In this paper, the rolling textures of six magnesium alloys containing different levels of zinc and rare earth (RE, e.g. mischmetal or Y) additions are examined. The overall texture strength and the ...basal pole intensity aligned with the sheet normal direction is lower for RE-containing alloys than for conventional alloys. The distinct textures generated in this study allow the influence of texture on the mechanical response to be investigated. The anisotropy of the yield and flow strengths is reversed and the planar anisotropy is reduced (
r
∼
1) in comparison to conventional alloys. Both aspects of the anisotropy are related to the fact that the dominant texture components in the Mg–Zn–RE alloys place more grains in favourable orientations for basal slip and tensile twinning, particularly during transverse direction tension. Mg sheets with lower
r-values should have improved forming behaviour, at least under straining conditions which call for thinning of the sheet.
For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance. Similarly, engineered systems are optimized for short time-to-solution ...and low energy-to-solution characteristics. At the level of neuronal implementation, this implies achieving the desired results with as few and as early spikes as possible. With time-to-first-spike coding, both of these goals are inherently emerging features of learning. Here, we describe a rigorous derivation of a learning rule for such first-spike times in networks of leaky integrate-and-fire neurons, relying solely on input and output spike times, and show how this mechanism can implement error backpropagation in hierarchical spiking networks. Furthermore, we emulate our framework on the BrainScaleS-2 neuromorphic system and demonstrate its capability of harnessing the system’s speed and energy characteristics. Finally, we examine how our approach generalizes to other neuromorphic platforms by studying how its performance is affected by typical distortive effects induced by neuromorphic substrates.Spiking neural networks promise fast and energy-efficient information processing. The ‘time-to-first-spike’ coding scheme, where the time elapsed before a neuron’s first spike is utilized as the main variable, is a particularly efficient approach and Göltz and Kriener et al. demonstrate that error backpropagation, an essential ingredient for learning in neural networks, can be implemented in this scheme.
The precise times of occurrence of individual pre- and postsynaptic action potentials are known to play a key role in the modification of synaptic efficacy. Based on stimulation protocols of two ...synaptically connected neurons, we infer an algorithm that reproduces the experimental data by modifying the probability of vesicle discharge as a function of the relative timing of spikes in the pre- and postsynaptic neurons. The primary feature of this algorithm is an asymmetry with respect to the direction of synaptic modification depending on whether the presynaptic spikes precede or follow the postsynaptic spike. Specifically, if the presynaptic spike occurs up to 50 ms before the postsynaptic spike, the probability of vesicle discharge is upregulated, while the probability of vesicle discharge is downregulated if the presynaptic spike occurs up to 50 ms after the postsynaptic spike. When neurons fire irregularly with Poisson spike trains at constant mean firing rates, the probability of vesicle discharge converges toward a characteristic value determined by the preand postsynaptic firing rates. On the other hand, if the mean rates of the Poisson spike trains slowly change with time, our algorithm predicts modifications in the probability of release that generalize Hebbian and Bienenstock-Cooper-Munro rules. We conclude that the proposed spike- based synaptic learning algorithm provides a general framework for regulating neurotransmitter release probability.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The sheet formability of current magnesium alloys at ambient temperatures is poor; however, the formability at moderately elevated temperatures can be excellent. Cylindrical cup drawing tests are ...used to compare the warm forming characteristics of conventional alloy AZ31B with alloys containing lithium or yttrium solid solutions. While both types of experimental alloy can have better room-temperature ductility (ef-25-30%) than AZ3 I B, only the lithium alloy has comparable or better deep-drawing capacity. The results are discussed in terms of the sheet anisotropy. Particular attention is drawn to the fact that magnesium alloys exhibit poor bending ductility due to their anisotropy and mechanical twinning-induced tension-compression strength asymmetry.
We report the characteristics of polymer/quantum dot solar cells fabricated using a water-soluble polymer and carbon nanotubes in a bulk heterojunction configuration. The water-soluble polythiophene ...polymer showed significant photoresponse and the potential for use in photovoltaics. The addition of carbon nanotubes to the polymer resulted in an order of magnitude increase in the photoconductivity. Improved charge separation and collection was evidenced by the large difference between light and dark conductivities as well as the increase in both open circuit voltage and short circuit current. Finally, photovoltaic cells using aligned nanotubes showed further improvement in the photoconductivity and IV characteristics.
Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of ...spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback-Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.
We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex ...stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Systematic temporal relations between single neuronal activities or population activities are ubiquitous in the brain. No experimental evidence, however, exists for a direct modification of neuronal ...delays during Hebbian-type stimulation protocols. We show that in fact an explicit delay adaptation is not needed if one assumes that the synaptic strengths are modified according to the recently observed temporally asymmetric learning rule with the downregulating branch dominating the upregulating branch. During development, slow, unbiased fluctuations in the transmission time, together with temporally correlated network activity, may control neural growth and implicitly induce drifts in the axonal delays and dendritic latencies. These delays and latencies become optimally tuned in the sense that the synaptic response tends to peak in the soma of the postsynaptic cell if this is most likely to fire. The nature of the selection process requires unreliable synapses in order to give successful synapses an evolutionary advantage over the others. The width of the learning function also determines the preferred dendritic delay and the preferred width of the postsynaptic response. Hence, it may implicitly determine whether a synaptic connection provides a precisely timed or a broadly tuned “contextual” signal.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Flight-related neck, shoulder and low back pain are the most common musculoskeletal disorders among helicopter pilots and their crewmembers, thus becoming a growing concern. Information on the ...combined prevalence of these types of pain and related risks are scarce. The aim of this study was therefore to estimate pain prevalence and to evaluate potential risk factors for neck pain among helicopter pilots and crewmembers within the armed forces, the airborne police and airborne rescue organizations in Austria.
Among a cohort of 104 helicopter pilots and 117 crewmembers (69.8% compliance), demographics, flying experience, use of Night Vision Goggles (NVG), helicopter type flown, prevalence and intensity of musculoskeletal symptoms (pain was defined as any reported pain experience, ache or discomfort) were collected by an online-based questionnaire.
For helicopter pilots the 12-month prevalence of neck pain was 67.3%, followed by low back (48.1%) and shoulder pain (43.3%). Among crewmembers, the 12-month pain prevalence were 45.3, 36.8 and 30.8% among the neck, lower back and shoulder, respectively. During this period, 41.8% of these helicopter pilots had experienced 8-30 pain days in the areas of neck (45.7%), shoulder (37.8%) and lower back (42.0%) whereas 47.8% of crewmembers self-reported 1-7 days of neck (54.7%), low back (44.2%) and shoulder (44.4%) pain in the previous year. The 3-month prevalence of neck pain was 64.4% followed by low back (42.3%) and shoulder pain (38.5%) for helicopter pilots. Among crewmembers, 41.9% suffered from neck, 29.9% from low back and 29.1% from shoulder pain the previous 3 months. Multivariate regression analysis revealed NVG use (OR 1.9, 95% CI, 1.06-3.50, p = 0.032), shoulder pain (OR 4.9, 95% CI, 2.48-9.55, p < 0.001) and low back pain (OR 2.3, 95% CI, 1.21-4.31, p = 0.011) to be significantly associated with neck pain.
The 12- and 3-month prevalence of neck, shoulder and low back is considerably high among both, helicopter pilots and crewmembers confirming the existence of this growing concern. The use of NVG devices, shoulder and low back pain in the previous 12 months represent independent risk factors for neck pain. These findings highlight the need for longitudinal studies.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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