Tagged: Brain modeling

A 3-D Immersive Environment for Characterizing EEG Signatures of Target Detection

Visual target detection is one of the most studied paradigms in human electrophysiology. Electroencephalo-graphic (EEG) correlates of target detection include the well-characterized N1, P2, and P300. In almost all cases the experimental paradigms used for studying visual target detection are extremely well-controlled – very simple stimuli are presented so as to minimize eye movements, and scenarios involve minimal active participation by the subject. However, to characterize these EEG correlates for real-world scenarios, where the target or the subject may be moving and the two may interact, a more flexible paradigm is required. The environment must be immersive and interactive, and the system must enable synchronization between events in the world, the behavior of the subject, and simultaneously recorded EEG signals. We have developed a hardware/software system that enables us to precisely control the appearance of objects in a 3D virtual environment, which subjects can navigate while the system tracks their eyes and records their EEG activity. We are using this environment to investigate a set of questions which focus on the relationship between the visibility, salience, and affect of the target; the agency and eye movements of the subject; and the resulting EEG signatures of detection. In this paper, we describe the design of our system and present some preliminary results regarding the EEG signatures of target detection.

Perceptual salience as novelty detection in cortical pinwheel space

We describe a filter-based model of orientation processing in primary visual cortex (V1) and demonstrate that novelty in cortical “pinwheel” space can be used as a measure of perceptual salience. In the model, novelty is computed as the negative log likelihood of a pinwheel’s activity relative to the population response. The population response is modeled using a mixture of Gaussians, enabling the representation of complex, multi-modal distributions. Hidden variables that are inferred in the mixture model can be viewed as grouping or “binding” pinwheels which have similar responses within the distribution space. Results are shown for several stimuli that illustrate well-known contextual effects related to perceptual salience, as well as results for a natural image.

Simulated optical imaging of orientation preference in a model of V1

Optical imaging studies have played an important role in mapping the orientation selectivity and ocular dominance of neurons across an extended area of primary visual cortex (V1). Such studies have produced images with a more or less smooth and regular spatial distribution of relevant neuronal response properties. This is in spite of the fact that results from electrophysiological recordings, though limited in their number and spatial distribution, show significant scatter/variability in the relevant response properties of nearby neurons. In this paper we present a simulation of the optical imaging experiments of ocular dominance and orientation selectivity using a computational model of the primary visual cortex. The simulations assume that the optical imaging signal is proportional to the averaged response of neighboring neurons. The model faithfully reproduces ocular dominance columns and orientation pinwheels in the presence of realistic scatter of single cell preferred responses. In addition,we find the simulated optical imaging of orientation pinwheels to be remarkably robust, with the pinwheel structure maintained up to an addition of degrees of random scatter in the orientation preference of single cells. Our results suggest that an optical imaging result does not necessarily, by itself, provide any obvious upperbound for the scatter of the underlying neuronal response properties on local scales.

Spatial signatures of visual object recognition events learned from single-trial analysis of EEG

In this paper we use linear discrimination for learning EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (63 electrodes) during the rapid presentation (50-200 msec per image) of natural images. Each trial consists of 100 images, with a 50% chance of a single target being in a trial. Subjects are instructed to press a left mouse button at the end of the trial if they detected a target image, otherwise they are instructed to press the right button. Subject EEG was analyzed on a single-trial basis with an optimal spatial linear discriminator learned at multiple time windows after the presentation of an image. Analysis of discrimination results indicated a periodic fluctuation (time-localized oscillation) in A/sub z/ performance. Analysis of the EEG using the discrimination components learned at the peaks of the A/sub z/ fluctuations indicate 1) the presence of a positive evoked response, followed in time by a negative evoked response in strongly overlapping areas and 2) a component which is not correlated with the discriminator learned during the time-localized fluctuation. Results suggest that multiple signatures, varying over time, may exist for discriminating between target and distractor trials.

Texture discrimination and binding by a modified energy model

The model presented shows how textured regions can be discriminated and textured surface created by the visual cortex. The model addresses two major processes: texture segmentation and texture binding. Textures are detected by using a version of the energy model of J. R. Bergen and E. H. Adelson (1988) and J. R. Bergen and M. S. Landy (1991), which was modified to include ON and OFF center cells, and units selective for line endings. A novel neural mechanism is described for binding a texture pattern together. Simulation results demonstrated the ability of the networks to segment and bind a well-known texture pattern.

A neural network model of object segmentation and feature binding in visual cortex

The authors present neural network simulations of how the visual cortex may segment objects and bind attributes based on depth-from-occlusion. They briefly discuss one particular subprocess in the occlusion-based model most relevant to segmentation and binding: determination of the direction of figure. They propose that the model allows addressing a central issue in object recognition: how the visual system defines an object. In addition, the model was tested on illusory stimuli, with the network’s response indicating the existence of robust psychophysical properties in the system.