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Balasubramaniam S, Somathilaka S, Sun S, Ratwatte A, Pierobon M. Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges. IEEE NANOTECHNOLOGY MAGAZINE 2023; 17:10-20. [PMID: 38855043 PMCID: PMC11160936 DOI: 10.1109/mnano.2023.3262099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is the transport, processing, and interpretation of information propagated by molecules through chemical reactions. We begin by reviewing the current approaches that have been developed for MML, before we move towards potential new directions that rely on gene regulatory networks inside biological organisms as well as their population interactions to create neural networks. We then investigate mechanisms for training machine learning structures in biological cells based on calcium signaling and demonstrate their application to build an Analog to Digital Converter (ADC). Lastly, we look at potential future directions as well as challenges that this area could solve.
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Affiliation(s)
| | - Samitha Somathilaka
- School of Computing, University of Nebraska-Lincoln, NE, USA
- Walton Institute, South East Technological University, Ireland
| | - Sehee Sun
- School of Computing, University of Nebraska-Lincoln, NE, USA
| | - Adrian Ratwatte
- School of Computing, University of Nebraska-Lincoln, NE, USA
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2
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Schmidgall S, Hays J. Meta-SpikePropamine: learning to learn with synaptic plasticity in spiking neural networks. Front Neurosci 2023; 17:1183321. [PMID: 37250397 PMCID: PMC10213417 DOI: 10.3389/fnins.2023.1183321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/06/2023] [Indexed: 05/31/2023] Open
Abstract
We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Although substantial progress has been made toward understanding the dynamics of learning in the brain, neuroscience-derived models of learning have yet to demonstrate the same performance capabilities as methods in deep learning such as gradient descent. Inspired by the successes of machine learning using gradient descent, we introduce a bi-level optimization framework that seeks to both solve online learning tasks and improve the ability to learn online using models of plasticity from neuroscience. We demonstrate that models of three-factor learning with synaptic plasticity taken from the neuroscience literature can be trained in Spiking Neural Networks (SNNs) with gradient descent via a framework of learning-to-learn to address challenging online learning problems. This framework opens a new path toward developing neuroscience inspired online learning algorithms.
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Affiliation(s)
- Samuel Schmidgall
- U.S. Naval Research Laboratory, Spacecraft Engineering Department, Washington, DC, United States
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Joe Hays
- U.S. Naval Research Laboratory, Spacecraft Engineering Department, Washington, DC, United States
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3
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Mishra R, Suri M. A survey and perspective on neuromorphic continual learning systems. Front Neurosci 2023; 17:1149410. [PMID: 37214407 PMCID: PMC10194827 DOI: 10.3389/fnins.2023.1149410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 04/03/2023] [Indexed: 05/24/2023] Open
Abstract
With the advent of low-power neuromorphic computing systems, new possibilities have emerged for deployment in various sectors, like healthcare and transport, that require intelligent autonomous applications. These applications require reliable low-power solutions for sequentially adapting to new relevant data without loss of learning. Neuromorphic systems are inherently inspired by biological neural networks that have the potential to offer an efficient solution toward the feat of continual learning. With increasing attention in this area, we present a first comprehensive review of state-of-the-art neuromorphic continual learning (NCL) paradigms. The significance of our study is multi-fold. We summarize the recent progress and propose a plausible roadmap for developing end-to-end NCL systems. We also attempt to identify the gap between research and the real-world deployment of NCL systems in multiple applications. We do so by assessing the recent contributions in neuromorphic continual learning at multiple levels-applications, algorithms, architectures, and hardware. We discuss the relevance of NCL systems and draw out application-specific requisites. We analyze the biological underpinnings that are used for acquiring high-level performance. At the hardware level, we assess the ability of the current neuromorphic platforms and emerging nano-device-based architectures to support these algorithms in the presence of several constraints. Further, we propose refinements to continual learning metrics for applying them to NCL systems. Finally, the review identifies gaps and possible solutions that are not yet focused upon for deploying application-specific NCL systems in real-life scenarios.
