Three-dimensional motor neuron morphology estimation in the Drosophila ventral nerve cord

Type-specific dendritic arborization patterns dictate synaptic connectivity and are fundamental determinants of neuronal function. We exploit the morphological stereotypy and relative simplicity of the Drosophila nervous system to model the diverse neuronal morphologies of individual motor neurons (MNs) and understand underlying principles of synaptic connectivity in a motor circuit. Our computational approach aims at the reconstruction of the neuron morphology, namely the robust segmentation of the neuron volumes from their surroundings with the simultaneous partitioning into their compartments, namely the soma, axon, and dendrites. We use the idea of cosegmentation, where every image along the z -axis (depth) is segmented using information from "neighboring" depths. We use 3-D Haar-like features to model appearance. Because soma and axon are determined by their distinctive shapes, we define an implicit shape representation of the 2-D segmentation sets to drive cosegmentation and achieve the desired partitioning. We validate our method using image stacks depicting single neurons labeled with green fluorescent protein (GFP) and serially imaged with laser scanning confocal microscopy.     

An analog neural computer with modular architecture for real-timedynamic computations

A multichip analog parallel neural network whose architecture, neuron characteristics, synaptic connections, and time constants are modifiable is described. The system has several important features, such as time constants for time-domain computations, interchangeable chips allowing a modifiable gross architecture, and expandability to any arbitrary size. Such an approach allows the exploration of different network architectures for a wide range of applications, in particular dynamic real-world computations. Four different modules (neuron, synapse, time constant, and switch units) have been designed and fabricated in a 2-μm CMOS technology. About 100 of these modules have been assembled in a fully functional prototype neural computer. An integrated software package for setting the network configuration and characteristics, and monitoring the neuron outputs has been developed as well. The performance of the individual modules as well as the overall system response for several applications was tested successfully. Results of a network for real-time decomposition of acoustical patterns are discussed     

Optimal discrimination and classification of neuronal actionpotential waveforms from multiunit, multichannel recordings usingsoftware-based linear filters

Describes advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. The programs are capable of detecting and classifying the spikes from multiple, simultaneously active neurons, even in situations where there is a high degree of spike waveform superposition on the recording channels. The protocols are based on the derivation of an optimal linear filter for each individual neuron. Each filter is tuned to selectively respond to the spike waveform generated by the corresponding neuron, and to attenuate noise and the spike waveforms from all other neurons. The protocol is essentially an extension of earlier work (S. Andreassen et al., 1979; W.M. Roberts and D.K. Hartline, 1975; R.B. Stein et al., 1979). However, the protocols extend the power and utility of the original implementations in two significant respects. First, a general single-pass automatic template estimation algorithm was derived and implemented. Second, the filters were implemented within a software environment providing a greatly enhanced functional organization and user interface. The utility of the analysis approach was demonstrated on samples of multiunit electrophysiological recordings from the cricket abdominal nerve cord     

Transparent and Flexible Inorganic Perovskite Photonic Artificial Synapses with Dual-Mode Operation

With the rapid development of artificial intelligence, the simulation of the human brain for neuromorphic computing has demonstrated unprecedented progress. Photonic artificial synapses are strongly desirable owing to their higher neuron selectivity, lower crosstalk, wavelength multiplexing capabilities, and low operating power compared to their electric counterparts. This study demonstrates a highly transparent and flexible artificial synapse with a two-terminal architecture that emulates photonic synaptic functionalities. This optically triggered artificial synapse exhibits clear synaptic characteristics such as paired-pulse facilitation, short/long-term memory, and synaptic behavior analogous to that of the iris in the human eye. Ultraviolet light illumination-induced neuromorphic characteristics exhibited by the synapse are attributed to carrier trapping and detrapping in the SnO₂ nanoparticles and CsPbCl₃ perovskite interface. Moreover, the ability to detect deep red light without changes in synaptic behavior indicates the potential for dual-mode operation. This study establishes a novel two-terminal architecture for highly transparent and flexible photonic artificial synapse that can help facilitate higher integration density of transparent 3D stacking memristors, and make it possible to approach optical learning, memory, computing, and visual recognition.     

Integrated multiscale modeling of the nervous system: predicting changes in hippocampal network activity by a positive AMPA receptor modulator

One of the fundamental characteristics of the brain is its hierarchical organization. Scales in both space and time that must be considered when integrating across hierarchies of the nervous system are sufficiently great as to have impeded the development of routine multilevel modeling methodologies. Complex molecular interactions at the level of receptors and channels regulate activity at the level of neurons; interactions between multiple populations of neurons ultimately give rise to complex neural systems function and behavior. This spatial complexity takes place in the context of a composite temporal integration of multiple, different events unfolding at the millisecond, second, minute, hour, and longer time scales. In this study, we present a multiscale modeling methodology that integrates synaptic models into single neuron, and multineuron, network models. We have applied this approach to the specific problem of how changes at the level of kinetic parameters of a receptor-channel model are translated into changes in the temporal firing pattern of a single neuron, and ultimately, changes in the spatiotemporal activity of a network of neurons. These results demonstrate how this powerful methodology can be applied to understand the effects of a given local process within multiple hierarchical levels of the nervous system.     

