The organization of behavior: A neuropsychological theory

Biophysics of Computation

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges. Photonics offers an attractive platform for implementing neuromorphic computing due to its low latency, multiplexing capabilities and integrated on-chip technology.

Photonics for artificial intelligence and neuromorphic computing

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence, in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, in particular, related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.

The physical layer of an optical network is vulnerable to a variety of attacks, including jamming, physical infrastructure attacks, eavesdropping, and interception. As the demand for network capacity grows dramatically, the issue of securing the physical layer of optical network cannot be overlooked. In this survey paper, we discuss the security threats in an optical network as well as present several existing optical techniques to improve the security. In the first part of this paper, we discuss various types of security threats that could appear in the optical layer of an optical network, including jamming, physical infrastructure attacks, eavesdropping, and interception. Intensive research has focused on improving optical network security, in the above specific areas. Real-time processing of the optical signal is essential in order to integrate security functionality at the physical layer while not undermining the true value of optical communications, which is its speed. Optical layer security benefits from the unique properties of optical processing-instantaneous response, broadband operation, electromagnetic immunity, compactness, and low latency. In the second part of this paper, various defenses against the security threats outlined in this paper are discussed, including optical encryption, optical code-division multiple access (CDMA) confidentiality, self-healing survivable optical rings, anti-jamming, and optical steganography.

Optical Layer Security in Fiber-Optic Networks

In this paper, we demonstrate for the first time an ultrafast fully functional photonic spiking neuron. Our experimental setup constitutes a complete all-optical implementation of a leaky integrate-and-fire neuron, a computational primitive that provides a basis for general purpose analog optical computation. Unlike purely analog computational models, spiking operation eliminates noise accumulation and results in robust and efficient processing. Operating at gigahertz speed, which corresponds to at least 108 speed-up compared with biological neurons, the demonstrated neuron provides all functionality required by the spiking neuron model. The two demonstrated prototypes and a demonstrated feedback operation mode prove the feasibility and stability of our approach and show the obtained performance characteristics.

Ultrafast all-optical implementation of a leaky integrate-and-fire neuron.

An optical analog self-interference cancellation system for radio-frequency communications is proposed and experimentally demonstrated. The system uses two electro-absorption modulators, an optical attenuator and delay, and a balanced photodetector to subtract strong self-interference from a corrupted received signal and recover a weak signal of interest. The system achieves >; 65 dB narrowband cancellation and >; 30 dB broadband cancellation across more than 40 MHz bandwidth for interference in both the 900 MHz and 2.4GHz bands. Both narrowband and broadband cancellation are frequency tunable, and can be combined with other forms of interference cancellation to achieve even higher levels of cancellation.

Optical Analog Self-Interference Cancellation Using Electro-Absorption Modulators

This paper presents an all optical fiber based implementation of a hybrid analog-digital computational primitive that provides a basis for complex processing on high bandwidth signals. A natural implementation of a hybrid analog/digital photonic processing primitive is achieved through the integration of new nonlinear fiber, and exploitation of the physics of semiconductor device to process signals in unique ways. Specifically, we describe the use of a semiconductor optical amplifier to implement leaky temporal integration of a signal and a highly Ge-doped nonlinear fiber for thresholding. A straightforward correspondence between our computational primitive and leaky-integrate-and-fire neurons permits leveraging of a large body of research characterizing the computational capabilities of these devices and the emerging pulse processing computational paradigm as a means to implement practical signal processing algorithms in hybrid computing platforms. An experimental demonstration of the behavior of the pulse processing primitive is presented.

A high performance photonic pulse processing device

A compact bidirectional soft curvature sensor is achieved by embedding a fiber Bragg grating (FBG) off-centered in a silicone sheet. The proposed approach is capable to distinguish both the bending curvatures and bending directions through wavelength shift measurement of the FBG. Based on the pure bending model, the relationship between the FBG embedded position and the sensor sensitivity is studied and verified experimentally. Real-time curvature measurement of both positive and negative bending directions is experimentally achieved. The curvature sensor has a consistent performance and the results are highly repeatable with small variances. A linear relationship between applied curvature and FBG wavelength shift is obtained, with a large measurement range of up to $\pm 80~\text{m}^{-1}$ curvature.

Mable P. Fok

Papers

Optical Layer Security in Fiber-Optic Networks

Ultrafast all-optical implementation of a leaky integrate-and-fire neuron.

Optical Analog Self-Interference Cancellation Using Electro-Absorption Modulators

A high performance photonic pulse processing device

Bidirectional Soft Silicone Curvature Sensor Based on Off-Centered Embedded Fiber Bragg Grating