Rimjhim Tomar, PhD
2026 Leon Levy Scholar in Neuroscience
Icahn School of Medicine at Mount Sinai
Sub-disciplinary Category
Computational Neuroscience
Previous Positions
- BS, University of Delhi
- MS, Indian Institute of Technology Madras
- PhD, Charles University and Institute of Physiology (Dr. Lubomir Kostal)
Bio
Dr. Rimjhim Tomar is a postdoctoral research associate in the Schaffer Lab for Neural Computation at the Icahn School of Medicine at Mount Sinai. With a background in Mathematics, her doctoral research at Charles University and the Institute of Physiology of the Czech Academy of Sciences developed statistical frameworks to quantify information encoding in single neurons and networks, with a focus on instantaneous firing rate dynamics. Her work was recognized with an early-career investigator grant from the Grant Agency of Charles University. Her current research in the Schaffer Lab asks how neural circuits transform this information through learning. By developing computational models of large-scale neural recordings combined with neuromodulatory receptor profiles of cortical neurons, she investigates how feedback signals guide synaptic plasticity in recurrent cortical networks and enable the brain to assign credit during learning.
Research Summary
Developing computational models to understand how neuromodulatory signals (chemical feedback) guide learning in the cerebral cortex.
Technical Overview
How the brain modifies the right synapses at the right time remains a central unsolved question in neuroscience, often referred to as the credit assignment problem. Backpropagation solves this in artificial networks, but this learning algorithm is not biologically plausible. Neuromodulators are strong candidates for carrying feedback signals, yet their diffuse action makes it unclear how they produce the selective, cell-specific updates learning requires. Recent transcriptomic studies show that neurons co-express dozens of G protein-coupled receptors (GPCRs), suggesting that combinatorial receptor logic may allow individual neurons to interpret global modulatory signals selectively and implement local credit assignment within a recurrent network.
Dr. Tomar develops neural network models to test this hypothesis, in close collaboration with experimentalists performing large-scale neural recordings and MERFISH-based molecular profiling, to identify how specific GPCR combinations govern feedback and credit assignment in the cortex. This work aims to establish the first quantitative link between molecular receptor diversity and circuit-level learning dynamics, with implications for plasticity deficits in a wide range of disorders, including addiction, ADHD, and Parkinson’s disease.
Learn about the The Leon Levy Scholarships in Neuroscience.