Efficient Coding Hypothesis Research Articles

Information Flow in Biological Networks for Color Vision

Biological neural networks for color vision (also known as color appearance models) consist of a cascade of linear + nonlinear layers that modify the linear measurements at the retinal photo-receptors leading to an internal (nonlinear) representation of color that correlates with psychophysical experience. The basic layers of these networks include: (1) chromatic adaptation (normalization of the mean and covariance of the color manifold); (2) change to opponent color channels (PCA-like rotation in the color space); and (3) saturating nonlinearities to obtain perceptually Euclidean color representations (similar to dimension-wise equalization). The Efficient Coding Hypothesis argues that these transforms should emerge from information-theoretic goals. In case this hypothesis holds in color vision, the question is what is the coding gain due to the different layers of the color appearance networks? In this work, a representative family of color appearance models is analyzed in terms of how the redundancy among the chromatic components is modified along the network and how much information is transferred from the input data to the noisy response. The proposed analysis is performed using data and methods that were not available before: (1) new colorimetrically calibrated scenes in different CIE illuminations for the proper evaluation of chromatic adaptation; and (2) new statistical tools to estimate (multivariate) information-theoretic quantities between multidimensional sets based on Gaussianization. The results confirm that the efficient coding hypothesis holds for current color vision models, and identify the psychophysical mechanisms critically responsible for gains in information transference: opponent channels and their nonlinear nature are more important than chromatic adaptation at the retina.

Visual Information flow in Wilson-Cowan networks

In this paper, we study the communication efficiency of a psychophysically tuned cascade of Wilson-Cowan and divisive normalization layers that simulate the retina-V1 pathway. This is the first analysis of Wilson-Cowan networks in terms of multivariate total correlation. The parameters of the cortical model have been derived through the relation between the steady state of the Wilson-Cowan model and the divisive normalization model. The communication efficiency has been analyzed in two ways: First, we provide an analytical expression for the reduction of the total correlation among the responses of a V1-like population after the application of the Wilson-Cowan interaction. Second, we empirically study the efficiency with visual stimuli and statistical tools that were not available before 1) we use a recent, radiometrically calibrated, set of natural scenes, and 2) we use a recent technique to estimate the multivariate total correlation in bits from sets of visual responses, which only involves univariate operations, thus giving better estimates of the redundancy. The theoretical and the empirical results show that, although this cascade of layers was not optimized for statistical independence in any way, the redundancy between the responses gets substantially reduced along the neural pathway. Specifically, we show that 1) the efficiency of a Wilson-Cowan network is similar to its equivalent divisive normalization model; 2) while initial layers (Von Kries adaptation and Weber-like brightness) contribute to univariate equalization, and the bigger contributions to the reduction in total correlation come from the computation of nonlinear local contrast and the application of local oriented filters; and 3) psychophysically tuned models are more efficient (reduce more total correlation) in the more populated regions of the luminance-contrast plane. These results are an alternative confirmation of the efficient coding hypothesis for the Wilson-Cowan systems, and, from an applied perspective, they suggest that neural field models could be an option in image coding to perform image compression.NEW & NOTEWORTHY The Wilson-Cowan interaction is analyzed in total correlation terms for the first time. Theoretical and empirical results show that this psychophysically tuned interaction achieves the biggest efficiency in the most frequent region of the image space. This is an original confirmation of the efficient coding hypothesis and suggests that neural field models can be an alternative to divisive normalization in image compression.

Talker normalization is mediated by structured indexical information.

Speech perception is challenged by indexical variability. A litany of studies on talker normalization have demonstrated that hearing multiple talkers incurs processing costs (e.g., lower accuracy, increased response time) compared to hearing a single talker. However, when reframing these studies in terms of stimulus structure, it is evident that past tests of multiple-talker (i.e., low structure) and single-talker (i.e., high structure) conditions are not representative of the graded nature of indexical variation in the environment. Here we tested the hypothesis that processing costs incurred by multiple-talker conditions would abate given increased stimulus structure. We tested this hypothesis by manipulating the degree to which talkers' voices differed acoustically (Experiment 1) and also the frequency with which talkers' voices changed (Experiment 2) in multiple-talker conditions. Listeners performed a speeded classification task for words containing vowels that varied in acoustic-phonemic ambiguity. In Experiment 1, response times progressively decreased as acoustic variability among talkers' voices decreased. In Experiment 2, blocking talkers within mixed-talker conditions led to more similar response times among single-talker and multiple-talker conditions. Neither result interacted with acoustic-phonemic ambiguity of the target vowels. Thus, the results showed that indexical structure mediated the processing costs incurred by hearing different talkers. This is consistent with the Efficient Coding Hypothesis, which proposes that sensory and perceptual processing are facilitated by stimulus structure. Defining the roles and limits of stimulus structure on speech perception is an important direction for future research.

