Hierarchical Bayesian modeling and Markov chain Monte Carlo
نویسندگان
چکیده
A central theme of systems neuroscience is to characterize the tuning of neural responses to sensory 12 stimuli or the production of movement. Statistically, we often want to estimate the parameters of the 13 tuning curve, such as preferred direction, as well as the associated degree of uncertainty, characterized 14 by errorbars. Here we present a new sampling-based, Bayesian method that allows the estimation of 15 tuning curve parameters, the estimation of error bars, and hypothesis testing. This method also provides 16 a useful way of visualizing which tuning curves are compatible with the recorded data. We demonstrate 17 the utility of this approach using recordings of orientation and direction tuning in primary visual cortex, 18 direction of motion tuning in primary motor cortex, and simulated data. 19 20
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