Timing Dependent Plasticity Research Articles

Revealing the Plastic Mode of Time-Dependent Deformation of a LiTaO3 Single Crystal by Nanoindentation.

Recently, instrumental nanoindentation has been widely applied to detect time-dependent plastic deformation or creep behavior in numerous materials, particularly thin films and heterogeneous materials. However, deformation mechanism at nanoindentation holding stage has not been well revealed hitherto. In the current work, nanoindentation holding tests with high loads were performed on a brittle LiTaO3 single crystal. The surface morphologies of residual impressions with various holding times were investigated. It was indicated that generation of secondary cracks and propagation of both main and secondary cracks were the dominating mechanism for time-dependent plastic deformation at the initial holding stage, and the density and length of cracks were invariable at the steady-state holding stage, which suggested a nonlocalized plastic deformation beneath the indenter. It could be concluded that time-dependent plastic deformation of brittle ceramic under nanoindentation is composed of instant cracking as the continuation of loading sequence and homogeneous creep flow by high shear-compression stress at room temperature.

Modified resistive switching performance by increasing Al concentration in HfO2 on transparent indium tin oxide electrode

In this work, a detailed improvement in the resistive switching behavior of Al-doped HfAlO-based resistive random-access memory (RRAM) devices was studied by controlling the Al ratio during atomic layer deposition (ALD) on a transparent indium tin oxide (ITO) electrode. The deposition of Al-doped HfO2 at 280 °C demonstrated a significant reduction in In and Sn diffusion from the ITO electrode compared with the ALD of HfO2 when examined by transmission electron microscopy and energy-dispersive X-ray spectroscopy. X-ray photoelectron spectroscopy confirmed that the bandgap energy increased with increasing Al content, further reducing the leakage current and creating additional extrinsic defects. A higher concentration of trivalent Al into HfO2 intrinsically distributes oxygen vacancies (Vo) in a uniform manner, further controlling the gradual resistive switching in the RRAM device. Improved cycle-to-cycle variability with a maximum on/off ratio of ~44 was achieved with better retention properties of as much as 5 × 103 s. Multilevel conductance modulation under DC and pulse conditions was successfully demonstrated, together with synaptic properties of potentiation/depression and spike time-dependent plasticity, for Al-doped HfO2 RRAM devices.

Cold creep of titanium: Analysis of stress relaxation using synchrotron diffraction and crystal plasticity simulations

It is well known that titanium and some titanium alloys creep at ambient temperature, resulting in a significant fatigue life reduction when a stress dwell is included in the fatigue cycle. It is thought that localised time dependent plasticity in ‘soft’ grains oriented for easy plastic slip leads to load shedding and an increase in stress within a neighbouring ‘hard’ grain that is poorly oriented for easy slip. Quantifying this time dependent plasticity process is key to successfully predicting the complex cold dwell fatigue problem. In this work, synchrotron X-ray diffraction during stress relaxation experiments was performed to characterise the time dependent plastic behaviour of commercially pure titanium (grade 4). Lattice strains were measured by tracking the diffraction peak shifts from multiple plane families (21 diffraction rings) as a function of their orientation with respect to the loading direction. The critical resolved shear stress, activation energy and activation volume were established for both prismatic and basal slip modes by fitting a crystal plasticity finite element model to the lattice strain relaxation responses measured along the loading axis for three strong reflections. Prismatic slip was the easier mode having both a lower critical resolved shear stress (τcbasal = 252 MPa and τcprism = 154 MPa) and activation energy (ΔFbasal= 10.5×10−20J = 0.65 eV andΔFprism = 9.0×10−20J = 0.56 eV). The prism slip parameters correspond to a stronger strain rate sensitivity compared to basal slip. This slip system dependence on strain rate has a significant effect on stress redistribution to ‘hard’ grain orientations during cold dwell fatigue.

