Biomedical subjects

Jarmo Hurri

Publications and source records attributed to Jarmo Hurri.

3 recordsLinked to original sources

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↗