Monolithic 3D neuromorphic computing system with hybrid CMOS and memristor-based synapses and neurons

Because of fabrication compatibility to current semiconductor technology, three-dimensional integrated circuits (3D-ICs) offer promising near-term solutions for maintaining Moore’s Law. 3D-ICs proffer high system speeds, massively parallel processing, low power consumption, and their high densities result in small footprints. In this paper, a novel 3D neuromorphic IC architecture which combines monolithic 3D integration and a synaptic array based on vertical resistive random-access memory structure (V-RRAM) is proposed. To analyze the electrical characteristics of the proposed synaptic array, a concise equivalent circuit model of the system is developed, and analytical calculations for each parameter of the equivalent circuit are provided. Moreover, a novel signal intensity encoding neuron design that can directly convert analog signal into a spiking waveform sequence is proposed and analyzed. A feasible 3D neuromorphic computing architecture is demonstrated. Applying the monolithic 3D integration technology on neuromorphic computing system hardware implementation can reduce the power consumption by 50%, and shrink die areas by 35%.     

An asynchronous architecture for modeling intersegmental neural communication

This paper presents an asynchronous VLSI architecture for modeling the oscillatory patterns seen in segmented biological systems. The architecture emulates the intersegmental synaptic connectivity observed in these biological systems. The communications network uses address-event representation (AER), a common neuromorphic protocol for data transmission. The asynchronous circuits are synthesized using communicating hardware processes (CHP) procedures. The architecture is scalable, supports multichip communication, and operates independent of the type of silicon neuron (spiking or burst envelopes). A 16-segment prototype system was developed, tested, and implemented; data from this system are presented.     

Structuro-functional considerations of the cerebellar neuron network

The neuronal network of the cerebellar cortex is discussed with strong emphasis upon its structure, but taking into consideration the recently revealed functional properties of its various neuronal elements. From the viewpoint of the experimenter this neuron network has the unique advantage of being arranged in the form of a highly regular rectangular lattice and being built up of relatively few well-known structural elements. After consideration of certain structural features of the granular layer (the receiving area of one of the two main lines of input) which suggest some important preprocessing of the arriving information, the mode of transmission of this excitation to the main part of the cortex is discussed. A functional model of the operation of the main neuron network is then proposed, based on the known geometry and functional properties of the elements involved. Some additional speculations are made about the possible functional significance of the most unusual structural feature of the cerebellar cortex: the complete separation in space (compartmentalization) of the Purkinje cell dendritic trees.     

Molecular Imaging in the Second Near‐Infrared Window

In the past decade, noticeable progress has been achieved regarding fluorescence imaging in the second near‐infrared (NIR‐II) window. Fluorescence imaging in the NIR‐II window demonstrates superiorities of deep tissue penetration and high spatial and temporal resolution, which are beneficial for profiling physiological processes. Meanwhile, molecular imaging has emerged as an efficient tool to decipher biological activities on the molecular and cellular level. Extending molecular imaging into the NIR‐II window would enhance the imaging performance, providing more detailed and accurate information of the biological system. In this progress report, selected achievements made in NIR‐II molecular imaging are summarized. The organization of this report is based on strategies underlying rational designs of NIR‐II imaging probes, and their applications in molecular imaging are highlighted. This progress report may provide guidance and reference for further development of functional NIR‐II probes designed for high‐performance molecular imaging.     

Evaluation of the Biocompatibility of PLACL/Collagen Nanostructured Matrices with Cardiomyocytes as a Model for the Regeneration of Infarcted Myocardium

Pioneering research suggests various modes of cellular therapeutics and biomaterial strategies for myocardial tissue engineering. Despite several advantages, such as safety and improved function, the dynamic myocardial microenvironment prevents peripherally or locally administered therapeutic cells from homing and integrating of biomaterial constructs with the infarcted heart. The myocardial microenvironment is highly sensitive due to the nanoscale cues that it exerts to control bioactivities, such as cell migration, proliferation, differentiation, and angiogenesis. Nanoscale control of cardiac function has not been extensively analyzed in the field of myocardial tissue engineering. Inspired by microscopic analysis of the ventricular organization in native tissue, a scalable in‐vitro model of nanoscale poly(L ‐lactic acid)‐co ‐poly(? ‐caprolactone)/collagen biocomposite scaffold is fabricated, with nanofibers in the order of 594 ± 56 nm to mimic the native myocardial environment for freshly isolated cardiomyocytes from rabbit heart, and the specifically underlying extracellular matrix architecture: this is done to address the specificity of the underlying matrix in overcoming challenges faced by cellular therapeutics. Guided by nanoscale mechanical cues provided by the underlying random nanofibrous scaffold, the tissue constructs display anisotropic rearrangement of cells, characteristic of the native cardiac tissue. Surprisingly, cell morphology, growth, and expression of an interactive healthy cardiac cell population are exquisitely sensitive to differences in the composition of nanoscale scaffolds. It is shown that suitable cell–material interactions on the nanoscale can stipulate organization on the tissue level and yield novel insights into cell therapeutic science, while providing materials for tissue regeneration.     

