Theoretical and Computational Neuroscience, Information Theory, Network Science
We aim to develop optimality theories that explain experimentally-measured properties of neural systems, often drawing on approaches from information theory and network science. Further, we try to develop mathematical models of associative memory and creativity. We are specifically interested in analyzing connectomics datasets to understand principles of neural computation. We also do some work in neuroengineering to develop better ways to sample neural spike trains and to reconstruct connectivity patterns from multielectrode stimulation experiments. We are recently intrigued by the neural correlates of cognitive training.
B. He, A. Wein, L. R. Varshney, J. Kusuma, A. G. Richardson, and L. Srinivasan, “Generalized Analog Thresholding for Spike Acquisition at Ultra-Low Sampling Rates,” Journal of Neurophysiology, vol. 114, no. 1, pp. 746-760, July 2015.
H. Chen, L. R. Varshney, and P. K. Varshney, “Noise-Enhanced Information Systems,” Proceedings of the IEEE, vol. 102, pp. 1607–1621, Oct. 2014.
A. Karbasi, A. H. Salavati, A. Shokrollahi, and L. R. Varshney, “Noise Facilitation in Associative Memories of Exponential Capacity,” Neural Computation, vol. 26, pp. 2493–2526, Nov. 2014.
L. R. Varshney, “The Wiring Economy Principle for Designing Inference Networks,” IEEE Journal on Selected Areas in Communications, vol. 31, pp. 1095–1104, June 2013.
J. Z. Sun, G. I. Wang, V. K. Goyal, and L. R. Varshney, “A Framework for Bayesian Optimality of Psychophysical Laws,” Journal of Mathematical Psychology, vol. 56, pp. 495–501, Dec. 2012.
L. R. Varshney, B. L. Chen, E. Paniagua, D. H. Hall, and D. B. Chklovskii, “Structural Properties of the Caenorhabditis elegans Neuronal Network,” PLoS Computational Biology, vol. 7, e1001066, Feb. 2011.
L. R. Varshney, P. J. Sjöström, and D. B. Chklovskii, “Optimal Information Storage in Noisy Synapses under Resource Constraints,” Neuron, vol. 52, pp. 409–423, Nov. 2006.