A Bayesian approach for characterizing direction tuning curves in the supplementary motor area of behaving monkeys.

Hadas Taubman , Eilon Vaadia , Rony Paz and Gal Chechik monkeys curves in the supplementary motor area of behaving A Bayesian approach for characterizing direction tuning

A Bayesian approach for characterizing direction tuning curves

22 Neural responses are commonly studied in terms of “tuning curves”, characterizing changes in neuronal 23 response as a function of a continuous stimulus parameter. In the motor system, neural responses to 24 movement-direction often follow a bell-shaped tuning curve, whose exact shape determines the properties of 25 neuronal movement coding. Estimating the shape of that tuning curve robustly...

Innovative Methodology A Bayesian approach for characterizing direction tuning curves in the supplementary motor area of behaving monkeys

Hadas Taubman, Eilon Vaadia, Rony Paz, and Gal Chechik The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel; Department of Medical Neurobiology, Faculty of Medicine, Institute for Medical Research Israel-Canada, Hebrew University, Jerusalem, Israel; The Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel; The Edmond and Lily...

Innovative Methodology Hierarchical Bayesian Modeling and Markov Chain Monte Carlo Sampling for Tuning-Curve Analysis

Cronin B, Stevenson IH, Sur M, Körding KP. Hierarchical Bayesian modeling and Markov chain Monte Carlo sampling for tuningcurve analysis. J Neurophysiol 103: 591–602, 2010. First published November 4, 2009; doi:10.1152/jn.00379.2009. A central theme of systems neuroscience is to characterize the tuning of neural responses to sensory stimuli or the production of movement. Statistically, we often...

Hierarchical Bayesian modeling and Markov chain Monte Carlo sampling for tuning-curve analysis.

A central theme of systems neuroscience is to characterize the tuning of neural responses to sensory stimuli or the production of movement. Statistically, we often want to estimate the parameters of the tuning curve, such as preferred direction, as well as the associated degree of uncertainty, characterized by error bars. Here we present a new sampling-based, Bayesian method that allows the est...