A system for MEA-based multisite stimulation

The capability for multisite stimulation is one of the biggest potential advantages of microelectrode arrays (MEAs). There remain, however, several technical problems which have hindered the development of a practical stimulation system. An important design goal is to allow programmable multisite stimulation, which produces minimal interference with simultaneous extracellular and patch or whole cell clamp recording. Here, we describe a multisite stimulation and recording system with novel interface circuit modules, in which preamplifiers and transistor transistor logic-driven solid-state switching devices are integrated. This integration permits PC-controlled remote switching of each substrate electrode. This allows not only flexible selection of stimulation sites, but also rapid switching of the selected sites between stimulation and recording, within 1.2 ms. This allowed almost continuous monitoring of extracellular signals at all the substrate-embedded electrodes, including those used for stimulation. In addition, the vibration-free solid-state switching made it possible to record whole-cell synaptic currents in one neuron, evoked from multiple sites in the network. We have used this system to visualize spatial propagation patterns of evoked responses in cultured networks of cortical neurons. This MEA-based stimulation system is a useful tool for studying neuronal signal processing in biological neuronal networks, as well as the process of synaptic integration within single neurons.     

Analogue VLSI Leaky Integrate-and-Fire Neurons and Their Use in a Sound Analysis System

Integrate-and-fire neurons are simple model neurons which can handle continuously time-varying signals. We have applied them to problems in real-time analysis of sounds. Two different chips have been built: the first had a fixed network architecture with all synaptic weights identical, and the second is reconfigurable with individually programmable weights. We present results characterising the latter chip, and results from processing real data from the earlier chip. We note that the second chip provides a more general integrate-and-fire neuron implementation.     

激光陷阱引导神经细胞生长方向的研究

激光陷阱技术是近年来发展起来的一种非接触的操纵技术,它在生命科学领域取得了许多开创性成果,作者用特别设计的倒置式激光陷阱放在生长锥的前方来引导生长锥的生长方向,通过对生长锥施以持续的作用力,作者在实验上成功地引导了神经细胞生长锥的生长方向,并讨论了激光陷阱引导神经细胞生长的分子生物学基础。对这种新的引导神经细胞定向生长的方法的研究,可能对神经轴突的定向生长机制、控制神经再生产生非常积极的影响。     

Bursting Hodgkin–Huxley model-based ultra-low-power neuromimetic silicon neuron

This paper presents a compact, ultra-low-power implementation of the bursting Hodgkin?CHuxley model-based silicon neuron. The Hodgkin?CHuxley model is a neuron imitation that consists of two calcium current channels, a potassium current channel and a leakage current channel. In the proposed architecture, the calcium and the potassium current channels have been implemented using a sigmoid-function structure, a log-domain filter, and a linear transconductor. Different neuronal signals can be generated by changing the value of the capacitor in the log-domain filter. The proposed silicon neuron is capable of generating four different outputs, namely, spiking, spiking with latency, bursting, and chaotic signals. Ultra-low-power consumption is achieved by current-reuse technique and subthreshold region operation of MOSFETs. The circuit is designed using 0.13???m standard CMOS process. The entire design uses 43 transistors, with a total power consumption of only 43?nW.     

Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing

Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic com-puting.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switching(TS)device with low operation voltage,large on/off ratio and high uniformity is presented.Measurement results indicate that this neuron demon-strates self-oscillation behavior under applied voltages as low as 1 V.The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance.It can then be used to evaluate the resistive random-access memory(RRAM)synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar ar-ray for neuromorphic computing.Meanwhile,simulation results show that a large RRAM crossbar array(＞128×128)can be sup-ported by our oscillation neuron owing to the high on/off ratio(＞108)of Ag NDs TS device.Moreover,the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy(＜1％).Therefore,the developed oscillation neuron based on the Ag NDs TS device shows great poten-tial for future neuromorphic computing applications.     

