Publications

Advanced Search

Results 1–25 of 28

Scope

Center for Computing Research

All Sandia Publications

Publication Type

Author

Funding Source

Current Filters Clear all

Remove author filter×

Publication	Type	Year
How Neural Plasticity Boosts Performance of Spiking Neural Networks Annual Conference on Cognitive Computational Neuroscience Nicholas Soures, Abdullah M. A. Zyarah, Kristofor David Carlson, James Bradley Aimone, Dhireesha Kudithipudi https://www.osti.gov/search/identifier:1458069	Conference Paper – 2017 Conference Paper	2017
Neurogenesis Deep Learning: Extending deep Networks to accommodate new classes IEEE International Joint Conference on Neural Networks Timothy J. Draelos, Nadine E. Miner, Christopher Lamb, Jonathan cox, Craig Michael Vineyard, Kristofor David Carlson, William Mark Severa, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1457976	Conference Paper – 2017 Conference Paper	2017
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to LSMs The 2017 International Joint Conference on Neural Networks (IJCNN 2017) Michael Reed Smith, Aaron Jamison Hill, Kristofor David Carlson, Craig Michael Vineyard, Jonathon W. Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1457873	Conference Paper – 2017 Conference Paper	2017
An Efficient Implementation of a Liquid State Machine on the Spiking Temporal Processing Unit Neuro Inspired Computational Elements Workshop Michael Reed Smith, Aaron Jamison Hill, Kristofor David Carlson, Craig Michael Vineyard, Jonathon W. Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1427932	Conference Paper – 2017 Conference Paper	2017
An Efficient Implementation of a LSM on the Spiking Temporal Processing Unit Neuro Inspired Computational Elements Workshop Michael Reed Smith, Aaron Jamison Hill, Kristofor David Carlson, Craig Michael Vineyard, Jonathon W. Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James Bradley Aimone, Michael Reed Smith, Michael Reed Smith https://www.osti.gov/search/identifier:1427931	Conference Paper – 2017 Conference Paper	2017
Neurogenesis Deep Learning Ijcnn 2-17 Timothy J. Draelos, Nadine E. Miner, Christopher Lamb, Jonathan A. Cox, Craig Michael Vineyard, Kristofor David Carlson, William Mark Severa, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1424868	Conference Paper – 2017 Conference Paper	2017
Learning, Homeostasis, and Neurogenesis in Neural Systems Computational Neuroscience Course Presentation at Rochester Institute of Technology Kristofor David Carlson https://www.osti.gov/search/identifier:1425303	Presentation (non-conference) – 2017 Presentation (non-conference)	2017
A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications Biologically Inspired Cognitive Architectures Conrad D. James, James Bradley Aimone, Nadine E. Miner, Craig Michael Vineyard, Fredrick Rothganger, Kristofor David Carlson, Samuel A. Mulder, Timothy J. Draelos, Aleksandra Faust, Matthew Marinella, John H. Naegle, Steven J. Plimpton https://www.osti.gov/search/identifier:1340263	Journal Article – 2016 Journal Article	2016
Neurogenesis Deep Learning Timothy J. Draelos, Nadine E. Miner, Christopher Lamb, Craig Michael Vineyard, Kristofor David Carlson, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1505351	Report – 2016 Report	2016
Can we be formal in assessing the strengths and weaknesses of neural architectures? A case study using a spiking cross-correlation algorithm Neural Information Processing Systems William Mark Severa, Kristofor David Carlson, Ojas D. Parekh, Craig Michael Vineyard, James Bradley Aimone https://www.osti.gov/search/identifier:1413431	Conference Paper – 2016 Conference Paper	2016
Can we be formal in assessing the strengths and weaknesses of neural architectures? A case study using a spiking cross-correlation algorithm Neural Information Processing Systems (NIPS) 2016 - Computing with Spikes Workshop William Mark Severa, Kristofor David Carlson, Ojas D. Parekh, Craig Michael Vineyard, James Bradley Aimone	Abstract – 2016 Abstract	2016
Can we be formal in assessing the strengths and weaknesses of neural architectures? A case study using a spiking cross-correlation algorithm Neural Information Processing Systems (NIPS) 2016 William Mark Severa, Kristofor David Carlson, Ojas D. Parekh, Craig Michael Vineyard, James Bradley Aimone https://www.osti.gov/search/identifier:1410236	Conference Paper – 2016 Conference Paper	2016
Applying Uncertainty Quantification and Sensitivity Analysis to Spiking Neural Network Models of Asynchronous Irregular Firing Activity Society for Neurosciene Annual Meeting 2016 Kristofor David Carlson, James Bradley Aimone https://www.osti.gov/search/identifier:1408952	Conference Paper – 2016 Conference Paper	2016
Implementation of a Liquid State Machine with Temporal Dynamics on a Novel Spiking Neuromorphic Architecture Workshop on Neuromorphic Computing and Algorithms, WI-BIH'16 Michael Reed Smith, Aaron Jamison Hill, Kristofor David Carlson, Craig Michael Vineyard, Jonathon W. Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1405258	Conference Paper – 2016 Conference Paper	2016
Spiking Network Algorithms for Scientific Computing IEEE International Conference on Rebooting Computing (ICRC 2016) William Mark Severa, Ojas D. Parekh, Kristofor David Carlson, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1401930	Conference Paper – 2016 Conference Paper	2016
Spiking Network Algorithms for Scientific Computing IEEE International Conference on Rebooting Computing (ICRC 2016) William Mark Severa, Ojas D. Parekh, Kristofor David Carlson, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1373236	Conference Paper – 2016 Conference Paper	2016
Neural Computing: What Scale and Complexity is Needed? Neuromorphic Computing Workshop James Bradley Aimone, Kristofor David Carlson, Fredrick Rothganger https://www.osti.gov/search/identifier:1367782	Conference Paper – 2016 Conference Paper	2016
Sandia Neural Computing Overview 2016 AFOSR Digital Electronics Working Group Kristofor David Carlson https://www.osti.gov/search/identifier:1428030	Presentation (non-conference) – 2016 Presentation (non-conference)	2016
Applying uncertainty quantification and sensitivity analysis to spiking neural network models of asynchronous irregular firing activity Society for Neuroscience Annual Conference 2016 Kristofor David Carlson, James Bradley Aimone	Abstract – 2016 Abstract	2016
Computational Perspectives on Adult Neurogenesis Rewiring the BrainA Computational Approach to Structural Plasticity in the Adult Brain Kristofor David Carlson, James Bradley Aimone, Fredrick Rothganger https://www.osti.gov/search/identifier:1574646	Book – 2016 Book	2016
Neural Computing: What Scale and Complexity is Needed? Oak Ridge Neuromorphic Computing Workshop James Bradley Aimone, Kristofor David Carlson, Fredrick Rothganger	Abstract – 2016 Abstract	2016
Applying Uncertainty Quantification and Sensitivity Analysis to Large-Scale Hippocampal Brain Models 2016 Neuro-Inspired Computational Elements Workshop Kristofor David Carlson, James Bradley Aimone https://www.osti.gov/search/identifier:1345886	Conference Paper – 2016 Conference Paper	2016
Sandia MICrONS Phase 1 kickoff slides Machine Intelligence from Cortical Networks (MICrONS) Program Kickoff Frances S. Chance, James Bradley Aimone, Kristofor David Carlson, Warren Leon Davis, Timothy Malcolm Shead, Craig Michael Vineyard https://www.osti.gov/search/identifier:1514321	Presentation (non-conference) – 2016 Presentation (non-conference)	2016
Neural Computing at Sandia National Laboratories Rebooting Computing Summit (RCS 4) Craig Michael Vineyard, James Bradley Aimone, Michael Lewis Bernard, Kristofor David Carlson, Frances S. Chance, James C. Forsythe, Conrad D. James, Fredrick Rothganger, William Mark Severa, Ann Speed, Stephen Joseph Verzi, Christina E. Warrender, John S. Wagner, Leann Adams Miller https://www.osti.gov/search/identifier:1335732	Conference Paper – 2015 Conference Paper	2015
Applying uncertainty quantification and sensitivity analysis to large-scale hippocampal brain models The 9th Annual Postdoctoral Technical Showcase Kristofor David Carlson, James Bradley Aimone https://www.osti.gov/search/identifier:1338026	Display or Poster (non-conference) – 2015 Display or Poster (non-conference)	2015
Document Title	Type	Year