Investigation on the microstructural and mechanical properties of a Polytetrafluoroethylene thin film by radio frequency magnetron sputtering

Relying on magnetron sputtering technology, a Polytetrafluoroethylene (PTFE) thin film was deposited on silicon substrate. We systematically investigated the atomic bonding, growth mode, and surface morphology of the as-prepared PTFE thin films by different nominal deposition power with different thicknesses. The mechanical properties including elastic modulus, hardness, strain rate sensitivity and creep resistance were carefully detected in a thick (1270 nm) PTFE film by an instrumental nanoindentation. In comparison to its bulk counterpart, PTFE thin film owns much higher elastic modulus and stronger resistances to the plastic and time-dependent plastic deformation. The results of its chemistry and mechanical properties show the films are hydrophobic and of higher mechanical strength. The enhanced mechanical properties of PTFE thin film could be intrinsically tied to its complex fluorocarbon groups which generated from the breakage and re-organization of C-F chains during sputtering process. The creep resistance of PTFE thin film was approximately two times stronger than the bulk PTFE.

Time-Resolved Digital Image Correlation in the Scanning Electron Microscope for Analysis of Time-Dependent Mechanisms

Background: Advancements in the Digitial Image Correlation (DIC) technique over the past decade have greatly improved spatial resolution. However, many processes, such as plastic deformation, have a temporal component spanning from fractions of a second to minutes that has not yet been addressed in detail, particularly for DIC conducted in-situ in the scanning electron microscope (SEM). Objective: To develop a methodology for conducting time-resolved digital image correlation in the SEM for analysis of time-dependent mechanical deformation phenomena. Methods: Microscope and electron beam scanning parameters that influence the rate at which time-resolved DIC information is mapped are experimentally investigated, providing a guide for use over a range of timescales and resolutions. Results: Time-resolved DIC imaging is demonstrated on a Ti-7Al alloy, where slip band propagation is resolved with imaging dwell times of seconds. The limits of strain resolution and strain collection speeds are analyzed. Conclusions: The new developed methodology can be applied to a wide range of materials loaded in-situ to quantify time-dependent plastic deformation phenomena.

SBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network With On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edge

In this work, we propose stochastic Binary Spiking Neural Network (sBSNN) composed of stochastic spiking neurons and binary synapses (stochastic only during training) that computes probabilistically with one-bit precision for power-efficient and memory-compressed neuromorphic computing. We present an energy-efficient implementation of the proposed sBSNN using ‘stochastic bit’ as the core computational primitive to realize the stochastic neurons and synapses, which are fabricated in 90nm CMOS process, to achieve efficient on-chip training and inference for image recognition tasks. The measured data shows that the ‘stochastic bit’ can be programmed to mimic spiking neurons, and stochastic Spike Timing Dependent Plasticity (or sSTDP) rule for training the binary synaptic weights without expensive random number generators. Our results indicate that the proposed sBSNN realization offers possibility of up to $32\times $ neuronal and synaptic memory compression compared to full precision (32-bit) SNN and energy efficiency of 89.49 TOPS/Watt for two-layer fully-connected SNN.

Neurogranin, Encoded by the Schizophrenia Risk Gene NRGN, Bidirectionally Modulates Synaptic Plasticity via Calmodulin-Dependent Regulation of the Neuronal Phosphoproteome

