
Neuromorphic scaling advantages for energyefficient random walk computation
Computing stands to be radically improved by neuromorphic computing (NMC...
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ConstantDepth and SubcubicSize Threshold Circuits for Matrix Multiplication
Boolean circuits of McCullochPitts threshold gates are a classic model ...
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Solving a steadystate PDE using spiking networks and neuromorphic hardware
The widely parallel, spiking neural networks of neuromorphic processors ...
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Composing Neural Algorithms with Fugu
Neuromorphic hardware architectures represent a growing family of potent...
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Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication
This paper presents a new technique for training networks for lowprecis...
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Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting
Robots and autonomous agents often complete goalbased tasks with limite...
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Spiking Neural Algorithms for Markov Process Random Walk
The random walk is a fundamental stochastic process that underlies many ...
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Tracking Cyber Adversaries with Adaptive Indicators of Compromise
A forensics investigation after a breach often uncovers network and host...
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Contextmodulation of hippocampal dynamics and deep convolutional networks
Complex architectures of biological neural circuits, such as parallel pr...
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Dynamic Analysis of Executables to Detect and Characterize Malware
It is needed to ensure the integrity of systems that process sensitive i...
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Datadriven Feature Sampling for Deep Hyperspectral Classification and Segmentation
The high dimensionality of hyperspectral imaging forces unique challenge...
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Exponential scaling of neural algorithms  a future beyond Moore's Law?
Although the brain has long been considered a potential inspiration for ...
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A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Information in neural networks is represented as weighted connections, o...
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Neurogenesis Deep Learning
Neural machine learning methods, such as deep neural networks (DNN), hav...
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James B. Aimone
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