Results 1–25 of 28

Publications

How Neural Plasticity Boosts Performance of Spiking Neural Networks

Neurogenesis Deep Learning: Extending deep Networks to accommodate new classes

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to LSMs

An Efficient Implementation of a Liquid State Machine on the Spiking Temporal Processing Unit

An Efficient Implementation of a LSM on the Spiking Temporal Processing Unit

Neurogenesis Deep Learning

Learning, Homeostasis, and Neurogenesis in Neural Systems

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

Neurogenesis Deep Learning

Can we be formal in assessing the strengths and weaknesses of neural architectures? A case study using a spiking cross-correlation algorithm

Can we be formal in assessing the strengths and weaknesses of neural architectures? A case study using a spiking cross-correlation algorithm

Can we be formal in assessing the strengths and weaknesses of neural architectures? A case study using a spiking cross-correlation algorithm

Applying Uncertainty Quantification and Sensitivity Analysis to Spiking Neural Network Models of Asynchronous Irregular Firing Activity

Implementation of a Liquid State Machine with Temporal Dynamics on a Novel Spiking Neuromorphic Architecture

Spiking Network Algorithms for Scientific Computing

Spiking Network Algorithms for Scientific Computing

Neural Computing: What Scale and Complexity is Needed?

Sandia Neural Computing Overview

Applying uncertainty quantification and sensitivity analysis to spiking neural network models of asynchronous irregular firing activity

Computational Perspectives on Adult Neurogenesis

Neural Computing: What Scale and Complexity is Needed?

Applying Uncertainty Quantification and Sensitivity Analysis to Large-Scale Hippocampal Brain Models

Sandia MICrONS Phase 1 kickoff slides

Neural Computing at Sandia National Laboratories

Applying uncertainty quantification and sensitivity analysis to large-scale hippocampal brain models