BackgroundNeurogranin (Ng), encoded by the schizophrenia risk gene NRGN, is a calmodulin-binding protein enriched in the postsynaptic compartments, and its expression is reduced in the postmortem brains of patients with schizophrenia. Experience-dependent translation of Ng is critical for encoding contextual memory, and Ng regulates developmental plasticity in the primary visual cortex during the critical period. However, the overall impact of Ng on the neuronal signaling that regulates synaptic plasticity is unknown. MethodsAltered Ng expression was achieved via virus-mediated gene manipulation in mice. The effect on long-term potentiation (LTP) was accessed using spike timing–dependent plasticity protocols. Quantitative phosphoproteomics analyses led to discoveries in significant phosphorylated targets. An identified candidate was examined with high-throughput planar patch clamp and was validated with pharmacological manipulation. ResultsNg bidirectionally modulated LTP in the hippocampus. Decreasing Ng levels significantly affected the phosphorylation pattern of postsynaptic density proteins, including glutamate receptors, GTPases, kinases, RNA binding proteins, selective ion channels, and ionic transporters, some of which highlighted clusters of schizophrenia- and autism-related genes. Hypophosphorylation of NMDA receptor subunit Grin2A, one significant phosphorylated target, resulted in accelerated decay of NMDA receptor currents. Blocking protein phosphatase PP2B activity rescued the accelerated NMDA receptor current decay and the impairment of LTP mediated by Ng knockdown, implicating the requirement of synaptic PP2B activity for the deficits. ConclusionsAltered Ng levels affect the phosphorylation landscape of neuronal proteins. PP2B activity is required for mediating the deficit in synaptic plasticity caused by decreasing Ng levels, revealing a novel mechanistic link of a schizophrenia risk gene to cognitive deficits.

Emulating synaptic response in n- and p-channel MoS2 transistors by utilizing charge trapping dynamics

Brain-inspired, neuromorphic computing aims to address the growing computational complexity and power consumption in modern von-Neumann architectures. Progress in this area has been hindered due to the lack of hardware elements that can mimic neuronal/synaptic behavior which form the fundamental building blocks for spiking neural networks (SNNs). In this work, we leverage the short/long term memory effects due to the electron trapping events in an atomically thin channel transistor that mimic the exchange of neurotransmitters and emulate a synaptic response. Re-doped (n-type) and Nb-doped (p-type) molybdenum di-sulfide (MoS2) field-effect transistors are examined using pulsed-gate measurements, which identify the time scales of electron trapping/de-trapping. The devices demonstrate promising trends for short/long term plasticity in the order of ms/minutes, respectively. Interestingly, pulse paired facilitation (PPF), which quantifies the short-term plasticity, reveal time constants (τ1 = 27.4 ms, τ2 = 725 ms) that closely match those from a biological synapse. Potentiation and depression measurements describe the ability of the synaptic device to traverse several analog states, where at least 50 conductance values are accessed using consecutive pulses of equal height and width. Finally, we demonstrate devices, which can emulate a well-known learning rule, spike time-dependent plasticity (STDP) which codifies the temporal sequence of pre- and post-synaptic neuronal firing into corresponding synaptic weights. These synaptic devices present significant advantages over iontronic counterparts and are envisioned to create new directions in the development of hardware for neuromorphic computing.

Training a Multi-Layer Photonic Spiking Neural Network With Modified Supervised Learning Algorithm Based on Photonic STDP

We propose a framework for hardware architecture and learning algorithm co-design of multi-layer photonic spiking neural network (SNN). The vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) which contains two polarization-resolved modes is employed as a spiking neuron. The connection between two identical polarization modes is considered as the excitatory synapse, whereas the connection between two orthogonal polarization modes is regarded as the inhibitory synapse. The physical model of the photonic spiking neuron is derived based on the combination of spin-flip model and Yamada model. The photonic spike timing dependent plasticity (STDP) is applied to design a hardware-friendly biologically plausible supervised learning algorithm for a multi-layer photonic SNN. Thanks to the polarization mode competition effect in the VCSEL-SA, the proposed neuromorphic network is capable of solving the classical XOR problem. The effect of physical parameters of photonic neuron on the training convergence is also considered. We further extend the multi-layer photonic SNN to realize other logic learning tasks. To the best of our knowledge, such a modified supervised learning algorithm dedicated for a multi-layer photonic SNN has not yet been reported, which is interesting for spiking learning of neuromorphic photonics.

