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Pragathi Priyadharsini Balasubramani

Researcher at University of California, San Diego

Publications -  41
Citations -  378

Pragathi Priyadharsini Balasubramani is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Cognition & Medicine. The author has an hindex of 11, co-authored 33 publications receiving 279 citations. Previous affiliations of Pragathi Priyadharsini Balasubramani include University of Rochester Medical Center & University of Rochester.

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An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning.

Pragathi Priyadharsini Balasubramani, +3 more
- 16 Apr 2014 - 
TL;DR: A model of risk based decision making in a modified Reinforcement Learning (RL)-framework that reconciles several existing theories of 5HT and DA in the BG is presented.
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A computational model of altered gait patterns in parkinson's disease patients negotiating narrow doorways.

Vignesh Muralidharan, +4 more
- 09 Jan 2014 - 
TL;DR: This model is the first to explain the non-dopamine dependence for FOG giving rise to several other possibilities for its etiology.
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A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making.

Pragathi Priyadharsini Balasubramani, +4 more
- 17 Jun 2015 - 
TL;DR: The model predicts that optimizing 5HT levels along with DA medications might be essential for improving the patients' reward-punishment learning deficits, and describes how DA and 5HT mediate activity in these classes of neurons (D1R, D2R-, D1R-D2R- MSNs).
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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3

Adam J. H. Newton, +567 more
- 18 Aug 2017 - 
TL;DR: In this article, the authors used FAPESP Research, Disseminations and Innovation Center for Neuromathematics (CNPq) for postdoctoral research.
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Computational model of precision grip in Parkinson's disease: a utility based approach.

Ankur Gupta, +2 more
- 02 Dec 2013 - 
TL;DR: The model is the first model of PG in PD conditions modeled using Reinforcement Learning with the significant difference that the action selection is performed using utility distribution instead of using purely Value-based distribution, thereby incorporating risk-based decision making.