Ramakrishnan Iyer, Ph.D.
Ram Iyer is a scientist in the modeling, analysis and theory team at the Allen Institute, where he is involved in efforts to develop theoretical and computational models of the mouse visual system. Working in close collaboration with the neural coding team, he hopes to develop models of neuronal dynamics at various scales, from individual neurons to large networks of interacting neurons and understand the underlying principles of neuronal information processing that would ultimately enable prediction of behavior. Prior to joining the Institute in 2011, Iyer obtained his B.S. and M.S. in physics from the Indian Institute of Technology, Kanpur, and his Ph.D. in theoretical high energy physics from University of Southern California under the guidance of Clifford V. Johnson. His thesis comprised investigation of interesting connections between low-dimensional quantum field theories, random matrices and integrable systems of non-linear coupled partial and ordinary differential equations. He has also worked on the non-equilibrium aspects of strongly coupled quantum theories with a view to understanding the dynamics of charged fluids using gravitational solutions.
Research Interests My research interests lie in understanding how the brain performs computations using various biophysical mechanisms and investigating interactions with neuronal networks using complex systems approaches. Specifically, I am interested in how these processes are integrated across various spatial and temporal scales to encode and decode information, leading to cognition and behavior. As part of current ongoing efforts, I have been involved in the development of a coarse-grained population statistic approach that can be used to efficiently simulate homogenous coupled neuronal populations using techniques from statistical physics. I have also been collaborating with members from the neural coding and modeling, analysis and theory groups to help develop models for the early mouse visual system.
- Theoretical and Computational Neuroscience
- Modeling, Analysis & Theory
Selected Publications View Publications
January 20, 2021
Siegle JH, Jia X, Durand S, Gale S, Bennett C, Graddis N, Heller G, Ramirez TK, Choi H, Luviano JA, Groblewski PA, Ahmed R, Arkhipov A, Bernard A, Billeh YN, Brown D, Buice MA, Cain N, Caldejon S, Casal L, Cho A, Chvilicek M, Cox TC, Dai K, Denman DJ, de Vries S., Dietzman R, Esposito L, Farrell C, Feng D, Galbraith J, Garrett M, Gelfand EC, Hancock N, Harris JA, Howard R, Hu B, Hytnen R, Iyer R, Jessett E, Johnson K, Kato I, Kiggins J, Lambert S, Lecoq J, Ledochowitsch P, Lee JH, Leon A, Li Y, Liang E, Long F, Mace K, Melchior J, Millman D, Mollenkopf T, Nayan C, Ng L, Ngo D, Nguyen T, Nicovich PR, North K, Ocker GK, Ollerenshaw D, Oliver M, Pachitariu M, Perkins J, Reding M, Reid D, Robertson M, Ronellenfitch K, Seid S, Slaughterbeck C, Stoecklin M, Sullivan D, Sutton B, Swapp J, Thompson C, Turner K, Wakeman W, Whitesell JD, Williams D, Williford A, Young R, Zeng H, Naylor S, Phillips JW, Reid RC, Mihalas S, Olsen SR, Koch C
Frontiers in Computational Neuroscience
April 23, 2020
Iyer R, Hu B, Mihalas S
March 5, 2020
Billeh YN, Cai B, Gratiy SL, Dai K, Iyer R, Gouwens NW, Abbasi-Asl R, Jia X, Siegle JH, Olsen SR, Koch C, Mihalas S, Arkhipov A
February 19, 2018
Teeter C, Iyer R, Menon V, Gouwens N, Feng D, Berg J, Szafer A, Cain N, Zeng H, Hawrylycz M, Koch C, Mihalas S
PLoS Computational Biology
November 12, 2018
Arkhipov A, Gouwens NW, Billeh YN, Gratiy S, Iyer R, Wei Z, Xu Z, Berg J, Buice M, Cain N, da Costa N, de Vries S, Denman D, Durand S, Feng D, Jarsky T, Lecoq J, Lee B, Li L, Mihalas S, Ocker GK, Olsen SR, Reid RC, Soler-LLavina G, Sorensen SA, Wang Q, Waters J, Scanziani M, Koch C
PLoS Computational Biology
October 24, 2013
Iyer R, Menon V, Buice M, Koch C, Mihalas S