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4
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Gao T, Deng B, Wang J, Yi G. Highly efficient neuromorphic learning system of spiking neural network with multi-compartment leaky integrate-and-fire neurons. Front Neurosci 2022; 16:929644. [PMID: 36248664 PMCID: PMC9554099 DOI: 10.3389/fnins.2022.929644] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
A spiking neural network (SNN) is considered a high-performance learning system that matches the digital circuits and presents higher efficiency due to the architecture and computation of spiking neurons. While implementing a SNN on a field-programmable gate array (FPGA), the gradient back-propagation through layers consumes a surprising number of resources. In this paper, we aim to realize an efficient architecture of SNN on the FPGA to reduce resource and power consumption. The multi-compartment leaky integrate-and-fire (MLIF) model is used to convert spike trains to the plateau potential in dendrites. We accumulate the potential in the apical dendrite during the training period. The average of this accumulative result is the dendritic plateau potential and is used to guide the updates of synaptic weights. Based on this architecture, the SNN is implemented on FPGA efficiently. In the implementation of a neuromorphic learning system, the shift multiplier (shift MUL) module and piecewise linear (PWL) algorithm are used to replace multipliers and complex nonlinear functions to match the digital circuits. The neuromorphic learning system is constructed with resources on FPGA without dataflow between on-chip and off-chip memories. Our neuromorphic learning system performs with higher resource utilization and power efficiency than previous on-chip learning systems.
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5
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Jedlicka P, Tomko M, Robins A, Abraham WC. Contributions by metaplasticity to solving the Catastrophic Forgetting Problem. Trends Neurosci 2022; 45:656-666. [PMID: 35798611 DOI: 10.1016/j.tins.2022.06.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/06/2022] [Accepted: 06/09/2022] [Indexed: 10/17/2022]
Abstract
Catastrophic forgetting (CF) refers to the sudden and severe loss of prior information in learning systems when acquiring new information. CF has been an Achilles heel of standard artificial neural networks (ANNs) when learning multiple tasks sequentially. The brain, by contrast, has solved this problem during evolution. Modellers now use a variety of strategies to overcome CF, many of which have parallels to cellular and circuit functions in the brain. One common strategy, based on metaplasticity phenomena, controls the future rate of change at key connections to help retain previously learned information. However, the metaplasticity properties so far used are only a subset of those existing in neurobiology. We propose that as models become more sophisticated, there could be value in drawing on a richer set of metaplasticity rules, especially when promoting continual learning in agents moving about the environment.
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Affiliation(s)
- Peter Jedlicka
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus Liebig University, Giessen, Germany; Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University Frankfurt, Frankfurt/Main, Germany; Frankfurt Institute for Advanced Studies, Frankfurt 60438, Germany.
| | - Matus Tomko
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus Liebig University, Giessen, Germany; Institute of Molecular Physiology and Genetics, Centre of Biosciences, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Anthony Robins
- Department of Computer Science, University of Otago, Dunedin 9016, New Zealand
| | - Wickliffe C Abraham
- Department of Psychology, Brain Health Research Centre, University of Otago, Dunedin 9054, New Zealand.
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6
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Yang Y, Truong ND, Eshraghian JK, Nikpour A, Kavehei O. Weak self-supervised learning for seizure forecasting: a feasibility study. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220374. [PMID: 35950196 PMCID: PMC9346358 DOI: 10.1098/rsos.220374] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/12/2022] [Indexed: 05/27/2023]
Abstract
This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system.
