Biology subjects

Roy, N. A.

Publications and source records attributed to Roy, N. A..

3 recordsLinked to original sources

bioRxiv2020

Mice alternate between discrete strategies during perceptual decision-making

Classical models of perceptual decision-making assume that subjects use a single, consistent strategy to form decisions, or that strategies evolve slowly over time. Here we present new analyses suggesting that this common view is incorrect. We analyzed data from mouse and human decision-making experiments and found that choice behavior relies on an interplay between multiple interleaved strategies. These strategies, characterized by states in a hidden Markov model, persist for tens to hundreds of trials before switching, and may alternate multiple times within a session. The identified mouse decision-making strategies were highly consistent across individuals and comprised a single "engaged" state, in which decisions relied heavily on the sensory stimulus, and several biased states in which errors frequently occurred. These results provide a powerful alternate explanation for "lapses" often observed in rodent psychophysical experiments, and suggest that standard measures of performance mask the presence of dramatic changes in strategy across trials.

neuroscience↗

bioRxiv2020

Extracting the Dynamics of Behavior in Decision-Making Experiments

Understanding how animals update their decision-making behavior over time is an important problem in neuroscience. Decision-making strategies evolve over the course of learning, and continue to vary even in well-trained animals. However, the standard suite of behavioral analysis tools is ill-equipped to capture the dynamics of these strategies. Here, we present a flexible method for characterizing time-varying behavior during decision-making experiments. We show that it successfully captures trial-to-trial changes in an animals sensitivity to not only task-relevant stimuli, but also task-irrelevant covariates such as choice, reward, and stimulus history. We use this method to derive insights from training data collected in mice, rats, and human subjects performing auditory discrimination and visual detection tasks. With this approach, we uncover the detailed evolution of an animals strategy during learning, including adaptation to time-varying task statistics, suppression of sub-optimal strategies, and shared behavioral dynamics between subjects within an experimental population.

neuroscience↗

bioRxiv2018

A comparison of deep learning and linear-nonlinear cascade approaches to neural encoding

A large body of work on neural encoding has focused on \"cascade\" type models such as the linear-nonlinear-Poisson (LNP) model. This approach seeks to describe the encoding process in terms of a series of stages: (1) projection of the stimulus onto a bank of linear filters; (2) a nonlinear function combining these filter outputs; and (3) a noisy spike generation process. Here we explore the relationship of the LNP modeling framework to more recent approaches arising from the deep learning literature. Specifically, we show that deep neural network (DNN) and convolutional neural network (CNN) models of neural activity sit firmly within the LNP framework, and correspond to particular parametrizations of the nonlinear stage of the LNP model. Using data from primate retina and primary visual cortex, we compare the performance of LNP models fit with deep learning methods to LNP models fit with traditional estimators, including spike-triggered covariance (STC), information-theoretic spike-triggered averaging and covariance (iSTAC), and maximum likelihood estimators also known as \"maximally informative dimensions\" (MID). We show that models with nonlinearities parametrized by deep networks achieve higher accuracy for a fixed number of filters, and can extract a larger number of informative filters than traditional models. Finally, we perform a dimensionality analysis of LNP models trained with deep learning methods, revealing that a large number of filters are needed to accurately describe the neural responses of many cells even early in the visual pathway. This result overturns one of the central tenets of the LNP modeling framework: that neural computations are low-dimensional, or depend on the stimulus only via its projection onto a small number of linear filters. We discuss the implications of these findings for both the fitting and interpretation of LNP encoding models.

neuroscience↗