Biology subjects

Quax, S. C.

Publications and source records attributed to Quax, S. C..

4 recordsLinked to original sources

bioRxiv2020

Visual Attention Through Uncertainty Minimization in Recurrent Generative Models

Allocating visual attention through saccadic eye movements is a key ability of intelligent agents. Attention is both influenced through bottom-up stimulus properties as well as top-down task demands. The interaction of these two attention mechanisms is not yet fully understood. A parsimonious reconciliation posits that both processes serve the minimization of predictive uncertainty. We propose a recurrent generative neural network model that predicts a visual scene based on foveated glimpses. The model shifts its attention in order to minimize the uncertainty in its predictions. We show that the proposed model produces naturalistic eye movements focusing on informative stimulus regions. Introducing additional tasks modulates the saccade patterns towards task-relevant stimulus regions. The models saccade characteristics correspond well with previous experimental data in humans, providing evidence that uncertainty minimization could be a fundamental mechanisms for the allocation of visual attention.

neuroscience↗

bioRxiv2019

Adaptive time scales in recurrent neural networks

Recurrent neural network models have become widely used in computational neuroscience to model the dynamics of neural populations as well as in machine learning applications to model data with temporal dependencies. The different variants of RNNs commonly used in these scientific fields can be derived as discrete time approximations of the instantaneous firing rate of a population of neurons. The time constants of the neuronal process are generally ignored in these approximations, while learning these time constants could possibly inform us about the time scales underlying temporal processes and enhance the expressive capacity of the network. To investigate the potential of adaptive time constants, we compare the standard Elman approximation to a more lenient one that still accounts for the time scales at which processes unfold. We show that such a model with adaptive time scales performs better on predicting temporal data, increasing the memory capacity of recurrent neural networks, and allows recovery of the time scales at which the underlying processes unfold.

neuroscience↗

bioRxiv2019

Population codes of prior knowledge learned through environmental regularities

How the brain makes correct inferences about its environment based on noisy and ambiguous observations, is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment plays an important role in this process. Humans are able to incorporate such prior knowledge in an efficient, Bayes optimal, way in many situations, but it remains an open question how the brain acquires and represents this prior knowledge. The long time spans over which prior knowledge is acquired make it a challenging question to investigate experimentally. In order to guide future experiments with clear empirical predictions, we used a neural network model to learn two commonly used tasks in the experimental literature (i.e. orientation classification and orientation estimation) where the prior probability of observing a certain stimulus is manipulated. We show that a population of neurons learns to correctly represent and incorporate prior knowledge, by only receiving feedback about the accuracy of their inference from trial-to-trial and without any probabilistic feedback. We identify different factors that can influence the neural responses to unexpected or expected stimuli, and find a novel mechanism that changes the activation threshold of neurons, depending on the prior probability of the encoded stimulus. In a task where estimating the exact stimulus value is important, more likely stimuli also led to denser tuning curve distributions and narrower tuning curves, allocating computational resources such that information processing is enhanced for more likely stimuli. These results can explain several different experimental findings and clarify why some contradicting observations concerning the neural responses to expected versus unexpected stimuli have been reported and pose some clear and testable predictions about the neural representation of prior knowledge that can guide future experiments.

neuroscience↗

bioRxiv2016

Slice-sampled Bayesian PRF mapping

Functional magnetic resonance imaging (FMRI) allows to non-invasively measure human brain activity at the millimeter scale. As such, it is widely used in computational neuroimaging studies that aim to build models to predict stimulus-induced neural responses in visual cortex. A popular method is population receptive field (PRF) mapping, which is able to characterize responses to a large range of stimuli. For each voxel, the PRF method estimates the best fitting receptive field properties (such as location and size in the visual field) using a coarse-to-fine approach which minimizes, but not eliminates, the risk of returning a local minimum. Here, we provide a Bayesian approach to the PRF method based on the slice sampler. Using this approach, we provide estimates of receptive field properties while at the same time being able to quantify their uncertainty. We test the performance of conventional and Bayesian approaches on simulated and empirical data.

neuroscience↗