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06/29/2023
Decomposing spiking neural networks with Graphical Neural Activity Threads
A satisfactory understanding of information processing in spiking neural...
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10/05/2022
Stochastic Neuromorphic Circuits for Solving MAXCUT
Finding the maximum cut of a graph (MAXCUT) is a classic optimization pr...
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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...
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03/23/2022
Spiking Neural Streaming Binary Arithmetic
Boolean functions and binary arithmetic operations are central to standa...
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07/27/2021
Neuromorphic scaling advantages for energy-efficient random walk computation
Computing stands to be radically improved by neuromorphic computing (NMC...
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06/25/2020
Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication
Boolean circuits of McCulloch-Pitts threshold gates are a classic model ...
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05/21/2020
Solving a steady-state PDE using spiking networks and neuromorphic hardware
The widely parallel, spiking neural networks of neuromorphic processors ...
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05/28/2019
Composing Neural Algorithms with Fugu
Neuromorphic hardware architectures represent a growing family of potent...
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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...
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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...
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05/01/2018
Spiking Neural Algorithms for Markov Process Random Walk
The random walk is a fundamental stochastic process that underlies many ...
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12/20/2017
Tracking Cyber Adversaries with Adaptive Indicators of Compromise
A forensics investigation after a breach often uncovers network and host...
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11/27/2017
Context-modulation of hippocampal dynamics and deep convolutional networks
Complex architectures of biological neural circuits, such as parallel pr...
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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...
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10/26/2017
Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation
The high dimensionality of hyperspectral imaging forces unique challenge...
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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 ...
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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...
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12/12/2016