Jarmo Hurri

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.

Applications of neural blind separation to signal and image processing

In blind source separation one tries to separate statistically independent unknown source signals from their linear mixtures without knowing the mixing coefficients. Such techniques are currently studied actively both in statistical signal processing and unsupervised neural learning. We apply neural blind separation techniques developed in our laboratory to the extraction of features from natural images and to the separation of medical EEG signals. The new analysis method yields features that describe the underlying data better than for example classical principal component analysis. We discuss difficulties related with real-world applications of blind signal processing, too.

Image feature extraction by sparse coding and independent component analysis

Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely significantly active. Such a representation is closely related to the techniques of independent component analysis and blind source separation. In this paper, we investigate the application of sparse coding for image feature extraction. We show how sparse coding can be used to extract wavelet-like features from natural image data. As an application of such a feature extraction scheme, we show how to apply a soft-thresholding operator on the components of sparse coding in order to reduce Gaussian noise. Methods based on sparse coding have the important benefit over wavelet methods that the features are determined solely by the statistical properties of the data, while the wavelet transformation relies heavily on certain abstract mathematical properties that may be only weakly related to the properties of the natural data.

Blind separation of sources that have spatiotemporal variance dependencies

In blind source separation methods, the sources are typically assumed to be independent. Some methods are also able to separate dependent sources by estimating or assuming a parametric model for their dependencies. Here, we propose a method that separates dependent sources without a parametric model of their dependency structure. This is possible by introducing some general assumptions on the structure of the dependencies: the sources are dependent only through their variances (general activity levels), and the variances of the sources have temporal correlations. The method can be called double-blind because of this additional blind aspect: We do not need to estimate (or assume) a parametric model of the dependencies, which is in stark contrast to most previous methods.

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.

Comparison of Adaptive Strategies for On-Line Character Recognition

Results on a comparison of adaptive recognition techniques for on-line recognition of handwritten Latin alphabets are presented. The classification strategies compared are based on first compressing or distilling a large database of handwritten characters to a small set of character prototypes. Each adaptive classifier then either modifies the original prototypes or conditionally adds new prototypes when they become available from the user of the system. In each case, the classification decision uses the 1-Nearest Neighbor (1-NN) rule for the distances between the input character and the stored prototypes. The distances are calculated using Dynamic Time Warping (DTW). One of the adaptive learning strategies features an extension of the neural Learning Vector Quantization (LVQ) algorithm to the DTW distance metric. All the methods concerned exhibit automatic unsupervised learning from user input simultaneously with the normal mode of operation. The presented experiments show that the assessed methods produce different tradeoffs between the accuracy and complexity of classification. Every version is, however, able to adapt to the user’s writing style with only a very few — say some tens of — handwritten characters.

A two-layer temporal generative model of natural video exhibits complex-cell-like pooling of simple cell outputs

Abstract 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 emergence of basis vectors that are localized, oriented and have different scales, just like previous work. But our new model enables the first layer to learn connections between the simple cells that are similar to complex cell pooling: connections are strong among cells with similar 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.

SPATIOTEMPORAL LINEAR SIMPLE-CELL MODELS BASED ON TEMPORAL COHERENCE AND INDEPENDENT COMPONENT ANALYSIS

The search for computational principles that underlie the functionality of different cortical areas is a fundamental scientific challenge. In the case of sensory areas, one approach is to examine how the statistical properties of natural stimuli in the case of vision, natural images and image sequences are related to the response properties of neurons. For simple cells, located in V1, the most prominent computational theories linking neural properties and stimulus statistics are temporal coherence and independent component analysis. For these theories, the case of spatial linear cell models has been studied in a number of recent publications, but the case of spatioternporal models has received fairly little attention. Here we examine the spatiotemporal case by applying the theories to natural image sequence data, and by analyzing the obtained results quantitatively. We compare the properties of the spatiotemporal linear cell models learned with the methods against each other, and against parameters measured from real visual systems.

Receptive Fields Similar to Simple Cells Maximize Temporal Coherence in Natural Video

Recently, statistical models of natural images have shown emergence of several properties of the visual cortex. Most models have considered the non-Gaussian properties of static image patches, leading to sparse coding or independent component analysis. Here we consider the basic statistical time dependencies of image sequences. 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 that have previously been used to explain complex cell receptive fields.