Biomedical subjects

Nicole C Rust

Publications and source records attributed to Nicole C Rust.

7 recordsLinked to original sources

PubMed2006

How MT cells analyze the motion of visual patterns.

Neurons in area MT (V5) are selective for the direction of visual motion. In addition, many are selective for the motion of complex patterns independent of the orientation of their components, a behavior not seen in earlier visual areas. We show that the responses of MT cells can be captured by a linear-nonlinear model that operates not on the visual stimulus, but on the afferent responses of a population of nonlinear V1 cells. We fit this cascade model to responses of individual MT neurons and show that it robustly predicts the separately measured responses to gratings and plaids. The model captures the full range of pattern motion selectivity found in MT. Cells that signal pattern motion are distinguished by having convergent excitatory input from V1 cells with a wide range of preferred directions, strong motion opponent suppression and a tuned normalization that may reflect suppressive input from the surround of V1 cells.

Algorithms↗

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↗

PubMed2005

Do we know what the early visual system does?

We can claim that we know what the visual system does once we can predict neural responses to arbitrary stimuli, including those seen in nature. In the early visual system, models based on one or more linear receptive fields hold promise to achieve this goal as long as the models include nonlinear mechanisms that control responsiveness, based on stimulus context and history, and take into account the nonlinearity of spike generation. These linear and nonlinear mechanisms might be the only essential determinants of the response, or alternatively, there may be additional fundamental determinants yet to be identified. Research is progressing with the goals of defining a single "standard model" for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes. These predictive models represent, at a given stage of the visual pathway, a compact description of visual computation. They would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.

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↗

PubMed2005

In praise of artifice.

The visual system evolved to process natural images, and the goal of visual neuroscience is to understand the computations it uses to do this. Indeed the goal of any theory of visual function is a model that will predict responses to any stimulus, including natural scenes. It has, however, recently become common to take this fundamental principle one step further: trying to use photographic or cinematographic representations of natural scenes (natural stimuli) as primary probes to explore visual computations. This approach is both challenging and controversial, and we argue that this use of natural images is so fraught with difficulty that it is not useful. Traditional methods for exploring visual computations that use artificial stimuli with carefully selected properties have been and continue to be the most effective tools for visual neuroscience. The proper use of natural stimuli is to test models based on responses to these synthetic stimuli, not to replace them.

Action Potentials↗

PubMed2004

Analyzing neural responses to natural signals: maximally informative dimensions.

We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli that are nongaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of a high-dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Existing analysis methods are based on correlation functions between stimuli and responses, but these methods are guaranteed to work only in the case of gaussian stimulus ensembles. As an alternative to correlation functions, we maximize the mutual information between the neural responses and projections of the stimulus onto low-dimensional subspaces. The procedure can be done iteratively by increasing the dimensionality of this subspace. Those dimensions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is small, it becomes feasible to map the neuron's input-output function even under fully natural stimulus conditions. These ideas are illustrated in simulations on model visual and auditory neurons responding to natural scenes and sounds, respectively.

Acoustic Stimulation↗

PubMed2002

A reciprocal relationship between reliability and responsiveness in developing visual cortical neurons.

As the visual cortex matures, developmental modifications change the visually evoked firing patterns of single neurons. To explore the relationship between these developmental changes and the fidelity with which neurons transmit information, we measured the reliability of neuronal responses during postnatal development. Infant neurons have lower variability and higher dependence of transmitted information on firing rate than adult cells. Fewer spikes are needed by the infant cortex to convey the same amount of information. The increase in firing rates that occurs during development is largely offset, therefore, by a decrease in the reliability of responses. We propose that these changes are a consequence of the increasing ability of cortical cells to encode rapid changes in the visual environment.

Animals↗