Publications

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In this paper, we highlight how computational properties of biological dendrites can be leveraged for neuromorphic applications. Specifically, we demonstrate analog silicon dendrites that support multiplication mediated by conductance-based input in an interception model inspired by the biological dragonfly. We also demonstrate spatiotemporal pattern recognition and direction selectivity using dendrites on the Loihi neuromorphic platform. These dendritic circuits can be assembled hierarchically as building blocks for classifying complex spatiotemporal patterns.

Recent work in neuromorphic computing has proposed a range of new architectures for Spiking Neural Network (SNN)-based systems. However, neuromorphic design lacks a framework to facilitate exploration of different SNN-based architectures and aid with early design decisions. While there are various SNN simulators, none can be used to rapidly estimate latency and energy of different spiking architectures. We show that while current spiking designs differ in implementation, they have common features which can be represented as a generic architecture template. We describe an initial version of a framework that simulates a range of neuromorphic architectures at an abstract time-step granularity. We demonstrate our simulator by modeling Intel's Loihi platform, estimating time-varying energy and latency with less than 10% mean error for various sizes of a two-layer SNN.

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Coordinate transformations are a fundamental operation that must be performed by any animal relying upon sensory information to interact with the external world. We present a neural network model that performs a coordinate transformation from the dragonfly eye's frame of reference to the body's frame of reference while hunting. We demonstrate that the model successfully calculates turns required for interception, and discuss how future work will compare our model with biological dragonfly neural circuitry and guide neural-inspired neuromorphic implementations of coordinate transformations.

Shunting inhibition is a potential mechanism by which biological systems multiply two time-varying signals, most recently proposed in single neurons of the fly visual system. Our work demonstrates this effect in a biological neuron model and the equivalent circuit in neuromorphic hardware modeling dendrites. We present a multi-compartment neuromorphic dendritic model that produces a multiplication-like effect using the shunting inhibition mechanism by varying leakage along the dendritic cable. Dendritic computation in neuromorphic architectures has the potential to increase complexity in single neurons and reduce the energy footprint for neural networks by enabling computation in the interconnect.

Coordinate transformations are a fundamental operation that must be performed by any animal relying upon sensory information to interact with the external world. We present a neural network model that performs a coordinate transformation from the dragonfly eye's frame of reference to the body's frame of reference while hunting. We demonstrate that the model successfully calculates turns required for interception, and discuss how future work will compare our model with biological dragonfly neural circuitry and guide neural-inspired neuromorphic implementations of coordinate transformations.

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Neuromorphic computing (NMC) is an exciting paradigm seeking to incorporate principles from biological brains to enable advanced computing capabilities. Not only does this encompass algorithms, such as neural networks, but also the consideration of how to structure the enabling computational architectures for executing such workloads. Assessing the merits of NMC is more nuanced than simply comparing singular, historical performance metrics from traditional approaches versus that of NMC. The novel computational architectures require new algorithms to make use of their differing computational approaches. And neural algorithms themselves are emerging across increasing application domains. Accordingly, we propose following the example high performance computing has employed using context capturing mini-apps and abstraction tools to explore the merits of computational architectures. Here we present Neural Mini-Apps in a neural circuit tool called Fugu as a means of NMC insight.

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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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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.