A Special Purpose Array Processor Architecture for the Molecular Dynamics Simulation of Point-Mutated Proteins

Point mutation of amino acids is a means used by biotechnologists to improve the performance of proteins. To study a point-mutated polypeptide, one requires its global minimum energy conformation. This conformation can be determined by molecular dynamics via Langevin's equations of motion. Molecular dynamics simulations belong to the most difficult problems to parallelize in a scalable manner. We provide a method for defining a special purpose 3D array processor architecture for the molecular dynamics simulation of point-mutated polypeptides. The architecture is derived from a spatial decomposition of a known conformation of the point-mutated polypeptide or the native conformation of the given protein. By using an approximation scheme for the deterministic forces, the interprocessor communication can be kept local. The architecture affords a simple distributed load balancer and is scalable. The computational workload of the array processor architecture to perform molecular dynamics simulations under realistic conditions is addressed. An example architecture is given by point-mutated penicillin amidase.     

A Flexible Mott Synaptic Transistor for Nociceptor Simulation and Neuromorphic Computing

Designing transparent flexible electronics with multi-biological neuronal functions and superior flexibility is a key step to establish wearable artificial intelligence equipment. Here, a flexible ionic gel-gated VO₂ Mott transistor is developed to simulate the functions of the biological synapse. Short-term and long-term plasticity of the synapse are realized by the volatile electrostatic carrier accumulation and nonvolatile proton-doping modulation, respectively. With the achievement of multi-essential synaptic functions, an important sensory neuron, nociceptor, is perfectly simulated in our synaptic transistors with all key characteristics of threshold, relaxation, and sensitization. More importantly, this synaptic transistor exhibits high tolerance to the bending deformation, and the cycle-to-cycle variations of multi-conductance states in potentiation and depression properties are maintained within 4%. This superior stability further indicates that our flexible device is suitable for neuromorphic computing. Simulation results demonstrate that high recognition accuracy of handwritten digits (>95%) can be achieved in a convolution neural network built from these synaptic transistors. The transparent and flexible Mott transistor based on electrically-controlled VO₂ metal-insulator transition is believed to open up alternative approaches to developing highly stable synapses for future flexible neuromorphic systems.     

指数突触电导IF神经元模型 及事件驱动模拟策略

提出了一种新的可进行精确模拟的指数突触电导Integrate-and-Fire(IF)神经元模型,通过单脉冲激励的突触后电位和多脉冲激励的自发放电统计分析,发现该模型的脉冲反应动态特性与指数突触电导被动膜方程模型接近,而计算效率接近脉冲耦合漏电IF模型.同时构建了指数突触电导IF神经元模型的事件驱动模拟策略,并分别应用事件驱动和时钟驱动模拟策略模拟了基于动态突触的随机网络,结果表明:(1)在事件驱动模拟策略中,模拟时间和总的脉冲事件数线性成比例;(2)在不同的模拟策略中,脉冲事件的时间精度会影响网络的神经动态特性.     

3D‐Printed Soft Magnetoelectric Microswimmers for Delivery and Differentiation of Neuron‐Like Cells

Neurodegenerative diseases generally result in irreversible neuronal damage and neuronal death. Cell therapy shows promise as a potential treatment for these diseases. However, the therapeutic targeted delivery of these cells and the in situ provision of a suitable microenvironment for their differentiation into functional neuronal networks remain challenging. A highly integrated multifunctional soft helical microswimmer featuring targeted neuronal cell delivery, on‐demand localized wireless neuronal electrostimulation, and post‐delivery enzymatic degradation is introduced. The helical soft body of the microswimmer is fabricated by two‐photon lithography of the photocurable gelatin–methacryloyl (GelMA)‐based hydrogel. The helical body is then impregnated with composite multiferroic nanoparticles displaying magnetoelectric features (MENPs). While the soft GelMA hydrogel chassis supports the cell growth, and is degraded by enzymes secreted by cells, the MENPs allow for the magnetic transportation of the bioactive chassis, and act as magnetically mediated electrostimulators of neuron‐like cells. The unique combination of the materials makes these microswimmers highly integrated devices that fulfill several requirements for their future translation to clinical applications, such as cargo delivery, cell stimulation, and biodegradability. The authors envision that these devices will inspire new avenues for targeted cell therapies for traumatic injuries and diseases in the central nervous system.     

High-voltage electron microscopy in neurocytology

The function of the living matter from biomolecules to the whole body of the organism is based on the structure. Consequently, structural information is essential for the understanding of the function of living organisms. The biological structures from biomolecules to cells and tissues are intimately related to each other, and changing their morphological, biochemical, and physiological properties dynamically according to the developmental and functional status of the organism. The molecular dynamics of living matter should be related to the function of whole body. We employed HVEM stereoscopy and morphometry for the purpose of combining the structural information at nanometer level with those of the micrometer level. Novel aspects of three-dimensional organization of neuronal and glial cell processes have been presented. This structural information together with morphometrical data could contribute to the elucidation of brain function.