Adaptive Efficient Coding of Correlated Acoustic Properties.

Natural sounds such as vocalizations often have covarying acoustic attributes, resulting in redundancy in neural coding. The efficient coding hypothesis proposes that sensory systems are able to detect such covariation and adapt to reduce redundancy, leading to more efficient neural coding. Recent psychoacoustic studies have shown the auditory system can rapidly adapt to efficiently encode two covarying dimensions as a single dimension, following passive exposure to sounds in which temporal and spectral attributes covaried in a correlated fashion. However, these studies observed a cost to this adaptation, which was a loss of sensitivity to the orthogonal dimension. Here we explore the neural basis of this psychophysical phenomenon by recording single-unit responses from the primary auditory cortex in awake ferrets exposed passively to stimuli with two correlated attributes, similar in stimulus design to the psychoacoustic experiments in humans. We found: (1) the signal-to-noise ratio of spike-rate coding of cortical responses driven by sounds with correlated attributes remained unchanged along the exposure dimension, but was reduced along the orthogonal dimension; (2) performance of a decoder trained with spike data to discriminate stimuli along the orthogonal dimension was equally reduced; (3) correlations between neurons tuned to the two covarying attributes decreased after exposure; and (4) these exposure effects still occurred if sounds were correlated along two acoustic dimensions, but varied randomly along a third dimension. These neurophysiological results are consistent with the efficient coding hypothesis and may help deepen our understanding of how the auditory system encodes and represents acoustic regularities and covariance.SIGNIFICANCE STATEMENT The efficient coding (EC) hypothesis (Attneave, 1954; Barlow, 1961) proposes that the neural code in sensory systems efficiently encodes natural stimuli by minimizing the number of spikes to transmit a sensory signal. Results of recent psychoacoustic studies in humans are consistent with the EC hypothesis in that, following passive exposure to stimuli with correlated attributes, the auditory system rapidly adapts so as to more efficiently encode the two covarying dimensions as a single dimension. In the current neurophysiological experiments, using a similar stimulus design and the experimental paradigm to the psychoacoustic studies of Stilp et al. (2010) and Stilp and Kluender (2011, 2012, 2016), we recorded responses from single neurons in the auditory cortex of the awake ferret, showing adaptive efficient neural coding of two correlated acoustic attributes.

On the relationship between optical variability, visual saliency, and eye fixations: A computational approach

A hierarchical definition of optical variability is proposed that links physical magnitudes to visual saliency and yields a more reductionist interpretation than previous approaches. This definition is shown to be grounded on the classical efficient coding hypothesis. Moreover, we propose that a major goal of contextual adaptation mechanisms is to ensure the invariance of the behavior that the contribution of an image point to optical variability elicits in the visual system. This hypothesis and the necessary assumptions are tested through the comparison with human fixations and state-of-the-art approaches to saliency in three open access eye-tracking datasets, including one devoted to images with faces, as well as in a novel experiment using hyperspectral representations of surface reflectance. The results on faces yield a significant reduction of the potential strength of semantic influences compared to previous works. The results on hyperspectral images support the assumptions to estimate optical variability. As well, the proposed approach explains quantitative results related to a visual illusion observed for images of corners, which does not involve eye movements.

Lakoff's roads not taken

This paper is a critique of George Lakoff's theory and practice as presented in his "Contemporary Theory of Metaphor" (Lakoff 1993). It addresses the issue on several planes, on each plane comparing Lakoff's approach to some alternative. The highest plane, affording the widest perspective, concerns two approaches to interpretation and scientific thinking: one that relies on a pre-established set of meanings, and one that assumes that "all the work remains to be done in each particular case ". The two approaches involve different cognitive strategies, rapid and delayed conceptualization. Another plane concerns the cognitive explanation for using spatial images in metaphoric and symbolic processes. Here the "embodied-mind hypothesis" is confronted with the "efficient-coding hypothesis". It is argued that the latter is more adequate, and can better account for the mental flexibility required for "delayed conceptualization ". On the third plane, Lakoff's "Contemporary Theory of Metaphor" is compared to Beardsley's "Controversion Theory of Metaphor". I will assert that precisely in those respects in which Lakoff claims superiority for his theory it is inferior to Beardsley's. On the most concrete plane, Lakoff's handling of three texts is considered, two literary and one nonliter-ary. It is argued that in two cases Lakoff's conceptual apparatus is less than adequate to handle the arising problems; in the third case it allows him to say about the text exactly what every critic would have said about it for the past seven hundred years.