58 research outputs found
Measuring uncertainty in human visual segmentation
Segmenting visual stimuli into distinct groups of features and visual objects
is central to visual function. Classical psychophysical methods have helped
uncover many rules of human perceptual segmentation, and recent progress in
machine learning has produced successful algorithms. Yet, the computational
logic of human segmentation remains unclear, partially because we lack
well-controlled paradigms to measure perceptual segmentation maps and compare
models quantitatively. Here we propose a new, integrated approach: given an
image, we measure multiple pixel-based same--different judgments and perform
model--based reconstruction of the underlying segmentation map. The
reconstruction is robust to several experimental manipulations and captures the
variability of individual participants. We demonstrate the validity of the
approach on human segmentation of natural images and composite textures. We
show that image uncertainty affects measured human variability, and it
influences how participants weigh different visual features. Because any
putative segmentation algorithm can be inserted to perform the reconstruction,
our paradigm affords quantitative tests of theories of perception as well as
new benchmarks for segmentation algorithms.Comment: 27 pages, 9 figures, 4 appendix, 3 figures in appendi
An Algebraic Approach to Linear-Optical Schemes for Deterministic Quantum Computing
Linear-Optical Passive (LOP) devices and photon counters are sufficient to
implement universal quantum computation with single photons, and particular
schemes have already been proposed. In this paper we discuss the link between
the algebraic structure of LOP transformations and quantum computing. We first
show how to decompose the Fock space of N optical modes in finite-dimensional
subspaces that are suitable for encoding strings of qubits and invariant under
LOP transformations (these subspaces are related to the spaces of irreducible
unitary representations of U(N)). Next we show how to design in algorithmic
fashion
LOP circuits which implement any quantum circuit deterministically. We also
present some simple examples, such as the circuits implementing a CNOT gate and
a Bell-State Generator/Analyzer.Comment: new version with minor modification
Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics
Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience
Origin of information-limiting noise correlations
The ability to discriminate between similar sensory stimuli relies on the amount of information encoded in sensory neuronal populations. Such information can be substantially reduced by correlated trial-to-trial variability. Noise correlations have been measured across a wide range of areas in the brain, but their origin is still far from clear. Here we show analytically and with simulations that optimal computation on inputs with limited information creates patterns of noise correlations that account for a broad range of experimental observations while at same time causing information to saturate in large neural populations. With the example of a network of V1 neurons extracting orientation from a noisy image, we illustrate to our knowledge the first generative model of noise correlations that is consistent both with neurophysiology and with behavioral thresholds, without invoking suboptimal encoding or decoding or internal sources of variability such as stochastic network dynamics or cortical state fluctuations. We further show that when information is limited at the input, both suboptimal connectivity and internal fluctuations could similarly reduce the asymptotic information, but they have qualitatively different effects on correlations leading to specific experimental predictions. Our study indicates that noise at the sensory periphery could have a major effect on cortical representations in widely studied discrimination tasks. It also provides an analytical framework to understand the functional relevance of different sources of experimentally measured correlations
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