A computational simulation of long-term synaptic potentiation inducing protocol processes with model of CA3 hippocampal microcircuit

An experimental study of computational model of the CA3 region presents cog­nitive and behavioural functions the hippocampus. The main property of the CA3 region is plastic recurrent connectivity, where the connections allow it to behave as an auto-associative memory. The computer simulations showed that CA3 model performs efficient long-term synaptic potentiation (LTP) induction and high rate of sub-millisecond coincidence detection. Average frequency of the CA3 pyramidal cells model was substantially higher in simulations with LTP induction protocol than without the LTP. The entropy of pyramidal cells with LTP seemed to be significantly higher than without LTP induction protocol (p = 0.0001). There was depression of entropy, which was caused by an increase of forgetting coefficient in pyramidal cells simulations without LTP (R = –0.88, p = 0.0008), whereas such correlation did not appear in LTP simulation (p = 0.4458). Our model of CA3 hippocampal formation microcircuit biologically inspired lets you understand neurophysiologic data. (Folia Morphol 2018; 77, 2: 210–220

Memory and forgetting processes with the firing neuron model

The aim of this paper is to present a novel algorithm for learning and forgetting within a very simplified, biologically derived model of the neuron, called firing cell (FC). FC includes the properties: (a) delay and decay of postsynaptic potentials, (b) modification of internal weights due to propagation of postsynaptic potentials through the dendrite, (c) modification of properties of the analog weight memory for each input due to a pattern of long-term synaptic potentiation. The FC model could be used in one of the three forms: excitatory, inhibitory, or receptory (gan­glion cell). The computer simulations showed that FC precisely performs the time integration and coincidence detection for incoming spike trains on all inputs. Any modification of the initial values (internal parameters) or inputs patterns caused the following changes of the interspike intervals time series on the output, even for the 10 s or 20 s real time course simulations. It is the basic evidence that the FC model has chaotic dynamical properties. The second goal is the presentation of various nonlinear methods for analysis of a biological time series. (Folia Morphol 2018; 77, 2: 221–233

Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites: implications for age interpretations

Meteorite impact processes are ubiquitous on the surfaces of rocky and icy bodies in the Solar System, including the Moon. One of the most common accessory minerals, zircon, when shocked, produces specific micro-structures that may become indicative of the age and shock conditions of these impact processes. To better understand the shock mechanisms in zircon from Apollo 15 and 16 impact breccias, we applied transmission electron microscopy (TEM) and studied nano-structures in eight lunar zircons displaying four different morphologies from breccias 15455, 67915, and 67955. Our observations revealed a range of shock-related features in zircon: (1) planar and non-planar fractures, (2) “columnar” zircon rims around baddeleyite cores, (3) granular textured zircon, in most cases with sub-µm-size inclusions of monoclinic ZrO2 (baddeleyite) and cubic ZrO2 (zirconia), (4) silica-rich glass and metal inclusions of FeS and FeNi present at triple junctions in granular zircon and in baddeleyite, (5) inclusions of rutile in shocked baddeleyite, (6) amorphous domains, (7) recrystallized domains. In many grain aggregates, shock-related micro-structures overprint each other, indicating either different stages of a single impact process or multiple impact events. During shock, some zircons were transformed to diaplectic glass (6), and others (7) were completely decomposed into SiO2 and Zr-oxide, evident from the observed round shapes of cubic zirconia and silica-rich glass filling triple junctions of zircon granules. Despite the highly variable effect on textures and Zr phases, shock-related features show no correlation with relatively homogeneous U–Pb or 207Pb/206Pb ages of zircons. Either the shock events occurred very soon after the solidification or recrystallization of the different Zr phases, or the shock events were too brief to result in noticeable Pb loss during shock metamorphism

Detection of rotor imbalance, including root cause, severity and location

This paper presents a new way of detecting imbalances on wind turbine rotors, by using a harmonic analysis of the rotor response in the fixed frame. The method is capable of distinguishing among different root causes of the imbalance. In addition, the imbalance severity and location, i.e. the affected blade, can be identified. The automatic classification of the imbalance problem is obtained by using a neural network. The performance of the method is illustrated with the help of different fault scenarios, within a high-fidelity simulation environment

Diffuse interstellar bands towards ο Per and ξ Per

Spectroscopic characteristics of interstellar matter vary strongly with the direction to the target star. Spectra of oPer and ξ Per contain many prominent and weak diffuse interstellar bands as well as absorption lines of some identiffed interstellar atoms and molecules. Using echelle optical spectra of these bright and moderately reddened stars we selected well detectable diffuse interstellar bands (DIBs). We measured intensities of all well noticeable absorption features to compare them with their counterparts observed in spectra of the other target stars

Data Predictive Control for Peak Power Reduction

Decisions on how best to optimize today\u27s energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs