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 estimation of tuning-curve parameters, the estimation of error bars, and hypothesis testing. This method also provides a useful way of visualizing which tuning curves are compatible with the recorded data. We demonstrate the utility of this approach using recordings of orientation and direction tuning in primary visual cortex, direction of motion tuning in primary motor cortex, and simulated data.
Cronin
Beau
B
Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Stevenson
Ian H
IH
Sur
Mriganka
M
Körding
Konrad P
KP
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D000882:Haplorhini; D008390:Markov Chains; D051379:Mice; D009010:Monte Carlo Method; D009043:Motor Activity / Q000502:physiology; D009044:Motor Cortex / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D009949:Orientation / Q000502:physiology; D016012:Poisson Distribution; D014793:Visual Cortex / Q000502:physiology