Richard E. Turner

Publications (5)6.77 Total impact

• Source

  Available from: PubMed Central

  Article: A structured model of video reproduces primary visual cortical organisation.

  Pietro Berkes, Richard E Turner, Maneesh Sahani
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  ABSTRACT: The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.
  PLoS Computational Biology 10/2009; 5(9):e1000495. · 5.22 Impact Factor
• Article: A statistical, formant-pattern model for segregating vowel type and vocal-tract length in developmental formant data.

  Richard E Turner, Thomas C Walters, Jessica J M Monaghan, Roy D Patterson
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  ABSTRACT: This paper investigates the theoretical basis for estimating vocal-tract length (VTL) from the formant frequencies of vowel sounds. A statistical inference model was developed to characterize the relationship between vowel type and VTL, on the one hand, and formant frequency and vocal cavity size, on the other. The model was applied to two well known developmental studies of formant frequency. The results show that VTL is the major source of variability after vowel type and that the contribution due to other factors like developmental changes in oral-pharyngeal ratio is small relative to the residual measurement noise. The results suggest that speakers adjust the shape of the vocal tract as they grow to maintain a specific pattern of formant frequencies for individual vowels. This formant-pattern hypothesis motivates development of a statistical-inference model for estimating VTL from formant-frequency data. The technique is illustrated using a third developmental study of formant frequencies. The VTLs of the speakers are estimated and used to provide a more accurate description of the complicated relationship between VTL and glottal pulse rate as children mature into adults.
  The Journal of the Acoustical Society of America 05/2009; 125(4):2374-86. · 1.55 Impact Factor
• Source

  Available from: citeseerx.ist.psu.edu

  Chapter: Probabilistic Amplitude Demodulation

  Richard E. Turner, Maneesh Sahani
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  ABSTRACT: Auditory scene analysis is extremely challenging. One approach, perhaps that adopted by the brain, is to shape useful representations of sounds on prior knowledge about their statistical structure. For example, sounds with harmonic sections are common and so time-frequency representations are efficient. Most current representations concentrate on the shorter components. Here, we propose representations for structures on longer time-scales, like the phonemes and sentences of speech. We decompose a sound into a product of processes, each with its own characteristic time-scale. This demodulation cascade relates to classical amplitude demodulation, but traditional algorithms fail to realise the representation fully. A new approach, probabilistic amplitude demodulation, is shown to out-perform the established methods, and to easily extend to representation of a full demodulation cascade.
  01/1970: pages 544-551;
• Article: Learning complex tasks with probabilistic population codes

  Richard E. Turner, Pietro Berkes, Jozsef Fiser
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  ABSTRACT: Recent psychophysical experiments imply that the brain employs a neural representation of the uncertainty in sensory stimuli and that probabilistic computations are supported by the cortex. Several candidate neural codes for uncertainty have been posited including Probabilistic Population Codes (PPCs). PPCs support various versions of probabilistic inference and marginalisation in a neurally plausible manner. However, in order to establish whether PPCs can be of general use, three important limitations must be addressed. First, it is critical that PPCs support learning. For example, during cue combination, subjects are able to learn the uncertainties associated with the sensory cues as well as the prior distribution over the stimulus. However, previous modelling work with PPCs requires these parameters to be carefully set by hand. Second, PPCs must be able to support inference in non-linear models. Previous work has focused on linear models and it is not clear whether non-linear models can be implemented in a neurally plausible manner. Third, PPCs must be shown to scale to high-dimensional problems with many variables. This contribution addresses these three limitations of PPCs by establishing a connection with variational Expectation Maximisation (vEM). In particular, we show that the usual PPC update for cue combination can be interpreted as the E-Step of a vEM algorithm. The corresponding M-Step then automatically provides a method for learning the parameters of the model by adapting the connection strengths in the PPC network in an unsupervised manner. Using a version of sparse coding as an example, we show that the vEM interpretation of PPC can be extended to non-linear and multi-dimensional models and we show how the approach scales with the dimensionality of the problem. Our results provide a rigorous assessment of the ability of PPCs to capture the probabilistic computations performed in the cortex.
  Nature Precedings.
• Source

  Available from: nature.com

  Article: The Army of One (Sample): the Characteristics of Sampling-based Probabilistic Neural Representations

  Pietro Berkes, Richard E. Turner, Jozsef Fiser
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  ABSTRACT: There is growing evidence that humans and animals represent the uncertainty associated with sensory stimuli and utilize this uncertainty during planning and decision making in a statistically optimal way. Recently, a nonparametric framework for representing probabilistic information has been proposed whereby neural activity encodes samples from the distribution over external variables. Although such sample-based probabilistic representations have strong empirical and theoretical support, two major issues need to be clarified before they can be considered as viable candidate theories of cortical computation. First, in a fluctuating natural environment, can neural dynamics provide sufficient samples to accurately estimate a stimulus? Second, can such a code support accurate learning over biologically plausible time-scales? Although it is well known that sampling is statistically optimal if the number of samples is unlimited, biological constraints mean that estimation and learning in the cortex must be supported by a relatively small number of possibly dependent samples. We explored these issues in a cue combination task by comparing a neural circuit that employed a sampling-based representation to an optimal estimator. For static stimuli, we found that a single sample is sufficient to obtain an estimator with less than twice the optimal variance, and that performance improves with the inverse square root of the number of samples. For dynamic stimuli, with linear-Gaussian evolution, we found that the efficiency of the estimation improves significantly as temporal information stabilizes the estimate, and because sampling does not require a burn-in phase. Finally, we found that using a single sample, the dynamic model can accurately learn the parameters of the input neural populations up to a general scaling factor, which disappears for modest sample size. These results suggest that sample-based representations can support estimation and learning using a relatively small number of samples and are therefore highly feasible alternatives for performing probabilistic cortical computations.
  Nature Precedings.