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Alexandre Pouget

Publications and source records attributed to Alexandre Pouget.

12 recordsLinked to original sources
PubMed2006

Bayesian inference with probabilistic population codes.

Recent psychophysical experiments indicate that humans perform near-optimal Bayesian inference in a wide variety of tasks, ranging from cue integration to decision making to motor control. This implies that neurons both represent probability distributions and combine those distributions according to a close approximation to Bayes' rule. At first sight, it would seem that the high variability in the responses of cortical neurons would make it difficult to implement such optimal statistical inference in cortical circuits. We argue that, in fact, this variability implies that populations of neurons automatically represent probability distributions over the stimulus, a type of code we call probabilistic population codes. Moreover, we demonstrate that the Poisson-like variability observed in cortex reduces a broad class of Bayesian inference to simple linear combinations of populations of neural activity. These results hold for arbitrary probability distributions over the stimulus, for tuning curves of arbitrary shape and for realistic neuronal variability.

Algorithms↗
PubMed2006

Neural correlations, population coding and computation.

How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.

Action Potentials↗
PubMed2005

Reference frames for representing visual and tactile locations in parietal cortex.

The ventral intraparietal area (VIP) receives converging inputs from visual, somatosensory, auditory and vestibular systems that use diverse reference frames to encode sensory information. A key issue is how VIP combines those inputs together. We mapped the visual and tactile receptive fields of multimodal VIP neurons in macaque monkeys trained to gaze at three different stationary targets. Tactile receptive fields were found to be encoded into a single somatotopic, or head-centered, reference frame, whereas visual receptive fields were widely distributed between eye- to head-centered coordinates. These findings are inconsistent with a remapping of all sensory modalities in a common frame of reference. Instead, they support an alternative model of multisensory integration based on multidirectional sensory predictions (such as predicting the location of a visual stimulus given where it is felt on the skin and vice versa). This approach can also explain related findings in other multimodal areas.

Animals↗
PubMed2004

Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations.

Several studies have shown that the information conveyed by bell-shaped tuning curves increases as their width decreases, leading to the notion that sharpening of tuning curves improves population codes. This notion, however, is based on assumptions that the noise distribution is independent among neurons and independent of the tuning curve width. Here we reexamine these assumptions in networks of spiking neurons by using orientation selectivity as an example. We compare two principal classes of model: one in which the tuning curves are sharpened through cortical lateral interactions, and one in which they are not. We report that sharpening through lateral interactions does not improve population codes but, on the contrary, leads to a severe loss of information. In addition, the sharpening models generate complicated codes that rely extensively on pairwise correlations. Our study generates several experimental predictions that can be used to distinguish between these two classes of model.

Action Potentials↗
PubMed2004

The Bayesian brain: the role of uncertainty in neural coding and computation.

To use sensory information efficiently to make judgments and guide action in the world, the brain must represent and use information about uncertainty in its computations for perception and action. Bayesian methods have proven successful in building computational theories for perception and sensorimotor control, and psychophysics is providing a growing body of evidence that human perceptual computations are "Bayes' optimal". This leads to the "Bayesian coding hypothesis": that the brain represents sensory information probabilistically, in the form of probability distributions. Several computational schemes have recently been proposed for how this might be achieved in populations of neurons. Neurophysiological data on the hypothesis, however, is almost non-existent. A major challenge for neuroscientists is to test these ideas experimentally, and so determine whether and how neurons code information about sensory uncertainty.

Animals↗
PubMed2003

Inference and computation with population codes.

In the vertebrate nervous system, sensory stimuli are typically encoded through the concerted activity of large populations of neurons. Classically, these patterns of activity have been treated as encoding the value of the stimulus (e.g., the orientation of a contour), and computation has been formalized in terms of function approximation. More recently, there have been several suggestions that neural computation is akin to a Bayesian inference process, with population activity patterns representing uncertainty about stimuli in the form of probability distributions (e.g., the probability density function over the orientation of a contour). This paper reviews both approaches, with a particular emphasis on the latter, which we see as a very promising framework for future modeling and experimental work.

Animals↗
PubMed2003

Basis functions for object-centered representations.

In an object-centered representation, the position of the subparts of an object are encoded with respect to a set of axes and an origin centered on the object. Several physiological and neuropsychological results support the existence of such representations in humans and monkeys. An explicit representation would involve neurons with invariant response properties in object-centered coordinates. We consider an alternative scheme using basis functions in which the cells have retinotopic receptive fields modulated by the orientation of the object and task-related signals. We show that this alternative is consistent with single-cell data, is computationally efficient, and accounts for object-centered hemineglect, a syndrome observed in humans after fronto-parietal lesions.

Algorithms↗
PubMed2002

Multisensory spatial representations in eye-centered coordinates for reaching.

Humans can reach for objects with their hands whether the objects are seen, heard or touched. Thus, the position of objects is recoded in a joint-centered frame of reference regardless of the sensory modality involved. Our study indicates that this frame of reference is not the only one shared across sensory modalities. The location of reaching targets is also encoded in eye-centered coordinates, whether the targets are visual, auditory, proprioceptive or imaginary. Furthermore, the remembered eye-centered location is updated after each eye and head movement. This is quite surprising since, in principle, a reaching motor command can be computed from any non-visual modality without ever recovering the eye-centered location of the stimulus. This finding may reflect the predominant role of vision in human spatial perception.

Adult↗
PubMed2002

A computational perspective on the neural basis of multisensory spatial representations.

We argue that current theories of multisensory representations are inconsistent with the existence of a large proportion of multimodal neurons with gain fields and partially shifting receptive fields. Moreover, these theories do not fully resolve the recoding and statistical issues involved in multisensory integration. An alternative theory, which we have recently developed and review here, has important implications for the idea of 'frame of reference' in neural spatial representations. This theory is based on a neural architecture that combines basis functions and attractor dynamics. Basis function units are used to solve the recoding problem, whereas attractor dynamics are used for optimal statistical inferences. This architecture accounts for gain fields and partially shifting receptive fields, which emerge naturally as a result of the network connectivity and dynamics.

Animals↗
PubMedDate not supplied

Optimal computation with attractor networks.

We investigate the ability of multi-dimensional attractor networks to perform reliable computations with noisy population codes. We show that such networks can perform computations as reliably as possible--meaning they can reach the Cramér-Rao bound--so long as the noise is small enough. "Small enough" depends on the properties of the noise, especially its correlational structure. For many correlational structures, noise in the range of what is observed in the cortex is sufficiently small that biologically plausible networks can compute optimally. We demonstrate that this result applies to computations that involve cues of varying reliability, such as the position of an object on the retina in bright versus dim light.

Animals↗
PubMedDate not supplied

Bayesian multisensory integration and cross-modal spatial links.

Our perception of the word is the result of combining information between several senses, such as vision, audition and proprioception. These sensory modalities use widely different frames of reference to represent the properties and locations of object. Moreover, multisensory cues come with different degrees of reliability, and the reliability of a given cue can change in different contexts. The Bayesian framework--which we describe in this review--provides an optimal solution to deal with this issue of combining cues that are not equally reliable. However, this approach does not address the issue of frames of references. We show that this problem can be solved by creating cross-modal spatial links in basis function networks. Finally, we show how the basis function approach can be combined with the Bayesian framework to yield networks that can perform optimal multisensory combination. On the basis of this theory, we argue that multisensory integration is a dialogue between sensory modalities rather that the convergence of all sensory information onto a supra-modal area.

Animals↗