Horwitz, G. D. | Search bioRxiv

bioRxiv2018

Focal optogenetic suppression in macaque area MT biases direction discrimination and choice confidence, but only transiently

Insights from causal manipulations of brain activity depend on targeting the spatial and temporal scale most relevant for behavior. Using a sensitive perceptual decision task in monkeys, we examined the effects of randomly-interleaved, rapid, reversible inactivation on a spatial scale previously achieved only with electrical microstimulation. Inactivating neurons in area MT with consistent direction tuning produced systematic effects on choice and confidence. Behavioral effects were attenuated over the course of each session, suggesting compensatory adjustments in the downstream readout of MT over tens of minutes. Compensation also occurred on a sub-second time scale: behavior was largely unaffected on trials with visual stimuli (and concurrent suppression) longer than [~]350ms. These trends were similar for choice and confidence, consistent with the idea of a common mechanism underlying both measures. The findings demonstrate the utility of hyperpolarizing opsins for linking neural population activity at fine spatial and temporal scales to cognitive functions in primates.

neuroscience↗

bioRxiv2017

Dethroning the Fano Factor: a flexible, model-based approach to partitioning neural variability

Neurons in many brain areas exhibit high trial-to-trial variability, with spike counts that are over-dispersed relative to a Poisson distribution. Recent work (Goris et al., 2014) has proposed to explain this variability in terms of a multiplicative interaction between a stochastic gain variable and a stimulus-dependent Poisson firing rate, which produces quadratic relationships between spike count mean and variance. Here we examine this quadratic assumption and propose a more flexible family of models that can account for a more diverse set of mean-variance relationships. Our model contains additive Gaussian noise that is transformed nonlinearly to produce a Poisson spike rate. Different choices of the nonlinear function can give rise to qualitatively different mean-variance relationships, ranging from sub-linear to linear to multiplicative. Intriguingly, a rectified squaring nonlinearity produces a linear mean-variance function, corresponding to responses with constant Fano factor. We describe a computationally efficient method for fitting this model to data, and demonstrate that a majority of neurons in a V1 population are better described by a model with non-quadratic relationship between mean and variance. Lastly, we develop an application to Bayesian adaptive stimulus selection in closed-loop neurophysiology experiments, which shows that accounting for overdispersion can lead to dramatic improvements in adaptive tuning curve estimation.

neuroscience↗