Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information.

Aimone, James B.; Betty, Rita G.

doi:10.2172/1172176

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A major challenge for the neuroscience field is the difficulty in making the connection between neural activity and function across scales. High performance computing resources have been leveraged to use information theory to understand how individual cell-based phenomena such as adult neurogenesis can impact the overall computational capability of a large network (Aimone et al, 2014). New initiatives at U.S. federal agencies are bridging this gap between the molecular biology of individual neurons and cognitive functions (Cepelewicz, 2016), and the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative is focused on developing new tools for such measurements (Insel et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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

James

Aimone

Miner

et al. 2017

Biologically Inspired Cognitive Architectures

View full text Add to dashboard Cite

Biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and others were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. In addition, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the

show abstract

Section: Introductionmentioning

confidence: 99%