Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems.

Anatomically incomplete spinal cord injuries are often followed by considerable functional recovery in patients and animal models, largely because of processes of neuronal plasticity. In contrast to the corticospinal system, where sprouting of fibers and rearrangements of circuits in response to lesions have been well studied, structural adaptations within descending brainstem pathways and intraspinal networks are poorly investigated, despite the recognized physiological significance of these systems across species. In the present study, spontaneous neuroanatomical plasticity of severed bulbospinal systems and propriospinal neurons was investigated following unilateral C4 spinal hemisection in adult rats. Injection of retrograde tracer into the ipsilesional segments C3-C4 revealed a specific increase in the projection from the ipsilesional gigantocellular reticular nucleus in response to the injury. Substantial regenerative fiber sprouting of reticulospinal axons above the injury site was demonstrated by anterograde tracing. Regrowing reticulospinal fibers exhibited excitatory, vGLUT2-positive varicosities, indicating their synaptic integration into spinal networks. Reticulospinal fibers formed close appositions onto descending, double-midline crossing C3-C4 propriospinal neurons, which crossed the lesion site in the intact half of the spinal cord and recrossed to the denervated cervical hemicord below the injury. These propriospinal projections around the lesion were significantly enhanced after injury. Our results suggest that severed reticulospinal fibers, which are part of the phylogenetically oldest motor command system, spontaneously arborize and form contacts onto a plastic propriospinal relay, thereby bypassing the lesion. These rearrangements were accompanied by substantial locomotor recovery, implying a potential physiological relevance of the detour in restoration of motor function after spinal injury.

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.