Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

/pdf/neuromorphic-silicon-neuron-circuits-3fphnmpvq9.pdf

Neuromorphic Silicon Neuron Circuits

Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using “neural compiler” to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.

/pdf/neuromorphic-photonic-networks-using-silicon-photonic-weight-3o4fn6wx4m.pdf

Neuromorphic photonic networks using silicon photonic weight banks.

After complete spinal cord transections that removed all supraspinal inputs in adult rats, combinations of serotonergic agonists and epidural electrical stimulation were able to acutely transform spinal networks from nonfunctional to highly functional and adaptive states as early as 1 week after injury. Using kinematics, physiological and anatomical analyses, we found that these interventions could recruit specific populations of spinal circuits, refine their control via sensory input and functionally remodel these locomotor pathways when combined with training. The emergence of these new functional states enabled full weight-bearing treadmill locomotion in paralyzed rats that was almost indistinguishable from voluntary stepping. We propose that, in the absence of supraspinal input, spinal locomotion can emerge from a combination of central pattern-generating capability and the ability of these spinal circuits to use sensory afferent input to control stepping. These findings provide a strategy by which individuals with spinal cord injuries could regain substantial levels of motor control.

/pdf/transformation-of-nonfunctional-spinal-circuits-into-20ye1d6358.pdf

Transformation of nonfunctional spinal circuits into functional states after the loss of brain input.

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.

Spinal cord injury disrupts the communication between the brain and the spinal circuits that orchestrate movement. To bypass the lesion, brain-computer interfaces have directly linked cortical activity to electrical stimulation of muscles, and have thus restored grasping abilities after hand paralysis. Theoretically, this strategy could also restore control over leg muscle activity for walking. However, replicating the complex sequence of individual muscle activation patterns underlying natural and adaptive locomotor movements poses formidable conceptual and technological challenges. Recently, it was shown in rats that epidural electrical stimulation of the lumbar spinal cord can reproduce the natural activation of synergistic muscle groups producing locomotion. Here we interface leg motor cortex activity with epidural electrical stimulation protocols to establish a brain-spine interface that alleviated gait deficits after a spinal cord injury in non-human primates. Rhesus monkeys (Macaca mulatta) were implanted with an intracortical microelectrode array in the leg area of the motor cortex and with a spinal cord stimulation system composed of a spatially selective epidural implant and a pulse generator with real-time triggering capabilities. We designed and implemented wireless control systems that linked online neural decoding of extension and flexion motor states with stimulation protocols promoting these movements. These systems allowed the monkeys to behave freely without any restrictions or constraining tethered electronics. After validation of the brain-spine interface in intact (uninjured) monkeys, we performed a unilateral corticospinal tract lesion at the thoracic level. As early as six days post-injury and without prior training of the monkeys, the brain-spine interface restored weight-bearing locomotion of the paralysed leg on a treadmill and overground. The implantable components integrated in the brain-spine interface have all been approved for investigational applications in similar human research, suggesting a practical translational pathway for proof-of-concept studies in people with spinal cord injury.

/pdf/a-brain-spine-interface-alleviating-gait-deficits-after-1y9dltwbsl.pdf

A brain–spine interface alleviating gait deficits after spinal cord injury in primates

In this paper, we examine the control signals that are required to generate stepping using two different intraspinal microstimulation (ISMS) paradigms and discuss the theoretical feasibility of controlling ISMS-evoked stepping using a brain computer interface. Tonic (constant amplitude) and phasic (modulated amplitude) ISMS protocols were used to produce stepping in the hind limbs of paralyzed cats. Low-amplitude tonic ISMS activated a spinal locomotor-like network that resulted in bilateral stepping of the hind limbs. Phasic ISMS generated coordinated stepping by simultaneously activating flexor synergies in one limb coupled with extensor synergies in the other. Using these ISMS paradigms, we propose that one or two independent cortical signals will be adequate for controlling ISMS-induced stepping after SCI.

Could cortical signals control intraspinal stimulators? A theoretical evaluation

Tapping into the Spinal Cord for Restoring Function after Spinal Cord Injury

Stimulation in the Neonatal Rat Spinal Cord In Vitro Locomotor Rhythm Evoked by Ventrolateral Funiculus

It is commonly accepted that locomotor-related neuronal circuitry resides in the lumbosacral spinal cord. Pharmacological agents, epidural electrical stimulation, and sensory stimulation can be used to activate these intrinsic networks in in vitro neonatal rat and in vivo cat preparations. In this study, we investigated the use of low-level tonic intraspinal microstimulation (ISMS) as a means of activating spinal locomotor networks in adult cats with complete spinal transections. Trains of low-amplitude electrical pulses were delivered to the spinal cord via groups of fine microwires implanted in the ventral horns of the lumbosacral enlargement. In contrast to published reports, tonic ISMS applied through microwires in the caudal regions of the lumbosacral enlargement (L7-S1) was more effective in eliciting alternating movements in the hindlimbs than stimulation in the rostral regions. Possible mechanisms of action of tonic ISMS include depolarization of locally oscillating networks in the lumbosacral cord, backfiring of primary afferents, or activation of propriospinal neurons

Locomotor-Related Networks in the Lumbosacral Enlargement of the Adult Spinal Cat: Activation Through Intraspinal Microstimulation

We present a neuromorphic silicon chip that emulates the activity of the biological spinal central pattern generator (CPG) and creates locomotor patterns to support walking. The chip implements ten integrate-and-fire silicon neurons and 190 programmable digital-to-analog converters that act as synapses. This architecture allows for each neuron to make synaptic connections to any of the other neurons as well as to any of eight external input signals and one tonic bias input. The chip's functionality is confirmed by a series of experiments in which it controls the motor output of a paralyzed animal in real-time and enables it to walk along a three-meter platform. The walking is controlled under closed-loop conditions with the aide of sensory feedback that is recorded from the animal's legs and fed into the silicon CPG. Although we and others have previously described biomimetic silicon locomotor control systems for robots, this is the first demonstration of a neuromorphic device that can replace some functions of the central nervous system in vivo.

A Silicon Central Pattern Generator Controls Locomotion in Vivo

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