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Computational perspectives on adult neurogenesis

The Rewiring Brain: A Computational Approach to Structural Plasticity in the Adult Brain

Carlson, Kristofor D.; Rothganger, Fredrick R.; Aimone, James B.

The continuous integration of young neurons into the adult brain represents a novel form of structural plasticity and has inspired the creation of numerous computational models to understand the functional role of adult neurogenesis. These computational models consist of abstract models that focus on the utility of new neurons in simple neural networks and biologically based models constrained by anatomical data that explore the role of new neurons in specific neural circuits such as the hippocampus. Simulation results from both classes of models have suggested a number of theoretical roles for neurogenesis such as increasing the capacity to learn novel information, promoting temporal context encoding, and influencing pattern separation. In this review, we discuss strategies and findings of past computational modeling efforts, current challenges and limitations, and new computational approaches pertinent to modeling adult neurogenesis.

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TYPE Book YEAR 2017
ScopusOSTI

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

Biologically Inspired Cognitive Architectures

James, Conrad D.; Aimone, James B.; Miner, Nadine E.; Vineyard, Craig M.; Rothganger, Fredrick R.; Carlson, Kristofor D.; Mulder, Samuel A.; Draelos, Timothy J.; Faust, Aleksandra; Marinella, Matthew J.; Naegle, John H.; Plimpton, Steven J.

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 other professions 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 functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.

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TYPE Journal Article YEAR 2017
ScopusOSTIDOI

Computing with dynamical systems

2016 IEEE International Conference on Rebooting Computing, ICRC 2016 - Conference Proceedings

Rothganger, Fredrick R.; James, Conrad D.; Aimone, James B.

The effort to develop larger-scale computing systems introduces a set of related challenges: Large machines are more difficult to synchronize. The sheer quantity of hardware introduces more opportunities for errors. New approaches to hardware, such as low-energy or neuromorphic devices are not directly programmable by traditional methods. These three challenges may be addressed, at least for a subset of interesting problems, by a dynamical systems approach. The initial state of system represents the problem, and the final state of the system represents the solution. By carefully controlling the attractive basin of the system, we can move it between these two points while tolerating errors, which appear as perturbations. Here we describe both conventional and neural computers as dynamical systems, and show how to construct algorithms with resilience to noise, using traditional numerical problems as a special case. This suggests a reduction from numerical problems to spiking neural hardware such as IBM's TrueNorth.

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TYPE Conference Poster YEAR 2016
ScopusOSTIDOI

Training neural hardware with noisy components

Proceedings of the International Joint Conference on Neural Networks

Rothganger, Fredrick R.; Evans, Brian R.; Aimone, James B.; DeBenedictis, Erik

Some next generation computing devices may consist of resistive memory arranged as a crossbar. Currently, the dominant approach is to use crossbars as the weight matrix of a neural network, and to use learning algorithms that require small incremental weight updates, such as gradient descent (for example Backpropagation). Using real-world measurements, we demonstrate that resistive memory devices are unlikely to support such learning methods. As an alternative, we offer a random search algorithm tailored to the measured characteristics of our devices.

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TYPE Conference Poster YEAR 2015
ScopusOSTI

Development characterization and modeling of a TaOx ReRAM for a neuromorphic accelerator

Marinella, Matthew J.; Mickel, Patrick R.; Lohn, Andrew L.; Hughart, David R.; Bondi, Robert J.; Mamaluy, Denis M.; Hjalmarson, Harold P.; Stevens, James E.; Decker, Seth D.; Apodaca, Roger A.; Evans, Brian R.; Aimone, James B.; Rothganger, Fredrick R.; James, Conrad D.; DeBenedictis, Erik

This report discusses aspects of neuromorphic computing and how it is used to model microsystems.

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TYPE Conference Poster YEAR 2014
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Final report for LDRD project 11-0783 : directed robots for increased military manpower effectiveness

Rohrer, Brandon R.; Morrow, James D.; Rothganger, Fredrick R.; Xavier, Patrick G.; Wagner, John S.

The purpose of this LDRD is to develop technology allowing warfighters to provide high-level commands to their unmanned assets, freeing them to command a group of them or commit the bulk of their attention elsewhere. To this end, a brain-emulating cognition and control architecture (BECCA) was developed, incorporating novel and uniquely capable feature creation and reinforcement learning algorithms. BECCA was demonstrated on both a mobile manipulator platform and on a seven degree of freedom serial link robot arm. Existing military ground robots are almost universally teleoperated and occupy the complete attention of an operator. They may remove a soldier from harm's way, but they do not necessarily reduce manpower requirements. Current research efforts to solve the problem of autonomous operation in an unstructured, dynamic environment fall short of the desired performance. In order to increase the effectiveness of unmanned vehicle (UV) operators, we proposed to develop robots that can be 'directed' rather than remote-controlled. They are instructed and trained by human operators, rather than driven. The technical approach is modeled closely on psychological and neuroscientific models of human learning. Two Sandia-developed models are utilized in this effort: the Sandia Cognitive Framework (SCF), a cognitive psychology-based model of human processes, and BECCA, a psychophysical-based model of learning, motor control, and conceptualization. Together, these models span the functional space from perceptuo-motor abilities, to high-level motivational and attentional processes.

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TYPE SAND Report YEAR 2011
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Results 26–50 of 57
Results 26–50 of 57