Neuromorphic engineering: neuromimetic computation for understanding the brain

Neuromorphic engineering attempts to understand the computational properties of neural processing systems by building electronic circuits and systems that emulate the principles of computation in the neural systems. The electronic systems that are developed in this process can serve both engineering and life sciences in various ways ranging from low-power brain-like computing embedded systems to neural-based control, brain machine interfaces, and neuroprosthesis. To realize such systems, various approaches and strategies with their own advantages and limitations, may be adopted. Here, we provide a summary of our recent article published in the proceedings of the IEEE [1], where we have discussed and reviewed the various approaches to the design and implementation of neuromorphic learning systems, and pointed out challenges and opportunities in these systems

Neuromorphic engineering: neuromimetic computation for understanding the brain

Neuromorphic engineering attempts to understand the computational properties of neural processing systems by building electronic circuits and systems that emulate the principles of computation in the neural systems. The electronic systems that are developed in this process can serve both engineering and life sciences in various ways ranging from low-power brain-like computing embedded systems to neural-based control, brain machine interfaces, and neuroprosthesis. To realize such systems, various approaches and strategies with their own advantages and limitations, may be adopted. Here, we provide a summary of our recent article published in the proceedings of the IEEE [1], where we have discussed and reviewed the various approaches to the design and implementation of neuromorphic learning systems, and pointed out challenges and opportunities in these systems

Electronic system with memristive synapses for pattern recognition

Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.open114844sciescopu

A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)

Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multi-core neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure

Dynamical Systems in Spiking Neuromorphic Hardware

Dynamical systems are universal computers. They can perceive stimuli, remember, learn from feedback, plan sequences of actions, and coordinate complex behavioural responses. The Neural Engineering Framework (NEF) provides a general recipe to formulate models of such systems as coupled sets of nonlinear differential equations and compile them onto recurrently connected spiking neural networks – akin to a programming language for spiking models of computation. The Nengo software ecosystem supports the NEF and compiles such models onto neuromorphic hardware. In this thesis, we analyze the theory driving the success of the NEF, and expose several core principles underpinning its correctness, scalability, completeness, robustness, and extensibility. We also derive novel theoretical extensions to the framework that enable it to far more effectively leverage a wide variety of dynamics in digital hardware, and to exploit the device-level physics in analog hardware. At the same time, we propose a novel set of spiking algorithms that recruit an optimal nonlinear encoding of time, which we call the Delay Network (DN). Backpropagation across stacked layers of DNs dramatically outperforms stacked Long Short-Term Memory (LSTM) networks—a state-of-the-art deep recurrent architecture—in accuracy and training time, on a continuous-time memory task, and a chaotic time-series prediction benchmark. The basic component of this network is shown to function on state-of-the-art spiking neuromorphic hardware including Braindrop and Loihi. This implementation approaches the energy-efficiency of the human brain in the former case, and the precision of conventional computation in the latter case

Real-time Neuromorphic Visual Pre-Processing and Dynamic Saliency

The human brain is by far the most computationally complex, efficient, and reliable computing system operating under such low-power, small-size, and light-weight specifications. Within the field of neuromorphic engineering, we seek to design systems with facsimiles to that of the human brain with means to reach its desirable properties. In this doctoral work, the focus is within the realm of vision, specifically visual saliency and related visual tasks with bio-inspired, real-time processing. The human visual system, from the retina through the visual cortical hierarchy, is responsible for extracting visual information and processing this information, forming our visual perception. This visual information is transmitted through these various layers of the visual system via spikes (or action potentials), representing information in the temporal domain. The objective is to exploit this neurological communication protocol and functionality within the systems we design. This approach is essential for the advancement of autonomous, mobile agents (i.e. drones/MAVs, cars) which must perform visual tasks under size and power constraints in which traditional CPU or GPU implementations to not suffice. Although the high-level objective is to design a complete visual processor with direct physical and functional correlates to the human visual system, we focus on three specific tasks. The first focus of this thesis is the integration of motion into a biologically-plausible proto-object-based visual saliency model. Laurent Itti, one of the pioneers in the field, defines visual saliency as ``the distinct subjective perceptual quality which makes some items in the world stand out from their neighbors and immediately grab our attention.'' From humans to insects, visual saliency is important for the extraction of only interesting regions of visual stimuli for further processing. Prior to this doctoral work, Russel et al. \cite{russell2014model} designed a model of proto-object-based visual saliency with biological correlates. This model was designed for computing saliency only on static images. However, motion is a naturally occurring phenomena that plays an essential role in both human and animal visual processing. Henceforth, the most ideal model of visual saliency should consider motion that may be exhibited within the visual scene. In this work a novel dynamic proto-object-based visual saliency is described which extends the Russel et. al. saliency model to consider not only static, but also temporal information. This model was validated by using metrics for determining how accurate the model is in predicting human eye fixations and saccades on a public dataset of videos with attached eye tracking data. This model outperformed other state-of-the-art visual saliency models in computing dynamic visual saliency. Such a model that can accurately predict where humans look, can serve as a front-end component to other visual processors performing tasks such as object detection and recognition, or object tracking. In doing so it can reduce throughput and increase processing speed for such tasks. Furthermore, it has more obvious applications in artificial intelligence in mimicking the functionality of the human visual system. The second focus of this thesis is the implementation of this visual saliency model on an FPGA (Field Programmable Gate Array) for real-time processing. Initially, this model was designed within MATLAB, a software-based approach running on a CPU, which limits the processing speed and consumes unnecessary amounts of power due to overhead. This is detrimental for integration with an autonomous, mobile system which must operate in real-time. This novel FPGA implementation allows for a low-power, high-speed approach to computing visual saliency. There are a few existing FPGA-based implementations of visual saliency, and of those, none are based on the notion of proto-objects. This work presents the first, to our knowledge, FPGA implementation of an object-based visual saliency model. Such an FPGA implementation allows for the low-power, light-weight, and small-size specifications that we seek within the field of neuromorphic engineering. For validating the FPGA model, the same metrics are used for determining the extent to which it predicts human eye saccades and fixations. We compare this hardware implementation to the software model for validation. The third focus of this thesis is the design of a generic neuromorphic platform both on FPGA and VLSI (Very-Large-Scale-Integration) technology for performing visual tasks, including those necessary in the computation of the visual saliency. Visual processing tasks such as image filtering and image dewarping are demonstrated via this novel neuromorphic technology consisting of an array of hardware-based generalized integrate-and-fire neurons. It allows the visual saliency model's computation to be offloaded onto this hardware-based architecture. We first demonstrate an emulation of this neuromorphic system on FPGA demonstrating its capability of dewarping and filtering tasks as well as integration with a neuromorphic camera called the ATIS (Asynchronous Time-based Image Sensor). We then demonstrate the neuromorphic platform implemented in CMOS technology, specifically designed for low-mismatch, high-density, and low-power. Such a VLSI technology-based platform further bridges the gap between engineering and biology and moves us closer towards developing a complete neuromorphic visual processor