Time-dependent plasticity in silicon microbeams mediated by dislocation nucleation

Understanding deformation mechanisms in silicon is critical for reliable design of miniaturized devices operating at high temperatures. Bulk silicon is brittle, but it becomes ductile at about 540 °C. It creeps (deforms plastically with time) at high temperatures (∼800 °C). However, the effect of small size on ductility and creep of silicon remains elusive. Here, we report that silicon at small scales may deform plastically with time at lower temperatures (400 °C) above a threshold stress. We achieve this stress by bending single-crystal silicon microbeams using an in situ thermomechanical testing stage. Small size, together with bending, localize high stress near the surface of the beam close to the anchor. This localization offers flaw tolerance, allowing ductility to win over fracture. Our combined scanning, transmission electron microscopy, and atomic force microscopy analysis reveals that as the threshold stress is approached, multiple dislocation nucleation sites appear simultaneously from the high-stressed surface of the beam with a uniform spacing of about 200 nm between them. Dislocations then emanate from these sites with time, lowering the stress while bending the beam plastically. This process continues until the effective shear stress drops and dislocation activities stop. A simple mechanistic model is presented to relate dislocation nucleation with plasticity in silicon.

Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization.

Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing deep learning applications. In recent years, there have been several proposals focused on supervised (conversion, spike-based gradient descent) and unsupervised (spike timing dependent plasticity) training methods to improve the accuracy of SNNs on large-scale tasks. However, each of these methods suffer from scalability, latency, and accuracy limitations. In this paper, we propose novel algorithmic techniques of modifying the SNN configuration with backward residual connections, stochastic softmax, and hybrid artificial-and-spiking neuronal activations to improve the learning ability of the training methodologies to yield competitive accuracy, while, yielding large efficiency gains over their artificial counterparts. Note, artificial counterparts refer to conventional deep learning/artificial neural networks. Our techniques apply to VGG/Residual architectures, and are compatible with all forms of training methodologies. Our analysis reveals that the proposed solutions yield near state-of-the-art accuracy with significant energy-efficiency and reduced parameter overhead translating to hardware improvements on complex visual recognition tasks, such as, CIFAR10, Imagenet datatsets.

In-situ TEM study of dislocation interaction with twin boundary and retraction in twinned metallic nanowires

Metallic nanowires (NWs) with twin boundaries (TBs) running parallel to the NW length direction exhibit unusual plastic strain recovery owing to the interaction of dislocations with TBs. Here, based on in-situ transmission electron microscopy nanomechanical testing and molecular dynamics simulations, we report observation and quantification of dislocation nucleation, interaction with TBs, and retraction in bi-twinned Ag NWs with a single TB along the NW length direction. Our results show that leading partial dislocations nucleated from the free surface can be hindered by the TB, and upon unloading all or part of the leading partials can retract due to the repulsive force from the TB, leading to full or partial plastic strain recovery (Bauschinger effect), respectively. The bi-twinned Ag NWs can undergo stress relaxation, even at a stress below the yield strength, where the plastic strain also recovers upon unloading. The relaxation and recovery behaviors are compared to those of penta-twinned Ag NWs. Our results illustrate that the internal TBs in NWs can interact with surface-nucleated dislocations, leading to time-dependent plastic strain recovery and Bauschinger effect.

Multiplexing rhythmic information by spike timing dependent plasticity.

Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of information and others. Rhythmic activity in the brain has also been suggested to be used for multiplexing information. Multiplexing is the ability to transmit more than one signal via the same channel. Here we focus on frequency division multiplexing, in which different signals are transmitted in different frequency bands. Recent work showed that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competition between subgroups of correlated synaptic inputs. This competition between different rhythmicity channels, induced by STDP, may prevent the multiplexing of information. Thus, raising doubts whether STDP is consistent with the idea of multiplexing. This study explores whether STDP can facilitate the multiplexing of information across multiple frequency channels, and if so, under what conditions. We address this question in a modelling study, investigating the STDP dynamics of two populations synapsing downstream onto the same neuron in a feed-forward manner. Each population was assumed to exhibit rhythmic activity, albeit in a different frequency band. Our theory reveals that the winner-take-all like competitions between the two populations is limited, in the sense that different rhythmic populations will not necessarily fully suppress each other. Furthermore, we found that for a wide range of parameters, the network converged to a solution in which the downstream neuron responded to both rhythms. Yet, the synaptic weights themselves did not converge to a fixed point, rather remained dynamic. These findings imply that STDP can support the multiplexing of rhythmic information, and demonstrate how functionality (multiplexing of information) can be retained in the face of continuous remodeling of all the synaptic weights. The constraints on the types of STDP rules that can support multiplexing provide a natural test for our theory.

