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A number of Centers and Departments contribute to Sandia's Neuroscience program.  These include: Center for Computing Research (CCR)

Rad Edge RAD-EDGE Sandia is developing analog computing accelerators to enable the deployment of trusted artificial intelligence (AI) at the point-of-sensing or at the edge in our nation’s mobile, airborne and satellite systems. We will co-design intelligent algorithms with tailored analog in-memory computing to achieve 100X more compute (performance/watt) than...

Computer Science Fred Rothganger is a refugee from the world of Artificial Intelligence. He earned his PhD playing with robots and computer vision at the University of Illinois. After sliding into despair about the inherent ceiling of modern AI techniques, he turned to the brain for answers. Unfortunately, Fred's own...

Laboratory Directed Research and Development Grand Challenge The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) Project is focused on developing neural-inspired algorithms to challenging pattern recognition problems in the imaging and cybersecurity domains. We have developed a non-spiking algorithm termed “neurogenesis deep learning” that incorporates the functionality of neurogenesis into...

Sandia’s Human Performance Lab is home to numerous capabilities for conducting cognitive neuroscience research. The lab’s resources include 64- and 128-channel EEG systems, a 16-channel portable EEG system, eye tracking systems, and a transcranial direct current stimulation (tDCS) system. In addition to our in-house capabilities, we have access to other...

The Need for Neuromorphic Computing Neuromorphic computers are energy-efficient computers that are designed to mimic the human brain. Neuromorphic computers have the potential to dramatically improve computing in environments where size, weight, and power (SWaP) are constrained. However, neuromorphic computing is still in its infancy. The process of inventing, designing,...

MAMMAL The process of discovering, designing, and optimizing neuromorphic computers is labor-intensive and requires a highly-skilled workforce. In order to accelerate the development of neuromorphic computers, we created an AI-enhanced codesign tool called Modular And Multi-level MAchine Learning (MAMMAL). MAMMAL is capable of automatically designing simple, dendrite-like signal processing circuits...

Techniques for modeling large scale neural systems have long been appreciated to be critical to understanding the function of complex neural circuits.  Leveraging Sandia's strengths in large scale modeling and simulation of physical systems, we have a number of efforts focused on improving the technical capabilities available for neuroscientists to...

Neural computing research at Sandia covers the full spectrum from theoretical neuroscience to neural algorithm development to neuromorphic architectures and hardware.  The neural computing effort is directed at impacting a number of real-world applications relevant to national security.  These applications include improved cyber security and cyber defenses, embedded pattern recognition...

NERL Overview Sandia’s Neural Exploration & Research Laboratory enables researchers to explore the boundaries of neural computation. The research conducted in the lab evaluates what is possible The SNL NERL facility enables researchers to explore the boundaries of neural computation. The research conducted in the lab evaluates what is possible...

Theoretical neuroscience research at Sandia focuses primarily on developing theories for different biological neural circuits and understanding basic neural computation concepts that may eventually impact neural machine learning.   Coordinate Transformations from Dragonflies to Neuromorphic Hardware We combine fundamental neuroscience research with exploration of novel neuromorphic architectures to understand the...

Neuroscience is increasingly a global challenge, with data and models produced by laboratories and research groups around the world.  The US BRAIN Initiative and other large scale neural efforts are producing data at ever increasing scales, with the necessary capabilities to consolidate and analyze this data being increasingly appreciated by...

Fugu Fugu is a spiking neural network library designed to produce algorithms that are composable, scalable and portable.  Developers define spiking neural network functions (“Bricks”) using procedural code to generate platform-agnostic networks.  These networks are then complied to specific execution backends, either spiking simulators or spiking neuromorphic hardware.  This approach...

2018 WCCI Workshop Submission Information Submit papers here: https://easychair.org/cfp/nipu2018Deadline: April 30, 2018 Organizers Craig M. Vineyard, PhDSandia National Laboratories*cmviney@sandia.gov William M. Severa, PhDSandia National Laboratories*wmsever@sandia.gov Kristofor D. Carlson, PhDBrainChip Inc.kcarlson@brainchipinc.com List of committed program committee members James B. Aimone, PhD – Sandia National Laboratories Conrad D. James, PhD – Sandia...

Neural Theory - Formal characterization of information representations and metrics for assessing the computation of neural regions. Modeling and Simulation - Developing software tools and methods for generating and deploying large-scale models of neural systems on conventional high-performance computing platforms and neural hardware systems. Neural-inspired Computing - Designing new computing architectures and device materials...

NERL at NICE 2023 Research Personnel Brad Aimone (Computational Neuroscience) Suma Cardwell (Electrical & Computer Engineering) Frances Chance (Computational Neuroscience) William Chapman (Computational Neuroscience) Cale Crowder (Artificial Intelligence) Ryan Dellana (Computer Science) Efrain Gonzalez (Mathematics) Michael Krygier (Physics) Srideep Musuvathy (Electrical & Control Engineering) Mark Plagge (Neuromorphic Computing) Fred Rothganger (Neuromorphic Computing) William...

Sandia Neuroscience in the News! Sandia's Billion Neuron Neuromorphic System Read More Frances Chance's Dragonfly TED Talk Watch the video Research Areas Neural Theory & Computational Neuroscience - Formal characterization of information representations and metrics for assessing the computation of neural regions.Brain-Inspired Machine Learning - Online learning, dendritic non-linear processingNeuromorphic...