Publications

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The retina plays an important role in animal vision --- namely to pre-process visual information before sending it to the brain. The goal of this LDRD was to develop models of motion-sensitive retinal cells for the purpose of developing retinal-inspired algorithms to be applied to real-world data specific to Sandia's national security missions. We specifically focus on detection of small, dim moving targets amidst varying types of clutter or distractor signals. We compare a classic motion-sensitive model, the Hassenstein-Reichardt model, to a model of the OMS (object motion- sensitive) cell, and find that the Reichardt model performs better under continuous clutter (e.g. white noise) but is very sensitive to particular stimulus conditions (e.g. target velocity). We also demonstrate that lateral inhibition, a ubiquitous characteristic of neural circuitry, can effect target-size tuning, improving detection specifically of small targets.

The retina plays an important role in animal vision - namely preprocessing visual information before sending it to the brain through the optic nerve. Understanding howthe retina does this is of particular relevance for development and design of neuromorphic sensors, especially those focused towards image processing. Our research focuses on examining mechanisms of motion processing in the retina. We are specifically interested in detection of moving targets under challenging conditions, specifically small or low-contrast (dim) targets amidst high quantities of clutter or distractor signals. In this paper we compare a classic motion-sensitive cell model, the Hassenstein-Reichardt model, to a model of the OMS (object motion-sensitive) cell, that relies primarily on change-detection, and describe scenarios for which each model is better suited. We also examine mechanisms, inspired by features of retinal circuitry, by which performance may be enhanced. For example, lateral inhibition (mediated by amacrine cells) conveys selectivity for small targets to the W3 ganglion cell - we demonstrate that a similar mechanism can be combined with the previously mentioned motion-processing cell models to select small moving targets for further processing.

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Modern computers are constantly faced with the challenge of processing ever-growing quantities of data that span a wide range of modalities. Of particular relevance to national security interests is the ability to integrate multimodal data for the purpose of fast decision making. The brain is a biological system that is specialized for high performance at this task, suggesting that understanding the mechanisms by which neural circuits integrate multimodal data may lead to improved man-made detection systems. This research focused on understanding these neural algorithms, and specifically tested the hypothesis that hippocampal neurons multiplex information from two different input streams. Specifically, we compare the spiking behavior of a computational model of hippocampal circuitry with neurophysiological data recorded from rodent hippocampus.

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