Biomedical subjects

Odelia Schwartz

Publications and source records attributed to Odelia Schwartz.

4 recordsLinked to original sources

PubMed2006

Spike-triggered neural characterization.

Response properties of sensory neurons are commonly described using receptive fields. This description may be formalized in a model that operates with a small set of linear filters whose outputs are nonlinearly combined to determine the instantaneous firing rate. Spike-triggered average and covariance analyses can be used to estimate the filters and nonlinear combination rule from extracellular experimental data. We describe this methodology, demonstrating it with simulated model neuron examples that emphasize practical issues that arise in experimental situations.

Action Potentials↗

PubMed2006

Soft mixer assignment in a hierarchical generative model of natural scene statistics.

Gaussian scale mixture models offer a top-down description of signal generation that captures key bottom-up statistical characteristics of filter responses to images. However, the pattern of dependence among the filters for this class of models is prespecified. We propose a novel extension to the gaussian scale mixture model that learns the pattern of dependence from observed inputs and thereby induces a hierarchical representation of these inputs. Specifically, we propose that inputs are generated by gaussian variables (modeling local filter structure), multiplied by a mixer variable that is assigned probabilistically to each input from a set of possible mixers. We demonstrate inference of both components of the generative model, for synthesized data and for different classes of natural images, such as a generic ensemble and faces. For natural images, the mixer variable assignments show invariances resembling those of complex cells in visual cortex; the statistics of the gaussian components of the model are in accord with the outputs of divisive normalization models. We also show how our model helps interrelate a wide range of models of image statistics and cortical processing.

Animals↗

PubMed2005

Spatiotemporal elements of macaque v1 receptive fields.

Neurons in primary visual cortex (V1) are commonly classified as simple or complex based upon their sensitivity to the sign of stimulus contrast. The responses of both cell types can be described by a general model in which the outputs of a set of linear filters are nonlinearly combined. We estimated the model for a population of V1 neurons by analyzing the mean and covariance of the spatiotemporal distribution of random bar stimuli that were associated with spikes. This analysis reveals an unsuspected richness of neuronal computation within V1. Specifically, simple and complex cell responses are best described using more linear filters than the one or two found in standard models. Many filters revealed by the model contribute suppressive signals that appear to have a predominantly divisive influence on neuronal firing. Suppressive signals are especially potent in direction-selective cells, where they reduce responses to stimuli moving in the nonpreferred direction.

Action Potentials↗

PubMed1999

Modeling the precedence effect for speech using the gamma filter.

The ability of the human auditory system to localize the direction of a sound source in a reverberant environment extends from simple clicks to complex speech signals. The perceived location of the auditory event is dominated by the direct sound, for small enough time intervals between the direct sound and reflection-a phenomenon known as the precedence effect. In this paper we present a computer simulation of the precedence effect for speech, implemented using Matlab and tested with speech signals from the TIMIT database. The model is based on the biological assumption that the rate of change of a signal (onset) is critical in triggering the precedence effect. The model demonstrates the precedence effect on speech signals, as opposed to prior models that have only been tested on clicks. In addition, a novel onset enhancement method is described and implemented using the gamma filter, a new class of linear systems for adaptive signal processing. This method can either use fixed coefficients and obtain similar results to prior methods of onset enhancement, or use an adaptive framework. The adaptive framework is promising in its ability to enhance onsets and to reduce the steady state portions of the signal.

Journal Article↗