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Affiliation(s)
- Yikai Yang
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Jason K. Eshraghian
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Armin Nikpour
- Faculty of Medicine and Health, Central Clinical School, The University of Sydney, Sydney, New South Wales 2006, Australia
- Comprehensive Epilepsy Service and Department of Neurology, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
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7
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Müller E, Arnold E, Breitwieser O, Czierlinski M, Emmel A, Kaiser J, Mauch C, Schmitt S, Spilger P, Stock R, Stradmann Y, Weis J, Baumbach A, Billaudelle S, Cramer B, Ebert F, Göltz J, Ilmberger J, Karasenko V, Kleider M, Leibfried A, Pehle C, Schemmel J. A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware. Front Neurosci 2022; 16:884128. [PMID: 35663548 PMCID: PMC9157770 DOI: 10.3389/fnins.2022.884128] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/20/2022] [Indexed: 11/29/2022] Open
Abstract
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability, and efficiency.
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Affiliation(s)
- Eric Müller
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Elias Arnold
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Oliver Breitwieser
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Milena Czierlinski
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Arne Emmel
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Jakob Kaiser
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Christian Mauch
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Sebastian Schmitt
- Third Institute of Physics, University of Göttingen, Göttingen, Germany
| | - Philipp Spilger
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Raphael Stock
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Yannik Stradmann
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Johannes Weis
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Andreas Baumbach
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Department of Physiology, University of Bern, Bern, Switzerland
| | | | - Benjamin Cramer
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Falk Ebert
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Julian Göltz
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Joscha Ilmberger
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Vitali Karasenko
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Mitja Kleider
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Aron Leibfried
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Christian Pehle
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Johannes Schemmel
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
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8
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Pehle C, Billaudelle S, Cramer B, Kaiser J, Schreiber K, Stradmann Y, Weis J, Leibfried A, Müller E, Schemmel J. The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity. Front Neurosci 2022; 16:795876. [PMID: 35281488 PMCID: PMC8907969 DOI: 10.3389/fnins.2022.795876] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 01/27/2022] [Indexed: 12/30/2022] Open
Abstract
Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives. Brain-inspired computing today encompasses a class of approaches ranging from using novel nano-devices for computation to research into large-scale neuromorphic architectures, such as TrueNorth, SpiNNaker, BrainScaleS, Tianjic, and Loihi. While implementation details differ, spiking neural networks-sometimes referred to as the third generation of neural networks-are the common abstraction used to model computation with such systems. Here we describe the second generation of the BrainScaleS neuromorphic architecture, emphasizing applications enabled by this architecture. It combines a custom analog accelerator core supporting the accelerated physical emulation of bio-inspired spiking neural network primitives with a tightly coupled digital processor and a digital event-routing network.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Johannes Schemmel
- Electronic Visions, Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
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9
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Kwon KC, Baek JH, Hong K, Kim SY, Jang HW. Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing. NANO-MICRO LETTERS 2022; 14:58. [PMID: 35122527 PMCID: PMC8818077 DOI: 10.1007/s40820-021-00784-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/03/2021] [Indexed: 05/21/2023]
Abstract
Two-dimensional (2D) transition metal chalcogenides (TMC) and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices, particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems. The distinct properties such as high durability, electrical and optical tunability, clean surface, flexibility, and LEGO-staking capability enable simple fabrication with high integration density, energy-efficient operation, and high scalability. This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications, including the promise of 2D TMC materials and heterostructures, as well as the state-of-the-art demonstration of memristive devices. The challenges and future prospects for the development of these emerging materials and devices are also discussed. The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.
