Testing Poisson versus Poisson Mixtures with Application to Neuronscience.

Won ASA NC Chapter and AISC Young Researcher Award


Won ASA NC Chapter and AISC Young Researcher Award in AISC 2018.


November 8, 2018


12:00 AM


Greensboro, USA



A second order stochasticity may manifest in a single neuron’s activation patterns when preserving information from multiple stimuli (Caruso et al, 2018; Glynn et al., 2019). Considering two simultaneously presented stimuli for simplicity, we develop a hypothesis testing to test between distinct competing modes of second order stochastic variations, which corresponds to a testing of different types of Poisson mixtures with unknown mixing distributions. Under a Bayesian paradigm, the testing requires an integration of an infinite-dimensional nonparametric mixing distribution for the marginal likelihood estimation, bringing a practicability challenge. We circumvent this computing problem by adopting a pseudo-Bayesian framework of Predictive Recursion Marginal Likelihood (PRML) proposed by Martin and Tokdar (2011) and design a PRML-based test. We further introduce a modified test based on a clearer separation of model space induced by a truncation hyperparameter. Simulation results show our PRML-based test can characterize overdispersed distribution of spike counts under dual-stimuli, which is common in practice, and improve the confidence of classification greatly. We analyze spike trains of a single neuron in the inferior colliculus region, suggesting a single neuron in dual-stimuli trials does exhibit a second order stochasticity by randomly interweaving between constituent signals.

Posted on:
November 8, 2018
1 minute read, 194 words
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