Bridging the gap: a reticulo-propriospinal detour bypassing an incomplete spinal cord injury.

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.

Single neuromorphic memristor closely emulates multiple synaptic mechanisms for energy efficient neural networks.

Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or learning-to-learn. The resulting bio-inspired DNN is then trained to play the video game Atari Pong, a complex reinforcement learning task in a dynamic environment. Our analysis shows that the energy consumption of the DNN with multi-functional memristive synapses decreases by about two orders of magnitude as compared to a pure GPU implementation. Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications.

Chalcogenide optomemristors for multi-factor neuromorphic computation.

Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase the efficiency of basic neural operations. However, powerful mechanisms such as reinforcement learning and dendritic computation require more advanced device operations involving multiple interacting signals. Here we show that nano-scaled films of chalcogenide semiconductors can perform such multi-factor in-memory computation where their tunable electronic and optical properties are jointly exploited. We demonstrate that ultrathin photoactive cavities of Ge-doped Selenide can emulate synapses with three-factor neo-Hebbian plasticity and dendrites with shunting inhibition. We apply these properties to solve a maze game through on-device reinforcement learning, as well as to provide a single-neuron solution to linearly inseparable XOR implementation.

Phase-change memtransistive synapses for mixed-plasticity neural computations.

In the mammalian nervous system, various synaptic plasticity rules act, either individually or synergistically, over wide-ranging timescales to enable learning and memory formation. Hence, in neuromorphic computing platforms, there is a significant need for artificial synapses that can faithfully express such multi-timescale plasticity mechanisms. Although some plasticity rules have been emulated with elaborate complementary metal oxide semiconductor and memristive circuitry, device-level hardware realizations of long-term and short-term plasticity with tunable dynamics are lacking. Here we introduce a phase-change memtransistive synapse that leverages both the non-volatility of the phase configurations and the volatility of field-effect modulation for implementing tunable plasticities. We show that these mixed-plasticity synapses can enable plasticity rules such as short-term spike-timing-dependent plasticity that helps with the modelling of dynamic environments. Further, we demonstrate the efficacy of the memtransistive synapses in realizing accelerators for Hopfield neural networks for solving combinatorial optimization problems.

Neuromorphic computing with multi-memristive synapses.

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.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

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.

Withdrawal of voluntary inhibition unravels the off state of the spontaneous blink generator.

Involuntary movements such as spontaneous eye blinks can be successfully inhibited at will. Little do we know how the voluntary motor circuits countermand spontaneous blinks. Do the voluntary inhibitory commands act to pause or to turn off the endogenous blink generator, or does inhibition intersect and counter the generator׳s excitatory outputs? In theory, the time taken for the system to generate an after-inhibition blink will reflect onto the form of inhibition. For instance, if voluntary commands were to turn the blink generator off then the after-blink latency would be fixed to the inhibition offset and reflect the time it takes for the generator to rebound and turn on. In this study we measured the after-blink latency from the offset of voluntary inhibition. Volunteers inhibited their blinks in response to sound tones of randomly varying durations. At the offset volunteers withdrew the inhibition and relaxed. Interestingly, the spontaneous after-blinks were fixed to the offset of the inhibition as if the generator rebounded from an off state. The after-blink latency was not related to the duration of the inhibition, and inhibiting even for a small fraction of the mean inter-blink interval generated an after-blink time-locked to the inhibition offset. Interestingly, the insertion of voluntary blinks after inhibition further altered the blink generator by delaying the spontaneous after-blinks. We propose that the inhibition of spontaneous blinks at the level of the generator allows for highly effective voluntary countermanding. Nevertheless, the withdrawal of such inhibition was strongly associated with motor excitation.