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Affiliation(s)
- Ki Chang Kwon
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
- Interdisciplinary Materials Measurement Institute, Korea Research Institute of Standards and Science (KRISS), Daejeon, 34133 Republic of Korea
| | - Ji Hyun Baek
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
| | - Kootak Hong
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
| | - Soo Young Kim
- Department of Materials Science and Engineering, Institute of Green Manufacturing Technology, Korea University, Seoul, 02841 Republic of Korea
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
- Advanced Institute of Convergence Technology, Seoul National University, Suwon, 16229 Korea
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10
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Schuman CD, Kulkarni SR, Parsa M, Mitchell JP, Date P, Kay B. Opportunities for neuromorphic computing algorithms and applications. NATURE COMPUTATIONAL SCIENCE 2022; 2:10-19. [PMID: 38177712 DOI: 10.1038/s43588-021-00184-y] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 12/07/2021] [Indexed: 01/06/2024]
Abstract
Neuromorphic computing technologies will be important for the future of computing, but much of the work in neuromorphic computing has focused on hardware development. Here, we review recent results in neuromorphic computing algorithms and applications. We highlight characteristics of neuromorphic computing technologies that make them attractive for the future of computing and we discuss opportunities for future development of algorithms and applications on these systems.
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Affiliation(s)
- Catherine D Schuman
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA.
| | - Shruti R Kulkarni
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Maryam Parsa
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA
| | - J Parker Mitchell
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Prasanna Date
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Bill Kay
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
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11
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Jordan J, Schmidt M, Senn W, Petrovici MA. Evolving interpretable plasticity for spiking networks. eLife 2021; 10:66273. [PMID: 34709176 PMCID: PMC8553337 DOI: 10.7554/elife.66273] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 08/19/2021] [Indexed: 11/25/2022] Open
Abstract
Continuous adaptation allows survival in an ever-changing world. Adjustments in the synaptic coupling strength between neurons are essential for this capability, setting us apart from simpler, hard-wired organisms. How these changes can be mathematically described at the phenomenological level, as so-called ‘plasticity rules’, is essential both for understanding biological information processing and for developing cognitively performant artificial systems. We suggest an automated approach for discovering biophysically plausible plasticity rules based on the definition of task families, associated performance measures and biophysical constraints. By evolving compact symbolic expressions, we ensure the discovered plasticity rules are amenable to intuitive understanding, fundamental for successful communication and human-guided generalization. We successfully apply our approach to typical learning scenarios and discover previously unknown mechanisms for learning efficiently from rewards, recover efficient gradient-descent methods for learning from target signals, and uncover various functionally equivalent STDP-like rules with tuned homeostatic mechanisms. Our brains are incredibly adaptive. Every day we form memories, acquire new knowledge or refine existing skills. This stands in contrast to our current computers, which typically can only perform pre-programmed actions. Our own ability to adapt is the result of a process called synaptic plasticity, in which the strength of the connections between neurons can change. To better understand brain function and build adaptive machines, researchers in neuroscience and artificial intelligence (AI) are modeling the underlying mechanisms. So far, most work towards this goal was guided by human intuition – that is, by the strategies scientists think are most likely to succeed. Despite the tremendous progress, this approach has two drawbacks. First, human time is limited and expensive. And second, researchers have a natural – and reasonable – tendency to incrementally improve upon existing models, rather than starting from scratch. Jordan, Schmidt et al. have now developed a new approach based on ‘evolutionary algorithms’. These computer programs search for solutions to problems by mimicking the process of biological evolution, such as the concept of survival of the fittest. The approach exploits the increasing availability of cheap but powerful computers. Compared to its predecessors (or indeed human brains), it also uses search strategies that are less biased by previous models. The evolutionary algorithms were presented with three typical learning scenarios. In the first, the computer had to spot a repeating pattern in a continuous stream of input without receiving feedback on how well it was doing. In the second scenario, the computer received virtual rewards whenever it behaved in the desired manner – an example of reinforcement learning. Finally, in the third ‘supervised learning’ scenario, the computer was told exactly how much its behavior deviated from the desired behavior. For each of these scenarios, the evolutionary algorithms were able to discover mechanisms of synaptic plasticity to solve the new task successfully. Using evolutionary algorithms to study how computers ‘learn’ will provide new insights into how brains function in health and disease. It could also pave the way for developing intelligent machines that can better adapt to the needs of their users.
