James B. Aimone | DeepAI

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Featured Co-authors

Aleksandra Faust
44 publications
Michael R. Smith
13 publications
Ojas Parekh
12 publications
William Severa
9 publications
Conrad D. James
8 publications
Craig M. Vineyard
6 publications
Lydia Tapia
6 publications
Cynthia A. Phillips
5 publications
Jack Lindsey
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Ryan Dellana
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Yipu Wang
4 publications

∙ 06/29/2023

Decomposing spiking neural networks with Graphical Neural Activity Threads

A satisfactory understanding of information processing in spiking neural...

0 Bradley H. Theilman, et al. ∙

∙ 10/05/2022

Stochastic Neuromorphic Circuits for Solving MAXCUT

Finding the maximum cut of a graph (MAXCUT) is a classic optimization pr...

0 Bradley H. Theilman, et al. ∙

∙ 05/02/2022

Sequence Learning and Consolidation on Loihi using On-chip Plasticity

In this work we develop a model of predictive learning on neuromorphic h...

0 Jack Lindsey, et al. ∙

∙ 03/23/2022

Spiking Neural Streaming Binary Arithmetic

Boolean functions and binary arithmetic operations are central to standa...

0 James B. Aimone, et al. ∙

∙ 07/27/2021

Neuromorphic scaling advantages for energy-efficient random walk computation

Computing stands to be radically improved by neuromorphic computing (NMC...

26 J. Darby Smith, et al. ∙

∙ 06/25/2020

Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication

Boolean circuits of McCulloch-Pitts threshold gates are a classic model ...

0 Ojas Parekh, et al. ∙

∙ 05/21/2020

Solving a steady-state PDE using spiking networks and neuromorphic hardware

The widely parallel, spiking neural networks of neuromorphic processors ...

0 J. Darby Smith, et al. ∙

∙ 05/28/2019

Composing Neural Algorithms with Fugu

Neuromorphic hardware architectures represent a growing family of potent...

0 James B. Aimone, et al. ∙

∙ 10/26/2018

Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication

This paper presents a new technique for training networks for low-precis...

0 William Severa, et al. ∙

∙ 09/25/2018

Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting

Robots and autonomous agents often complete goal-based tasks with limite...

0 Aleksandra Faust, et al. ∙

∙ 05/01/2018

Spiking Neural Algorithms for Markov Process Random Walk

The random walk is a fundamental stochastic process that underlies many ...

0 William Severa, et al. ∙

∙ 12/20/2017

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host...

0 Justin E. Doak, et al. ∙

∙ 11/27/2017

Context-modulation of hippocampal dynamics and deep convolutional networks

Complex architectures of biological neural circuits, such as parallel pr...

0 James B. Aimone, et al. ∙

∙ 11/10/2017

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive i...

0 Michael R. Smith, et al. ∙

∙ 10/26/2017

Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation

The high dimensionality of hyperspectral imaging forces unique challenge...

0 William M. Severa, et al. ∙

∙ 05/04/2017

Exponential scaling of neural algorithms - a future beyond Moore's Law?

Although the brain has long been considered a potential inspiration for ...

0 James B. Aimone, et al. ∙

∙ 03/21/2017

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...

0 Michael R. Smith, et al. ∙

∙ 12/12/2016

Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), hav...

0 Timothy J. Draelos, et al. ∙

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