Biomedical subjects

Aapo Hyvärinen

Publications and source records attributed to Aapo Hyvärinen.

10 recordsLinked to original sources

PubMed2006

Collinear context (and learning) change the profile of the perceptual filter.

The effect of collinear context on the filter mediating the detection of a Gabor stimulus was investigated by using the classification image method. Classification images were estimated for a 1.5 cpd horizontal Gabor target and the same target flanked by two collinear Gabors horizontally 1.7 degrees displaced from the target. The target was masked by a low-contrast white-noise mask. Obtained classification images were fitted by Gabor functions. The results show that collinear flankers increase the length of the classification image profiles along the collinear axis. At the same time, modest facilitory effects were observed in most subjects. The specificity and the amount of context-induced elongation in the classification images makes it hard to be explained by uncertainty reduction alone. In previous studies, collinear facilitation has been reported to abolish due to perceptual learning. We report a possibly related phenomenon: classification image data was re-analyzed in two parts consisting of the early and the late trials. In the latter trials, differences between the classification images in flankers and no-flankers condition are no longer significant.

Contrast Sensitivity↗

PubMed2006

Consistency of pseudolikelihood estimation of fully visible Boltzmann machines.

A Boltzmann machine is a classic model of neural computation, and a number of methods have been proposed for its estimation. Most methods are plagued by either very slow convergence or asymptotic bias in the resulting estimates. Here we consider estimation in the basic case of fully visible Boltzmann machines. We show that the old principle of pseudolikelihood estimation provides an estimator that is computationally very simple yet statistically consistent.

Algorithms↗

PubMed2005

Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2.

BACKGROUND: It has been shown that the classical receptive fields of simple and complex cells in the primary visual cortex emerge from the statistical properties of natural images by forcing the cell responses to be maximally sparse or independent. We investigate how to learn features beyond the primary visual cortex from the statistical properties of modelled complex-cell outputs. In previous work, we showed that a new model, non-negative sparse coding, led to the emergence of features which code for contours of a given spatial frequency band. RESULTS: We applied ordinary independent component analysis to modelled outputs of complex cells that span different frequency bands. The analysis led to the emergence of features which pool spatially coherent across-frequency activity in the modelled primary visual cortex. Thus, the statistically optimal way of processing complex-cell outputs abandons separate frequency channels, while preserving and even enhancing orientation tuning and spatial localization. As a technical aside, we found that the non-negativity constraint is not necessary: ordinary independent component analysis produces essentially the same results as our previous work. CONCLUSION: We propose that the pooling that emerges allows the features to code for realistic low-level image features related to step edges. Further, the results prove the viability of statistical modelling of natural images as a framework that produces quantitative predictions of visual processing.

Models, Statistical↗

PubMed2004

Validating the independent components of neuroimaging time series via clustering and visualization.

Recently, independent component analysis (ICA) has been widely used in the analysis of brain imaging data. An important problem with most ICA algorithms is, however, that they are stochastic; that is, their results may be somewhat different in different runs of the algorithm. Thus, the outputs of a single run of an ICA algorithm should be interpreted with some reserve, and further analysis of the algorithmic reliability of the components is needed. Moreover, as with any statistical method, the results are affected by the random sampling of the data, and some analysis of the statistical significance or reliability should be done as well. Here we present a method for assessing both the algorithmic and statistical reliability of estimated independent components. The method is based on running the ICA algorithm many times with slightly different conditions and visualizing the clustering structure of the obtained components in the signal space. In experiments with magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) data, the method was able to show that expected components are reliable; furthermore, it pointed out components whose interpretation was not obvious but whose reliability should incite the experimenter to investigate the underlying technical or physical phenomena. The method is implemented in a software package called Icasso.

Algorithms↗

PubMed2003

Independent component analysis of nondeterministic fMRI signal sources.

