Xue-Xin Wei

Direct recordings of grid-like neuronal activity in human spatial navigation

Grid cells in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats and monkeys, are believed to support a wide range of spatial behaviors. Recording neuronal activity from neurosurgical patients performing a virtual-navigation task, we identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.

A Bayesian observer model constrained by efficient coding can explain 'anti-Bayesian' percepts

Bayesian observer models provide a principled account of the fact that our perception of the world rarely matches physical reality. The standard explanation is that our percepts are biased toward our prior beliefs. However, reported psychophysical data suggest that this view may be simplistic. We propose a new model formulation based on efficient coding that is fully specified for any given natural stimulus distribution. The model makes two new and seemingly anti-Bayesian predictions. First, it predicts that perception is often biased away from an observers prior beliefs. Second, it predicts that stimulus uncertainty differentially affects perceptual bias depending on whether the uncertainty is induced by internal or external noise. We found that both model predictions match reported perceptual biases in perceived visual orientation and spatial frequency, and were able to explain data that have not been explained before. The model is general and should prove applicable to other perceptual variables and tasks.

Bayesian inference with efficient neural population codes

The accuracy with which the brain can infer the value of a stimulus variable depends on both the amount of stimulus information that is represented in sensory neurons (encoding) and the mechanism by which this information is subsequently retrieved from the responses of these neurons (decoding). Previous studies have mainly focused on either the encoding or the decoding aspect. Here, we present a new framework that functionally links the two. More specifically, we demonstrate that optimal (efficient) population codes which guarantee uniform firing rate distributions allow the accurate emulation of optimal (Bayesian) inference using a biophysically plausible neural mechanism. The framework provides predictions for estimation bias and variability as a function of stimulus prior, strength and integration time, as well as physiological parameters such as tuning curves and spontaneous firing rates. Our framework represents an example of the duality between representation and computation in neural information processing.

Lawful relation between perceptual bias and discriminability

Significance We present a law of human perception. The law expresses a mathematical relation between our ability to perceptually discriminate a stimulus from similar ones and our bias in the perceived stimulus value. We derived the relation based on theoretical assumptions about how the brain represents sensory information and how it interprets this information to create a percept. Our main assumption is that both encoding and decoding are optimized for the specific statistical structure of the sensory environment. We found large experimental support for the law in the literature, which includes biases and changes in discriminability induced by contextual modulation (e.g., adaptation). Our results imply that human perception generally relies on statistically optimized processes. Perception of a stimulus can be characterized by two fundamental psychophysical measures: how well the stimulus can be discriminated from similar ones (discrimination threshold) and how strongly the perceived stimulus value deviates on average from the true stimulus value (perceptual bias). We demonstrate that perceptual bias and discriminability, as functions of the stimulus value, follow a surprisingly simple mathematical relation. The relation, which is derived from a theory combining optimal encoding and decoding, is well supported by a wide range of reported psychophysical data including perceptual changes induced by contextual modulation. The large empirical support indicates that the proposed relation may represent a psychophysical law in human perception. Our results imply that the computational processes of sensory encoding and perceptual decoding are matched and optimized based on identical assumptions about the statistical structure of the sensory environment.

Dynamic self-organized error-correction of grid cells by border cells

Grid cells in the entorhinal cortex are believed to establish their regular, spatially correlated firing patterns by path integration of the animal’s motion. Mechanisms for path integration, e.g. in attractor network models, predict stochastic drift of grid responses, which is not observed experimentally. We demonstrate a biologically plausible mechanism of dynamic self-organization by which border cells, which fire at environmental boundaries, can correct such drift in grid cells. In our model, experience-dependent Hebbian plasticity during exploration allows border cells to learn connectivity to grid cells. Border cells in this learned network reset the phase of drifting grids. This error-correction mechanism is robust to environmental shape and complexity, including enclosures with interior barriers, and makes distinctive predictions for environmental deformation experiments. Our work demonstrates how diverse cell types in the entorhinal cortex could interact dynamically and adaptively to achieve robust path integration.

Perceptual adaptation: Getting ready for the future

Perceptual systems continually adapt to changes in their sensory environment. Adaptation has been mainly thought of as a mechanism to exploit the spatiotemporal regularities of the sensory input in order to efficiently represent sensory information. Thus, most computational explanations for adaptation can be conceptualized as a form of Efficient coding. We propose a novel and more holistic explanation. We argue that perceptual adaptation is a process with which the perceptual system adjusts its operational regime to be best possible prepared for the future, i.e. the next sensory input. Crucially, we assume that these adjustments affect both the way the system represents sensory information (encoding) and how it interprets that information (decoding). We apply this idea in the context of a Bayesian observer model. More specifically, we propose that the perceptual system tries to predict the probability distribution from which the next sensory input is drawn. It does so by exploiting the fact that the recent stimulus history is generally a good predictor of the future and that the overall long-term stimulus distribution is stationary. We assume that this predicted probability distribution reflects the updated prior belief of the Bayesian observer. In addition, we assume that the system is adjusting its sensory representation according to the predicted future stimulus distribution via Efficient coding. Because this sensory representation directly constrains the likelihood function, we can define an optimal Bayesian observer model for any predicted distribution over the next sensory input. We demonstrate that this model framework provides a natural account of the reported adaptation after-effects for visual orientation and spatial frequency, both in terms of discrimination thresholds and biases. It also allows us to predict how these after-effects depend on the specific form of the short- and long-term input histories. Meeting abstract presented at VSS 2015.

A new law of human perception

Perception is a subjective experience that depends on the expectations and beliefs of an observer1. Psychophysical measures provide an objective yet indirect characterization of this experience by describing the dependency between the physical properties of a stimulus and the corresponding perceptually guided behavior2. Two fundamental psychophysical measures characterize an observer’s perception of a stimulus: how well the observer can discriminate the stimulus from similar ones (discrimination threshold) and how strongly the observer’s perceived stimulus value deviates from the true stimulus value (perceptual bias). It has long been thought that these two perceptual characteristics are independent3. Here we demonstrate that discrimination threshold and perceptual bias show a surprisingly simple mathematical relation. The relation, which we derived from assumptions of optimal sensory encoding and decoding4, is well supported by a wide range of reported psychophysical data5–16 including perceptual changes induced by spatial17,18 and temporal19–23 context, and attention24. The large empirical support suggests that the proposed relation represents a new law of human perception. Our results imply that universal rules govern the computational processes underlying human perception.