Transformation Plasticity in Carbonitrided PM-Steels: Quantification of Plasticity Effects in Dependence of the Part Density*

To optimize heat treatment processes of case hardened components, heat treatment simulations are used to predict surface layer conditions. Only a precise knowledge and modelling of the transformation processes allows a trustworthy prediction of the hardness and residual stresses in the surface zone. The transformation plasticity mechanism plays an essential role in the heat treatment process and its correct simulation has a significant influence on the resulting calculated residual stress profiles and component distortion. Without considering transformation plasticity, simulative residual stresses are significantly overestimated [1]. In this work, powder metallurgical components are pressed and sintered and subsequently carbonitrided for a dilatometric investigation to characterize the correlation between transformation plasticity effect and the density. The results show a dependence of the austenite-martensite volume change that led to a significant difference of 0.5 Vol-%. A model describing the martensite volume change with respect to density is proposed. This also affects the description of the transformation plasticity constants (K) between K = 5 – 6 × 10–5 MPa–1 in dependence of density. With currently available data, the effect of chemical composition and density cannot be separated and quantified and further studies are therefore necessary to allow such a refinement.

Influence of stoichiometry on indentation-induced plasticity in CuZr glasses

Plasticity in metallic glasses depends on their stoichiometry. We explore this dependence by molecular dynamics simulations for the case of CuZr alloys using the compositions Cu$$_{64.5}$$ 64.5 Zr$$_{35.5}$$ 35.5 , Cu$$_{50}$$ 50 Zr$$_{50}$$ 50 , and Cu$$_{35.5}$$ 35.5 Zr$$_{64.5}$$ 64.5 . Plasticity is induced by nanoindentation and orthogonal cutting. Only the Cu$$_{64.5}$$ 64.5 Zr$$_{35.5}$$ 35.5 sample shows the formation of localized strain in the form of shear bands, while plasticity is more homogeneous for the other samples. This feature concurs with the high fraction of full icosahedral short-range order found for Cu$$_{64.5}$$ 64.5 Zr$$_{35.5}$$ 35.5 . In all samples, the atomic density is reduced in the plastic zone; this reduction is accompanied by a decrease of the average atom coordination, with the possible exception of Cu$$_{35.5}$$ 35.5 Zr$$_{64.5}$$ 64.5 , where coordination fluctuations are high. The strongest density reduction occurs in Cu$$_{64.5}$$ 64.5 Zr$$_{35.5}$$ 35.5 , where it is connected with the partial destruction of full icosahedral short-range order. The difference in plasticity mechanism influences the shape of the pileup and of the chip generated by nanoindentation and cutting, respectively.

Integration of sleep homeostasis and navigation in Drosophila

During sleep, the brain undergoes dynamic and structural changes. In Drosophila, such changes have been observed in the central complex, a brain area important for sleep control and navigation. The connectivity of the central complex raises the question about how navigation, and specifically the head direction system, can operate in the face of sleep related plasticity. To address this question, we develop a model that integrates sleep homeostasis and head direction. We show that by introducing plasticity, the head direction system can function in a stable way by balancing plasticity in connected circuits that encode sleep pressure. With increasing sleep pressure, the head direction system nevertheless becomes unstable and a sleep phase with a different plasticity mechanism is introduced to reset network connectivity. The proposed integration of sleep homeostasis and head direction circuits captures features of their neural dynamics observed in flies and mice.

The Perils of Navigating Activity-Dependent Alternative Splicing of Neurexins

Neurexins are presynaptic cell-adhesion molecules essential for synaptic function that are expressed in thousands of alternatively spliced isoforms. Recent studies suggested that alternative splicing at splice site 4 (SS4) of Nrxn1 is tightly regulated by an activity-dependent mechanism. Given that Nrxn1 alternative splicing at SS4 controls NMDA-receptor-mediated synaptic responses, activity-dependent SS4 alternative splicing would suggest a new synaptic plasticity mechanism. However, conflicting results confound the assessment of neurexin alternative splicing, prompting us to re-evaluate this issue. We find that in cortical cultures, membrane depolarization by elevated extracellular K+-concentrations produced an apparent shift in Nrxn1-SS4 alternative splicing by inducing neuronal but not astroglial cell death, resulting in persistent astroglial Nrxn1-SS4+ expression and decreased neuronal Nrxn1-SS4– expression. in vivo, systemic kainate-induced activation of neurons in the hippocampus produced no changes in Nrxn1-SS4 alternative splicing. Moreover, focal kainate injections into the mouse cerebellum induced small changes in Nrxn1-SS4 alternative splicing that, however, were associated with large decreases in Nrxn1 expression and widespread DNA damage. Our results suggest that although Nrxn1-SS4 alternative splicing may represent a mechanism of activity-dependent synaptic plasticity, common procedures for testing this hypothesis are prone to artifacts, and more sophisticated approaches will be necessary to test this important question.

Interneuron activity-structural plasticity association is driven by context-dependent sensory experience

Neuronal dendritic spine dynamics provide a plasticity mechanism for altering brain circuit connectivity to integrate new information for learning and memory. Previous in vivo studies in the olfactory bulb (OB) showed that regional increases in activity caused localized spine stability, at a population level, yet how activity affects spine dynamics at an individual neuron level remains unknown. In this study, we tracked in vivo the correlation between an individual neuron’s activity and its dendritic spine dynamics of OB granule cell (GC) interneurons. Odor experience caused a consistent correlation between individual GC activity and spine stability. Dissecting the components of the OB circuit showed that increased principal cell (MC) activity was sufficient to drive this correlation, whereas cell-autonomously driven GC activity had no effect. A mathematical model was able to replicate the GC activity-spine stability correlation and showed MC output having improved odor discriminability while retaining odor memory. These results reveal that GC spine plasticity provides a sufficient network mechanism to decorrelate odors and maintain a memory trace.

Integration of sleep homeostasis and navigation in Drosophila

ABSTRACTDuring sleep, the brain undergoes dynamic and structural changes. In Drosophila, such changes have been observed in the central complex, a brain area important for sleep control and navigation. The connectivity of the central complex raises the question about how navigation, and specifically the head direction system, can operate in the face of sleep related plasticity.To address this question, we develop a model that integrates sleep homeostasis and head direction. We show that by introducing plasticity, the head direction system can function in a stable way by balancing plasticity in connected circuits that encode sleep pressure. With increasing sleep pressure, the head direction system nevertheless becomes unstable and a sleep phase with a different plasticity mechanism is introduced to reset network connectivity.The proposed integration of sleep homeostasis and head direction circuits captures features of their neural dynamics observed in flies and mice.

Reinforcement Learning in Spiking Neural Networks with Stochastic and Deterministic Synapses

Though succeeding in solving various learning tasks, most existing reinforcement learning (RL) models have failed to take into account the complexity of synaptic plasticity in the neural system. Models implementing reinforcement learning with spiking neurons involve only a single plasticity mechanism. Here, we propose a neural realistic reinforcement learning model that coordinates the plasticities of two types of synapses: stochastic and deterministic. The plasticity of the stochastic synapse is achieved by the hedonistic rule through modulating the release probability of synaptic neurotransmitter, while the plasticity of the deterministic synapse is achieved by a variant of a reward-modulated spike-timing-dependent plasticity rule through modulating the synaptic strengths. We evaluate the proposed learning model on two benchmark tasks: learning a logic gate function and the 19-state random walk problem. Experimental results show that the coordination of diverse synaptic plasticities can make the RL model learn in a rapid and stable form.