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.

The ability to tackle analysis of the brain at multiple levels simultaneously is emerging from rapid methodological developments. The classical research strategies of "measure," "model," and "make" are being applied to the exploration of nervous system function. These include novel conceptual and theoretical approaches, creative use of mathematical modeling, and attempts to build brain-like devices and systems, as well as other developments including instrumentation and statistical modeling (not covered here). Increasingly, these efforts require teams of scientists from a variety of traditional scientific disciplines to work together. The potential of such efforts for understanding directed motor movement, emergence of cognitive function from neuronal activity, and development of neuromimetic computers are described by a team that includes individuals experienced in behavior and neuroscience, mathematics, and engineering. Funding agencies, including the National Science Foundation, explore the potential of these changing frontiers of research for developing research policies and long-term planning.

https://www.jneurosci.org/content/jneuro/27/44/11807.full.pdf

Neurotech for Neuroscience: Unifying Concepts, Organizing Principles, and Emerging Tools

Hardware implementation of spiking neural networks on FPGA

The explosion of &#x201C;big data&#x201D; applications imposes severe challenges of speed and scalability on traditional computer systems. As the performance of traditional Von Neumann machines is greatly hindered by the increasing performance gap between CPU and memory (&#x201C;known as the memory wall&#x201D;), neuromorphic computing systems have gained considerable attention. The biology-plausible computing paradigm carries out computing by emulating the charging/discharging process of neuron and synapse potential. The unique spike domain information encoding enables asynchronous event driven computation and communication, and hence has the potential for very high energy efficiency. This survey reviews computing models and hardware platforms of existing neuromorphic computing systems. Neuron and synapse models are first introduced, followed by the discussion on how they will affect hardware design. Case studies of several representative hardware platforms, including their architecture and software ecosystems, are further presented. Lastly we present several future research directions. 

A Survey on Neuromorphic Computing: Models and Hardware

Recently the authors presented transistor channel models of biological channels and the resulting implementation towards building spiking nodes, synapses, and dendrites. The authors also discussed how to build reconfigurable dendrites using programmable analog techniques. With all of this technology components available, the authors begin to address the question of the computation model possible using a dendrite element, as well as a network of dendrite elements. The authors discuss the connection between a dendrite element and a hidden Markov model (HMM) classifier branch, as well as a network of dendrites and somas to create an HMM classifier typical of what is used in speech recognition systems. The authors present simulation and experimental results for the branch elements; the authors also present initial results for a small dendrite based classifier structure to show the similarities to the HMM paradigm.

Transistor Channel Dendrites implementing HMM classifiers

An analog circuit capable of accurately emulating large complex cells, or multiple less complex ones is described. This circuit is termed the FPNA or the field programmable neural array. It is analogous to the more familiar FPGA, but is composed of biologically relevant circuit components including active channels, dendrites, and synapses. Taking each of these circuit models, and adding a routing structure capable of routing outputs from cells (or external inputs) to any individual synapse at any node yields a device which is capable of emulating complex biological circuits. This circuit opens doors to investigating what particular types of computation individual cells are performing, as well as small networks simpler cells.

A field programmable neural array

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A field programmable neural array