David Rosenbluth

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

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.

Signal feature recognition based on lightwave neuromorphic signal processing

We developed a hybrid analog/digital lightwave neuromorphic processing device that effectively performs signal feature recognition. The approach, which mimics the neurons in a crayfish responsible for the escape response mechanism, provides a fast and accurate reaction to its inputs. The analog processing portion of the device uses the integration characteristic of an electro-absorption modulator, while the digital processing portion employ optical thresholding in a highly Ge-doped nonlinear loop mirror. The device can be configured to respond to different sets of input patterns by simply varying the weights and delays of the inputs. We experimentally demonstrated the use of the proposed lightwave neuromorphic signal processing device for recognizing specific input patterns.

A high performance photonic pulse processing device.

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.

Pulse lead/lag timing detection for adaptive feedback and control based on optical spike-timing-dependent plasticity.

Biological neurons perform information processing using a model called pulse processing, which is both computationally efficient and scalable, adopting the best features of both analog and digital computing. Implementing pulse processing with photonics can result in bandwidths that are billions of times faster than biological neurons and substantially faster than electronics. Neurons have the ability to learn and adapt their processing based on experience through a change in the strength of synaptic connections in response to spiking activity. This mechanism is called spike-timing-dependent plasticity (STDP). Functionally, STDP constitutes a mechanism in which strengths of connections between neurons are based on the timing and order between presynaptic spikes and postsynaptic spikes, essentially forming a pulse lead/lag timing detector that is useful in feedback control and adaptation. Here we report for the first time the demonstration of optical STDP that is useful in pulse lead/lag timing detection and apply it to automatic gain control of a photonic pulse processor.

Asynchronous spiking photonic neuron for lightwave neuromorphic signal processing

We developed an asynchronous spiking photonic neuron that forms the basic building block for hybrid analog/digital lightwave neuromorphic processing. Our approach enables completely asynchronous spiking in response to input signals while maximizing the throughput relative to synchronous approaches. Asynchronous operation is achieved by generating the spike source for the photonic neuron through four-wave mixing. This hybrid analog/digital photonic neuron has an electro-absorption modulator as the temporal integration unit for analog processing, while the digital processing portion employs optical thresholding in a highly Ge-doped nonlinear loop mirror.

Lightwave neuromorphic signal processing

Spike processing devices for optical computational systems have the potential to be scalable, computationally powerful, and have high operation bandwidth. They open up a range of optical processing applications for which electronic processing is too slow. In this paper, we demonstrate the feasibility of implementing simple photonic neuromorphic circuits, including the auditory localization algorithm of the barn owl, which is useful for LIDAR localization, and the crayfish tail-flip escape response. Our approach is based on a hybrid analog/digital computational primitive that elegantly implements the functionality of an integrate-and-fire neuron using a Ge-doped non-linear optical fiber and off-the-shelf semiconductor devices.

Lightwave Neuromorphic Signal Processing [In The Spotlight]

This article discusses a technique that promises to deliver improved optical computing. Specifically, neuromorphic engineering that can inspire novel optical computing devices. Neuromorphic engineering aims to develop practical computing and signal processing devices based on an understanding of the biophysics of neuronal computation.

Optical hybrid analog-digital signal processing based on spike processing in neurons

Spike processing is one kind of hybrid analog-digital signal processing, which has the efficiency of analog processing and the robustness to noise of digital processing. When instantiated with optics, a hybrid analog-digital processing primitive has the potential to be scalable, computationally powerful, and have high operation bandwidth. These devices open up a range of processing applications for which electronic processing is too slow. Our approach is based on a hybrid analog/digital computational primitive that elegantly implements the functionality of an integrate-and-fire neuron using a Ge-doped non-linear optical fiber and off-the-shelf semiconductor devices. In this paper, we introduce our photonic neuron architecture and demonstrate the feasibility of implementing simple photonic neuromorphic circuits, including the auditory localization algorithm of the barn owl, which is useful for LIDAR localization, and the crayfish tail-flip escape response.

A photonic neuromorphic computational primitive for complex high bandwidth signal processing

The photonic computational primitive presented in this paper is the first all optical implementation of a spiking leaky integrate and fire (LIF) neuron. Due to its hybrid analog/digital nature it is capable of overcoming both the noise problems of purely analog optical devices and the limited computational capabilities of individual digital optical devices. It is possible to chain together large numbers of these devices, each individually capable of significant computation, to implement far more complex computations on high bandwidth signals than is currently possible. This device represents both a technology capable of scaling the complexity of computations that can be performed on high-bandwidth signals, and a contribution to the nacent field of photonic neuromorphic engineering. This technology applies to a wide range of avionics applications in which data bandwidth is too high or the response delay too short for electronic processing.