Pattern-stimulation and large-scale activity imaging both require methods for synthetically generating and analyzing multiple spike trains, taking into account features like their correlation structure. We have introduced a strategy based on correlation distortions in the Linear-Nonlinear-Poisson (LNP) model for generating multiple spike trains with an exactly controlled correlation structure, and for identifying neural encoding models and directed information flow in networks of neurons.
Selected publications:
- Aharoni T. & Shoham S., Phase-controlled, speckle-free holographic projection with applications in precision optogenetics, Neurophotonics, 5(2), 025004 (2018). doi: 10.1117/1.NPh.5.2.025004.
- Tankus A., Fried I. & Shoham S., Cognitive-motor brain–machine interfaces, J. Physiology-Paris, ISSN 0928-4257 (2014)
- Tankus A, Fried I & Shoham S, Sparse decoding of multiple spike trains for brain-machine interfaces, J Neural Eng 9(5), 054001 (2012)
- Krumin M, Reutsky I & Shoham S, Correlation-based analysis and generation of multiple spike trains using Hawkes models with an exogenous input, Frontiers in Computational Neuroscience, 4:147 (2010)
- Krumin M & Shoham S, Multivariate auto-regressive modeling and Granger causality analysis of multiple spike trains, Computational Intelligence and Neuroscience (2010)
- Golan L. & Shoham S., Speckle elimination using shift-averaging in high-rate holographic projection, Optics Express, 17(3), 1330-1339 (2009). doi: 10.1364/OE.17.001330.
Neural Interface Engineering Lab 