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Affiliation(s)
- Jakob Jordan
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Maximilian Schmidt
- Ascent Robotics, Tokyo, Japan.,RIKEN Center for Brain Science, Tokyo, Japan
| | - Walter Senn
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Mihai A Petrovici
- Department of Physiology, University of Bern, Bern, Switzerland.,Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
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12
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Zhang W, Wang Y, Ji X, Wu Y, Zhao R. ROA: A Rapid Learning Scheme for In-Situ Memristor Networks. Front Artif Intell 2021; 4:692065. [PMID: 34723173 PMCID: PMC8554302 DOI: 10.3389/frai.2021.692065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 09/10/2021] [Indexed: 11/13/2022] Open
Abstract
Memristors show great promise in neuromorphic computing owing to their high-density integration, fast computing and low-energy consumption. However, the non-ideal update of synaptic weight in memristor devices, including nonlinearity, asymmetry and device variation, still poses challenges to the in-situ learning of memristors, thereby limiting their broad applications. Although the existing offline learning schemes can avoid this problem by transferring the weight optimization process into cloud, it is difficult to adapt to unseen tasks and uncertain environments. Here, we propose a bi-level meta-learning scheme that can alleviate the non-ideal update problem, and achieve fast adaptation and high accuracy, named Rapid One-step Adaption (ROA). By introducing a special regularization constraint and a dynamic learning rate strategy for in-situ learning, the ROA method effectively combines offline pre-training and online rapid one-step adaption. Furthermore, we implemented it on memristor-based neural networks to solve few-shot learning tasks, proving its superiority over the pure offline and online schemes under noisy conditions. This method can solve in-situ learning in non-ideal memristor networks, providing potential applications of on-chip neuromorphic learning and edge computing.
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Affiliation(s)
| | | | | | | | - Rong Zhao
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Beijing Innovation Center for Future Chip, Tsinghua University, Beijing, China
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13
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Jiang R, Zhang J, Yan R, Tang H. Few-Shot Learning in Spiking Neural Networks by Multi-Timescale Optimization. Neural Comput 2021; 33:2439-2472. [PMID: 34280263 DOI: 10.1162/neco_a_01423] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 04/01/2021] [Indexed: 11/04/2022]
Abstract
Learning new concepts rapidly from a few examples is an open issue in spike-based machine learning. This few-shot learning imposes substantial challenges to the current learning methodologies of spiking neuron networks (SNNs) due to the lack of task-related priori knowledge. The recent learning-to-learn (L2L) approach allows SNNs to acquire priori knowledge through example-level learning and task-level optimization. However, an existing L2L-based framework does not target the neural dynamics (i.e., neuronal and synaptic parameter changes) on different timescales. This diversity of temporal dynamics is an important attribute in spike-based learning, which facilitates the networks to rapidly acquire knowledge from very few examples and gradually integrate this knowledge. In this work, we consider the neural dynamics on various timescales and provide a multi-timescale optimization (MTSO) framework for SNNs. This framework introduces an adaptive-gated LSTM to accommodate two different timescales of neural dynamics: short-term learning and long-term evolution. Short-term learning is a fast knowledge acquisition process achieved by a novel surrogate gradient online learning (SGOL) algorithm, where the LSTM guides gradient updating of SNN on a short timescale through an adaptive learning rate and weight decay gating. The long-term evolution aims to slowly integrate acquired knowledge and form, which can be achieved by optimizing the LSTM guidance process to tune SNN parameters on a long timescale. Experimental results demonstrate that the collaborative optimization of multi-timescale neural dynamics can make SNNs achieve promising performance for the few-shot learning tasks.
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Affiliation(s)
- Runhao Jiang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Jie Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Rui Yan
- College of Computer Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China, and Zhejiang Lab, Hangzhou 311121, China
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Covi E, Donati E, Liang X, Kappel D, Heidari H, Payvand M, Wang W. Adaptive Extreme Edge Computing for Wearable Devices. Front Neurosci 2021; 15:611300. [PMID: 34045939 PMCID: PMC8144334 DOI: 10.3389/fnins.2021.611300] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 03/24/2021] [Indexed: 11/13/2022] Open
Abstract
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.
