et al., 2017]. This model, which we refer to as reward-dependent metaplasticity (ROMP) model... ## Key facts - **NIH application ID:** 10455256 - **Project number:** 3R01DA047870-04S1 - **Recipient organization:** DARTMOUTH COLLEGE - **Principal Investigator:** Alicia Izquierdo - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $62,324 - **Award type:** 3 - **Project period:** 2018-09-15 → 2022-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10455256 ## Citation > US National Institutes of Health, RePORTER application 10455256, CRCNS Research Proposal: Cortico-amygdalar substrates of adaptive learning (3R01DA047870-04S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10455256. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*