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Results for “neural-inspired algorithms”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Neuromorphic Processing and Sensing for Interception

Interception of a moving and potentially evading target can be a challenging problem, in particular for conditions in which the target may be moving at high speeds and difficult to detect. We have proposed to merge two Sandia LDRD efforts, the SPARR Spiking/Processing Array (neuromorphic event-driven sensing) and the Dragonfly-Inspired Algorithms for Intercept- Trajectory Planning (neural-inspired algorithms for interception) toward a unified system with direct application to national security. Neuromorphic systems demonstrate the most potential for speed and efficiency gains when communication is event-driven and computations are simple but parallelizable. Accordingly, we anticipate fully realizing potential benefits from a neuromorphic interception system if event-driven sensing is combined with processing and acting also implemented on event-driven (spiking) systems. We have successfully translated a neural-inspired interception algorithm to a neural network architecture for evaluation on neuromorphic hardware. Preliminary implementations of the neural network designed for implementation on the Loihi chip are still too immature for conclusive evaluation, but the results of this effort have demonstrated a viable path for a previously developed dragonfly-inspired interception algorithm to be implemented on neuromorphic hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Biologically Inspired Interception on an Unmanned System

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.

42 ENGINEERING↗
DOE OSTI2020

Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence

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.

60 APPLIED LIFE SCIENCES↗