Robust and scalable Bayesian analysis of spatial neural tuning function data
| Autor: | Kamiar Rahnama Rad, Liam Paninski, Timothy A. Machado |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences Computer science scalability and robustness Bayesian probability Machine Learning (stat.ML) 02 engineering and technology Bayesian Statistics - Computation spatial statistics 03 medical and health sciences symbols.namesake 0302 clinical medicine Robustness (computer science) Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering posterior sampling Computation (stat.CO) Block (data storage) Computational neuroscience Quantitative Biology::Neurons and Cognition Function (mathematics) Modeling and Simulation Scalability symbols 020201 artificial intelligence & image processing Statistics Probability and Uncertainty Algorithm 030217 neurology & neurosurgery Curse of dimensionality Gibbs sampling |
| Zdroj: | Ann. Appl. Stat. 11, no. 2 (2017), 598-637 |
| DOI: | 10.48550/arxiv.1606.07845 |
| Popis: | A common analytical problem in neuroscience is the interpretation of neural activity with respect to sensory input or behavioral output. This is typically achieved by regressing measured neural activity against known stimuli or behavioral variables to produce a “tuning function” for each neuron. Unfortunately, because this approach handles neurons individually, it cannot take advantage of simultaneous measurements from spatially adjacent neurons that often have similar tuning properties. On the other hand, sharing information between adjacent neurons can errantly degrade estimates of tuning functions across space if there are sharp discontinuities in tuning between nearby neurons. In this paper, we develop a computationally efficient block Gibbs sampler that effectively pools information between neurons to denoise tuning function estimates while simultaneously preserving sharp discontinuities that might exist in the organization of tuning across space. This method is fully Bayesian, and its computational cost per iteration scales sub-quadratically with total parameter dimensionality. We demonstrate the robustness and scalability of this approach by applying it to both real and synthetic datasets. In particular, an application to data from the spinal cord illustrates that the proposed methods can dramatically decrease the experimental time required to accurately estimate tuning functions. |
| Databáze: | OpenAIRE |
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