A semi-quantitative explanation of the cold dwell effect in titanium alloys

Cold dwell fatigue has become one critical issue in titanium industry over the past decades. Though some reports have emphasized the importance of time-dependent plastic strain on cold dwell effect in titanium alloys, the mathematical relationship between plastic strain and fatigue life has never been discussed because plastic strain changes continuously no matter in normal cyclic fatigue or dwell fatigue. In this paper, a series of fatigue tests under constant plastic strain is designed to investigate the mathematical relationship between plastic strain and fatigue life in titanium alloys at room temperature in air. Results show that plastic strain has a definitely detrimental effect on fatigue life in titanium alloys. Different from the traditional qualitative mechanisms, one semi-quantitative explanation for cold dwell effect in titanium alloys is proposed by introducing hysteresis energy density.

Non-REM Sleep Facilitates Consolidation of Contextual Fear Memory Through Temporal Coding Among Hippocampal Neurons

Sleep plays a critical role in memory consolidation, although the exact mechanisms mediating this process are unknown. Combining computational and in vivo experimental approaches, we test the hypothesis that reduced cholinergic input to the hippocampus during non-rapid eye movement (NREM) sleep generates stable spike timing relationships between neurons. We find that the order of firing among neurons during a period of NREM sleep reflects their relative firing rates during prior wake, and changes as a function of prior learning. We show that learning-dependent pattern formation (e.g. “replay”) in the hippocampus during NREM, together with spike timing dependent plasticity (STDP), restructures network activity in a manner similar to that observed in brain circuits across periods of sleep. This suggests that sleep actively promotes memory consolidation by switching the network from rate-based to firing phase-based information encoding.

N3-CPL: Neuroplasticity-based neuromorphic network cell proliferation learning

In general, Spiking Neural Networks (SNNs) have a network structure with special methods applied to neuron models and information transmission to mimic humans biologically. However, the existing SNN structures have two problems, such as fixed existing Artificial Neural Network structures and difficulty in learning due to lack of spike information during information transfer. Recently, many approaches of learning SNNs have been proposed in order to alleviate those two drawbacks. However, it is very difficult to overcome the drawbacks only by the learning method without the fundamental solution for the structure. In order to solve the problem of structure and learning method, we propose a novel flexible network construction method using neurogenesis-based cell proliferation concept and Triple Simultaneous- Spike Timing Dependent Plasticity (TS-STDP) which is improved learning method through neuroplasticity-based spike timing. We build the network flexibly and automatically by employing the concept that not only one neuron exists in the neural network, but also that various cells proliferate and transform from stem cells to function. In addition, TS-STDP is designed by considering the correlation of signal response among several neurons to solve the lack of information due to spike sparsity, which is a disadvantage of STDP. In the experimental section, we demonstrate and analyze our method using Mixed National Institute of Standards and Technology image data. Our method is 2.7× better in memory efficiency and 1.7× better in computational efficiency than the existing method. In particular, the research that automatically constructs the network structure is the first to my knowledge.

