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Detorakis G, Sheik S, Augustine C, Paul S, Pedroni BU, Dutt N, Krichmar J, Cauwenberghs G, Neftci E. Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning. Front Neurosci 2018; 12:583. [PMID: 30210274 PMCID: PMC6123384 DOI: 10.3389/fnins.2018.00583] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 08/03/2018] [Indexed: 11/13/2022] Open
Abstract
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large scales that are both flexible and efficient have been hindered by a lack of a suitable algorithmic framework. As a result, most neuromorphic hardware are trained off-line on large clusters of dedicated processors or GPUs and transferred post hoc to the device. We address this by introducing the neural and synaptic array transceiver (NSAT), a neuromorphic computational framework facilitating flexible and efficient embedded learning by matching algorithmic requirements and neural and synaptic dynamics. NSAT supports event-driven supervised, unsupervised and reinforcement learning algorithms including deep learning. We demonstrate the NSAT in a wide range of tasks, including the simulation of Mihalas-Niebur neuron, dynamic neural fields, event-driven random back-propagation for event-based deep learning, event-based contrastive divergence for unsupervised learning, and voltage-based learning rules for sequence learning. We anticipate that this contribution will establish the foundation for a new generation of devices enabling adaptive mobile systems, wearable devices, and robots with data-driven autonomy.
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Affiliation(s)
- Georgios Detorakis
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Sadique Sheik
- Biocircuits Institute, University of California, San Diego, La Jolla, CA, United States
| | - Charles Augustine
- Intel Corporation-Circuit Research Lab, Hillsboro, OR, United States
| | - Somnath Paul
- Intel Corporation-Circuit Research Lab, Hillsboro, OR, United States
| | - Bruno U. Pedroni
- Department of Bioengineering and Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
| | - Nikil Dutt
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Jeffrey Krichmar
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Gert Cauwenberghs
- Department of Bioengineering and Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
| | - Emre Neftci
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
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Deadwyler SA, Berger TW, Opris I, Song D, Hampson RE. Neurons and networks organizing and sequencing memories. Brain Res 2014; 1621:335-44. [PMID: 25553617 DOI: 10.1016/j.brainres.2014.12.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Revised: 12/16/2014] [Accepted: 12/17/2014] [Indexed: 01/23/2023]
Abstract
Hippocampal CA1 and CA3 neurons sampled randomly in large numbers in primate brain show conclusive examples of hierarchical encoding of task specific information. Hierarchical encoding allows multi-task utilization of the same hippocampal neural networks via distributed firing between neurons that respond to subsets, attributes or "categories" of stimulus features which can be applied in events in different contexts. In addition, such networks are uniquely adaptable to neural systems unrestricted by rigid synaptic architecture (i.e. columns, layers or "patches") which physically limits the number of possible task-specific interactions between neurons. Also hierarchical encoding is not random; it requires multiple exposures to the same types of relevant events to elevate synaptic connectivity between neurons for different stimulus features that occur in different task-dependent contexts. The large number of cells within associated hierarchical circuits in structures such as hippocampus provides efficient processing of information relevant to common memory-dependent behavioral decisions within different contextual circumstances. This article is part of a Special Issue entitled SI: Brain and Memory.
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Affiliation(s)
- Sam A Deadwyler
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1083, USA.
| | - Theodore W Berger
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 1042 Downey Way (DRB140), Los Angeles, CA 90089-1111, USA
| | - Ioan Opris
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1083, USA
| | - Dong Song
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 1042 Downey Way (DRB140), Los Angeles, CA 90089-1111, USA
| | - Robert E Hampson
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1083, USA
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