Download Plasticity __LINK_

In physics and materials science, plasticity (also known as plastic deformation) is the ability of a solid material to undergo permanent deformation, a non-reversible change of shape in response to applied forces.[1][2] For example, a solid piece of metal being bent or pounded into a new shape displays plasticity as permanent changes occur within the material itself. In engineering, the transition from elastic behavior to plastic behavior is known as yielding.

Plastic deformation is observed in most materials, particularly metals, soils, rocks, concrete, and foams.[3][4][5][6] However, the physical mechanisms that cause plastic deformation can vary widely. At a crystalline scale, plasticity in metals is usually a consequence of dislocations. Such defects are relatively rare in most crystalline materials, but are numerous in some and part of their crystal structure; in such cases, plastic crystallinity can result. In brittle materials such as rock, concrete and bone, plasticity is caused predominantly by slip at microcracks. In cellular materials such as liquid foams or biological tissues, plasticity is mainly a consequence of bubble or cell rearrangements, notably T1 processes.

Download Plasticity

Download 🔥 https://urluso.com/2y7O6U 🔥

Perfect plasticity is a property of materials to undergo irreversible deformation without any increase in stresses or loads. Plastic materials that have been hardened by prior deformation, such as cold forming, may need increasingly higher stresses to deform further. Generally, plastic deformation is also dependent on the deformation speed, i.e. higher stresses usually have to be applied to increase the rate of deformation. Such materials are said to deform visco-plastically.

Most metals show more plasticity when hot than when cold. Lead shows sufficient plasticity at room temperature, while cast iron does not possess sufficient plasticity for any forging operation even when hot. This property is of importance in forming, shaping and extruding operations on metals. Most metals are rendered plastic by heating and hence shaped hot.

On the nanoscale the primary plastic deformation in simple face-centered cubic metals is reversible, as long as there is no material transport in form of cross-slip.[7] Shape-memory alloys such as Nitinol wire also exhibit a reversible form of plasticity which is more properly called pseudoelasticity.

The presence of other defects within a crystal may entangle dislocations or otherwise prevent them from gliding. When this happens, plasticity is localized to particular regions in the material. For crystals, these regions of localized plasticity are called shear bands.

Soils, particularly clays, display a significant amount of inelasticity under load. The causes of plasticity in soils can be quite complex and are strongly dependent on the microstructure, chemical composition, and water content. Plastic behavior in soils is caused primarily by the rearrangement of clusters of adjacent grains.

There are several mathematical descriptions of plasticity.[12] One is deformation theory (see e.g. Hooke's law) where the Cauchy stress tensor (of order d-1 in d dimensions) is a function of the strain tensor. Although this description is accurate when a small part of matter is subjected to increasing loading (such as strain loading), this theory cannot account for irreversibility.

In 1934, Egon Orowan, Michael Polanyi and Geoffrey Ingram Taylor, roughly simultaneously, realized that the plastic deformation of ductile materials could be explained in terms of the theory of dislocations. The mathematical theory of plasticity, flow plasticity theory, uses a set of non-linear, non-integrable equations to describe the set of changes on strain and stress with respect to a previous state and a small increase of deformation.

Rationale: Recent evidence highlights the importance of optimal lung development during childhood for health throughout life. Objectives: To explore the plasticity of individual lung function states during childhood. Methods: Prebronchodilator FEV1 z-scores determined at age 8, 16, and 24 years in the Swedish population-based birth cohort BAMSE (Swedish abbreviation for Child [Barn], Allergy, Milieu, Stockholm, Epidemiological study) (N = 3,069) were used. An unbiased, data-driven dependent mixture model was applied to explore lung function states and individual state chains. Lung function catch-up was defined as participants moving from low or very low states to normal or high or very high states, and growth failure as moving from normal or high or very high states to low or very low states. At 24 years, we compared respiratory symptoms, small airway function (multiple-breath washout), and circulating inflammatory protein levels, by using proteomics, across states. Models were replicated in the independent Dutch population-based PIAMA (Prevention and Incidence of Asthma and Mite Allergy) cohort. Measurements and Main Results: Five lung function states were identified in BAMSE. Lung function catch-up and growth failure were observed in 74 (14.5%) BAMSE participants with low or very low states and 36 (2.4%) participants with normal or high or very high states, respectively. The occurrence of catch-up and growth failure was replicated in PIAMA. Early-life risk factors were cumulatively associated with the very low state, as well as with catch-up (inverse association) and growth failure. The very low state as well as growth failure were associated with respiratory symptoms, airflow limitation, and small airway dysfunction at adulthood. Proteomics identified IL-6 and CXCL10 (C-X-C motif chemokine 10) as potential biomarkers of impaired lung function development. Conclusions: Individual lung function states during childhood are plastic, including catch-up and growth failure.

