Modeling Neural Switching via Drift-Diffusion Model

Neural encoding is a field in neuroscience that focuses on characterizing how information from stimuli is encoded in the spiking activity of neurons. When more than one stimulus is present, a theory known as multiplexing posits that neurons temporally switch between encoding various stimuli, creating a fluctuating firing pattern. Here, we propose a new statistical framework to test and analyze rate fluctuations of multiplexing neurons by proposing a state-space model for point process data. Specifically, we posit that multiplexing arises from competition between the stimuli, which are modeled as latent drift-diffusion processes. The proposed state-space model differs from most state space models in the statistical literature, as the state changes are continuous-time, non-Markovian, and endogenous. Consequently, typical state-space MCMC methods fail, so we provide a novel MCMC scheme to efficiently conduct inference in linear time. Using the proposed framework, we provide evidence of multiplexing within the inferior colliculus and novel insight into the switching dynamics from single-neuron recordings. Time permitting, I will also describe how we extend these single-neuron models to population-level models and discuss an adaptive generalized elliptical slice sampler that makes inference feasible in these high-dimensional settings.