Neuronal activation can be separated from other signal sources of functional magnetic resonance imaging (fMRI) data by using independent component analysis (ICA). Without deliberate neuronal activity of the brain cortex, the fMRI signal is a stochastic sum of various physiological and artifact related signal sources. The ability of spatial-domain ICA to separate spontaneous physiological signal sources was evaluated in 15 anesthetized children known to present prominent vasomotor fluctuations in the functional cortices. ICA separated multiple clustered signal sources in the primary sensory areas in all of the subjects. The spatial distribution and frequency spectra of the signal sources correspond to the known properties of 0.03-Hz very-low-frequency vasomotor waves in fMRI data. In addition, ICA was able to separate major artery and sagittal sinus related signal sources in each subject. The characteristics of the blood vessel related signal sources were different from the parenchyma sources. ICA analysis of fMRI can be used for both assessing the statistical independence of brain signals and segmenting nondeterministic signal sources for further analysis.

Artifacts↗

PubMed2003

Simple-cell-like receptive fields maximize temporal coherence in natural video.

Recently, statistical models of natural images have shown the emergence of several properties of the visual cortex. Most models have considered the nongaussian properties of static image patches, leading to sparse coding or independent component analysis. Here we consider the basic time dependencies of image sequences instead of their nongaussianity. We show that simple-cell-type receptive fields emerge when temporal response strength correlation is maximized for natural image sequences. Thus, temporal response strength correlation, which is a nonlinear measure of temporal coherence, provides an alternative to sparseness in modeling simple-cell receptive field properties. Our results also suggest an interpretation of simple cells in terms of invariant coding principles, which have previously been used to explain complex-cell receptive fields.

Animals↗

PubMed2003

Bubbles: a unifying framework for low-level statistical properties of natural image sequences.

Recently, different models of the statistical structure of natural images have been proposed. These models predict properties of biological visual systems and can be used as priors in Bayesian inference. The fundamental model is independent component analysis, which can be estimated by maximization of the sparsenesses of linear filter outputs. This leads to the emergence of principal simple cell properties. Alternatively, simple cell properties are obtained by maximizing the temporal coherence in natural image sequences. Taking account of the basic dependencies of linear filter outputs permit modeling of complex cells and topographic organization as well. We propose a unifying framework for these statistical properties, based on the concept of spatiotemporal activity "bubbles."A bubble means here an activation of simple cells (linear filters) that is contiguous both in space (the cortical surface) and in time.

Bayes Theorem↗

PubMed2003

Temporal and spatiotemporal coherence in simple-cell responses: a generative model of natural image sequences.

We present a two-layer dynamic generative model of the statistical structure of natural image sequences. The second layer of the model is a linear mapping from simple-cell outputs to pixel values, as in most work on natural image statistics. The first layer models the dependencies of the activity levels (amplitudes or variances) of the simple cells, using a multivariate autoregressive model. The second layer shows the emergence of basis vectors that are localized, oriented and have different scales, just like in previous work. But in our new model, the first layer learns connections between the simple cells that are similar to complex cell pooling: connections are strong among cells with similar preferred location, frequency and orientation. In contrast to previous work in which one of the layers needed to be fixed in advance, the dynamic model enables us to estimate both of the layers simultaneously from natural data.

Models, Neurological↗

PubMed2002

A multi-layer sparse coding network learns contour coding from natural images.

An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous studies have shown how many basic properties of the primary visual cortex, such as the receptive fields of simple and complex cells and the spatial organization (topography) of the cells, can be understood as efficient coding of natural images. Here we extend the framework by considering how the responses of complex cells could be sparsely represented by a higher-order neural layer. This leads to contour coding and end-stopped receptive fields. In addition, contour integration could be interpreted as top-down inference in the presented model.

Cerebral Cortex↗

PubMed1999

Nonlinear independent component analysis: Existence and uniqueness results.

The question of existence and uniqueness of solutions for nonlinear independent component analysis is addressed. It is shown that if the space of mixing functions is not limited there exists always an infinity of solutions. In particular, it is shown how to construct parameterized families of solutions. The indeterminacies involved are not trivial, as in the linear case. Next, it is shown how to utilize some results of complex analysis to obtain uniqueness of solutions. We show that for two dimensions, the solution is unique up to a rotation, if the mixing function is constrained to be a conformal mapping together with some other assumptions. We also conjecture that the solution is strictly unique except in some degenerate cases, as the indeterminacy implied by the rotation is essentially similar to estimating the model of linear ICA.

Journal Article↗