Publications

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Borrowing from nature, neural-inspired interception algorithms were implemented onboard a vehicle. To maximize success, work was conducted in parallel within a simulated environment and on physical hardware. The intercept vehicle used only optical imaging to detect and track the target. A successful outcome is the proof-of-concept demonstration of a neural-inspired algorithm autonomously guiding a vehicle to intercept a moving target. This work tried to establish the key parameters for the intercept algorithm (sensors and vehicle) and expand the knowledge and capabilities of implementing neural-inspired algorithms in simulation and on hardware.

In each of our brains, 86 billion neurons work in parallel, processing inputs from senses and memories to produce the many feats of human cognition. The brains of other creatures are less broadly capable, but those animals often exhibit innate aptitudes for particular tasks, abilities honed by millions of years of evolution.

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While dragonflies are well-known for their high success rates when hunting prey, how the underlying neural circuitry generates the prey-interception trajectories used by dragonflies to hunt remains an open question. I present a model of dragonfly prey interception that uses a neural network to calculate motor commands for prey-interception. The model uses the motor outputs of the neural network to internally generate a forward model of prey-image translation resulting from the dragonfly's own turning that can then serve as a feedback guidance signal, resulting in trajectories with final approaches very similar to proportional navigation. The neural network is biologically-plausible and can therefore can be compared against in vivo neural responses in the biological dragonfly, yet parsimonious enough that the algorithm can be implemented without requiring specialized hardware.

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Historically, neuroscience principles have heavily influenced artificial intelligence (AI), for example the influence of the perceptron model, essentially a simple model of a biological neuron, on artificial neural networks. More recently, notable recent AI advances, for example the growing popularity of reinforcement learning, often appear more aligned with cognitive neuroscience or psychology, focusing on function at a relatively abstract level. At the same time, neuroscience stands poised to enter a new era of large-scale high-resolution data and appears more focused on underlying neural mechanisms or architectures that can, at times, seem rather removed from functional descriptions. While this might seem to foretell a new generation of AI approaches arising from a deeper exploration of neuroscience specifically for AI, the most direct path for achieving this is unclear. Here we discuss cultural differences between the two fields, including divergent priorities that should be considered when leveraging modern-day neuroscience for AI. For example, the two fields feed two very different applications that at times require potentially conflicting perspectives. We highlight small but significant cultural shifts that we feel would greatly facilitate increased synergy between the two fields.

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Dragonflies are known to be highly successful hunters (achieving 90 - 95% success rate in nature) that implement a guidance law like proportional navigation to intercept their prey. This project tested the hypothesis that dragonflies are able to implement p roportional navigation using prey - image translation on their eyes. The model dragonfly presented here calculates changes in pitch and yaw to maintain the prey's image at a designated location (the fovea) on a two - dimensional screen (the model's eyes ). Wh en the model also uses self - knowledge of its own maneuvers as an error signal to adjust the location of the fovea, its interception trajectory becomes equivalent to proportional navigation. I also show that this model can also be applied successfully (in a liminted nu mber of scenarios) against maneuvering prey. My results provide a proof - of - concept demonstration of the potential of using the dragonfly nervous system to design a robust interception algorithm for implementation on a man - made system. ACKNOWLEDGEME NTS First I would like to thank the Autonomy for Hypersonics Mission Campaign for their support of this LDRD. I am also grateful to Larry Jones, Julie Parish, Jeff Spooner, Brad Aimone, Fred Rothganger and Srideep Musuvathy for helpful discussions during development of this model.

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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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