Project Description

How does the brain use sensory (visual, tactile, proprioceptive) information to move around in and manipulate the world? How are the "algorithms" underlying these behaviors implemented in neural circuits? And what can we do when something goes wrong? Answering some of the most fundamental questions about how the brain works, like these, is now becoming feasible due to improved electrodes and to the ability to record with them from human patients. But making sense of experimental data will require, in addition to expertise in neuroscience, a theoretical framework and analytical tools, both of which increasingly come from electrical engineering and computer science.

In this project, the postdoc would collaborate with faculty in the ECE and BME departments, and with neurosurgeons (our external collaborators), to devise and test computational theories of neural function, including: designing experiments, analyzing data, formulating and simulating mathematical models, and improving algorithms. We are particularly interested in problems of sensorimotor processing, including applications to brain-machine interfaces; and the postdoc would be expected to draw on basic neurobiology, control theory, and statistical learning theory.

Start Date

Summer 2021

Postdoc Qualifications

The ideal candidate would have training in engineering, particularly dynamical systems and machine learning; and some background or strong interest in neuroscience.

Co-Advisors

Joseph Makin
jgmakin@purdue.edu
Assistant Professor, ECE
https://engineering.purdue.edu/MakinLab

Maria Dadarlat
mdadarla@purdue.edu
Assistant Professor, BME 

References

J.G. Makin, D. A. Moses, and E. F. Chang. Machine Translation of Cortical Activity to Text with an Encoder-Decoder Framework. Nature Neuroscience, 23:575–582, 2020.

J.G. Makin, J.E. O’Doherty, M.M.B. Cardoso, and P.N. Sabes. Superior Arm-Movement Decoding with a New, Unsupervised-Learning Algorithm. J. Neural Engin., 15(2), Jan. 2018.

M.C. Dadarlat and M.P. Stryker. Locomotion Enhances Neural Encoding of Visual Stimuli in Mouse V1. Journal of Neuroscience, 37(14):3764-3775, April 2017,

J.G. Makin*, B.K. Dichter*, and P.N. Sabes. Learning to Estimate Dynamical State with Probabilistic Population Codes. PLoS Computational Biology, 11(11), 2015.

M.C. Dadarlat, J.E. O'Doherty, and P.N. Sabes. A learning–based approach to artificial sensory feedback leads to optimal integration. Nat Neurosci., 18(1):138–144, Jan. 2015.