Our paper identifies the principle of prospective configuration, according to which learning relies on neurons first optimizing their pattern of activity to match the correct output and then reinforcing these prospective activities through synaptic plasticity. Although it was known that in energy-based networks the activity of neurons shifts before weight update, it has been previously thought that this shift is a necessary cost of error propagation in biological networks, and several methods have been proposed to suppress it11,12,14,20,21 to approximate backpropagation more closely. By contrast, we demonstrate that this reconfiguration of neural activity is the key to achieving learning performance superior to that of backpropagation and to explaining experimental data from diverse learning tasks. Prospective configuration further offers a range of experimental predictions distinct from those of backpropagation (Supplementary Figs. 11 and 12). Together, we have demonstrated that prospective configuration enables more efficient learning than backpropagation by reducing interference, demonstrates superior performance in situations faced by biological organisms, requires only local computation and plasticity and matches experimental data across a wide range of tasks.

Our theory addresses a long-standing question of how the brain solves the plasticity-stability dilemma, for example, how it is possible that, despite adjustment of representation in the primary visual cortex during learning43, we can still understand the meaning of visual stimuli we learned over our lifetime. According to prospective configuration, when some weights are modified, compensatory changes are made to other weights to ensure the stability of correctly predicted outputs. Thus, prospective configuration reduces interference between different weight modifications while learning a single association. Previous computational models have proposed mechanisms that reduce interference between new and previously acquired information while learning multiple associations34,44. It is highly likely that such mechanisms and prospective configuration operate in the brain in parallel to minimize both types of interference.

This will also result in an equation that can be implemented with local plasticity because it is just a gradient descent on the local energy. We refer to such an equation as weight dynamics, because it describes the dynamics of the weights in energy-based networks.

As shown in Fig. 5, we trained a network that included two input neurons, two hidden neurons and two output neurons. The two input neurons were one-to-one connected to the two hidden neurons, and the two hidden neurons were fully connected to the two output neurons. The two input neurons were considered to encode presenting the blue and red background, respectively. The two output neurons were considered to encode the prediction of the perturbations toward positive and negative directions, respectively. Presenting and not presenting a background color were encoded 1 and 0, respectively; presenting and not presenting perturbations of a particular direction were encoded 1 and 0, respectively. The weights were initialized from a normal distribution with mean 0 and an s.d. fitted to the behavioral data (see below), simulating that the participants had not built any associations before the experiments. Learning rates were independent for the two layers, as we expected the connections from perception to belief and from belief to predictions to have different degrees of plasticity. The two learning rates were also fitted to the data (see below).

Spike timing dependent plasticity (STDP) is a learning rule that modifies synaptic strength as a function of the relative timing of pre- and postsynaptic spikes. When a neuron is repeatedly presented with similar inputs, STDP is known to have the effect of concentrating high synaptic weights on afferents that systematically fire early, while postsynaptic spike latencies decrease. Here we use this learning rule in an asynchronous feedforward spiking neural network that mimics the ventral visual pathway and shows that when the network is presented with natural images, selectivity to intermediate-complexity visual features emerges. Those features, which correspond to prototypical patterns that are both salient and consistently present in the images, are highly informative and enable robust object recognition, as demonstrated on various classification tasks. Taken together, these results show that temporal codes may be a key to understanding the phenomenal processing speed achieved by the visual system and that STDP can lead to fast and selective responses. 006ab0faaa