Microstructure-informed, predictive crystal plasticity finite element model of fatigue-dwells

Crystal plasticity finite element (CPFE) modelling is an effective tool from which detailed information on the meso-scale behaviour of crystalline metallic systems can be extracted and used, not only to enhance the understanding of material behaviour under different loading conditions, but also to improve the structural integrity assessment of engineering components. To be of full benefit however it must be demonstrated to not only predict the average global response of the material, but also the local behaviour, to provide insight into localised regions of stress and plastic strain. In this study, a slip system based constitutive model is developed to improve the simulation capability of time-independent and time-dependent plasticity. Comparison has been made between the macro-mechanical behaviour predicted by the model and previous experiments carried out at engineering length scale. Critically, the macro-mechanical behaviour predicted by the model has been examined against the behaviour of the materials at the meso-scale crystalline level measured by previous diffraction experiments. The robustness of the model is demonstrated on both the macro- and meso-scale through the successful prediction of macro-scale behaviour and lattice strain evolution under a variety of loading conditions. The model not only effectively recognised the influence of prior deformation on subsequent loading, but also complemented neutron diffraction data to enrich the understanding of the influence of an important loading condition on the deformation of grains within the material.

Digital Hardware Spiking Neuronal Network with STDP for Real-time Pattern Recognition

By mimicking or being inspired by the nervous system, neuromorphic systems are designed to realize robust and power-efficient information processing by highly parallel architecture. Spike Timing Dependent Plasticity (STDP) is a common learning method for Spiking Neural Networks (SNNs). Here, we present a real-time SNN with STDP implementation on Field Programmable Gate Array (FPGA) using digital spiking silicon neuron model. Equipped with Ethernet Interface, FPGA allows online configuration as well as real-time processing data input and output. We show that this hardware implementation can achieve real-time pattern recognition tasks and allows the connection between multi-SNNs to extend the scale of networks.

R-STDP Based Spiking Neural Network for Human Action Recognition

ABSTRACT Video surveillance systems are omnipresent and automatic monitoring of human activities is gaining importance in highly secured environments. The proposed work explores the use of the bio-inspired third generation neural network called spiking neural network (SNN) in order to recognize the action sequences present in a video. The SNN used in this work carries the neural information in terms of timing of spikes rather than the shape of the spikes. The learning technique used herein is reward-modulated spike time-dependent plasticity (R-STDP). It is based on reinforcement learning that modulates or demodulates the synaptic weights depending on the reward or the punishment signal that it receives from the decision layer. The absence of gradient descent techniques and external classifiers makes the system computationally efficient and simple. Finally, the performance of the network is evaluated on the two benchmark datasets, viz., Weizmann and KTH datasets.

Structural Plasticity and Molecular Markers in Hippocampus of Male Rats after Acute Stress

Stress plays a crucial role in the pathogenesis of psychiatric disorders and affects neuronal plasticity in different brain regions. We have previously found that acute foot-shock (FS) stress elicits fast and long-lasting functional and morphological remodeling of excitatory neurons in the prefrontal cortex (PFC), which were partly prevented by the pretreatment with antidepressants. Here we investigated, whether acute stress and pretreatment with desipramine (DMI) interfere in hippocampal dendritic remodeling. Male Sprague-Dawley rats were subjected to acute FS-stress, followed by measurement of time-dependent (1, 7 and 14 days) structural plasticity (dendritic arborization, spine number and morphology) in hippocampal CA1 pyramidal neurons and expression patterns of molecular markers implicated in neuronal plasticity. We found that acute stress significantly decreased spine number, dendritic length, and altered spine morphometric parameters at all time points evaluated after stress. This was paralleled by changes in the gene expression of Spinophilin and Cdc42, and protein expression of homer1. Pretreatment with DMI prevented the stress-induced dendritic atrophy and spine loss 14 days after acute FS. However, DMI treatment without stress differentially affected the expression patterns of spine-related genes and proteins. In conclusion, acute FS-stress and pretreatment with DMI significantly changed dendritic morphology, including number and morphology of spines, and the length of dendrites in hippocampal CA1 pyramidal cells as early as 1 day, and sustained up to 14 days after acute FS. The findings were paralleled by changes in gene and protein expression of actin binding and cytoskeletal proteins, Rho GTPases, and postsynaptic scaffolding proteins.