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Affiliation(s)
| | - Elisa Donati
- Institute of Neuroinformatics, University of Zurich, Eidgenössische Technische Hochschule Zürich (ETHZ), Zurich, Switzerland
| | - Xiangpeng Liang
- Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - David Kappel
- Bernstein Center for Computational Neuroscience, III Physikalisches Institut–Biophysik, Georg-August Universität, Göttingen, Germany
| | - Hadi Heidari
- Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Melika Payvand
- Institute of Neuroinformatics, University of Zurich, Eidgenössische Technische Hochschule Zürich (ETHZ), Zurich, Switzerland
| | - Wei Wang
- The Andrew and Erna Viterbi Department of Electrical Engineering, Technion–Israel Institute of Technology, Haifa, Israel
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Structural plasticity on an accelerated analog neuromorphic hardware system. Neural Netw 2020; 133:11-20. [PMID: 33091719 DOI: 10.1016/j.neunet.2020.09.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 07/17/2020] [Accepted: 09/28/2020] [Indexed: 11/23/2022]
Abstract
In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural connectivity and synaptic capacity depend on their specific design choices, but is always intrinsically limited. Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and postsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse. In particular, we implemented this algorithm on the analog neuromorphic system BrainScaleS-2. It was executed on a custom embedded digital processor located on chip, accompanying the mixed-signal substrate of spiking neurons and synapse circuits. We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology with respect to the nature of its training data, as well as its overall computational efficiency.
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16
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Parsa M, Mitchell JP, Schuman CD, Patton RM, Potok TE, Roy K. Bayesian Multi-objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design. Front Neurosci 2020; 14:667. [PMID: 32848531 PMCID: PMC7396641 DOI: 10.3389/fnins.2020.00667] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 06/02/2020] [Indexed: 11/29/2022] Open
Abstract
In resource-constrained environments, such as low-power edge devices and smart sensors, deploying a fast, compact, and accurate intelligent system with minimum energy is indispensable. Embedding intelligence can be achieved using neural networks on neuromorphic hardware. Designing such networks would require determining several inherent hyperparameters. A key challenge is to find the optimum set of hyperparameters that might belong to the input/output encoding modules, the neural network itself, the application, or the underlying hardware. In this work, we present a hierarchical pseudo agent-based multi-objective Bayesian hyperparameter optimization framework (both software and hardware) that not only maximizes the performance of the network, but also minimizes the energy and area requirements of the corresponding neuromorphic hardware. We validate performance of our approach (in terms of accuracy and computation speed) on several control and classification applications on digital and mixed-signal (memristor-based) neural accelerators. We show that the optimum set of hyperparameters might drastically improve the performance of one application (i.e., 52–71% for Pole-Balance), while having minimum effect on another (i.e., 50–53% for RoboNav). In addition, we demonstrate resiliency of different input/output encoding, training neural network, or the underlying accelerator modules in a neuromorphic system to the changes of the hyperparameters.
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Affiliation(s)
- Maryam Parsa
- Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States.,Computational Data Analytics, Oak Ridge National Laboratory, Oak Ridge, IN, United States
| | - John P Mitchell
- Computational Data Analytics, Oak Ridge National Laboratory, Oak Ridge, IN, United States
| | - Catherine D Schuman
- Computational Data Analytics, Oak Ridge National Laboratory, Oak Ridge, IN, United States
| | - Robert M Patton
- Computational Data Analytics, Oak Ridge National Laboratory, Oak Ridge, IN, United States
| | - Thomas E Potok
- Computational Data Analytics, Oak Ridge National Laboratory, Oak Ridge, IN, United States
| | - Kaushik Roy
- Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
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