23 research outputs found

    Characterization of melt-quenched and milled amorphous solids of gatifloxacin

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    <p>The objectives of this study were to characterize and investigate the differences in amorphous states of gatifloxacin. We prepared two types of gatifloxacin amorphous solids coded as M and MQ using milling and melt-quenching methods, respectively. The amorphous solids were characterized via X-ray diffraction (XRD), nonisothermal differential scanning calorimetry (DSC) and time-resolved near-infrared (NIR) spectroscopy. Both the solids displayed halo XRD patterns, the characteristic of amorphous solids; however, in the non-isothermal DSC profiles, these amorphous solids were distinguished by their crystallization and melting temperatures. The Kissinger–Akahira–Sunose plots of non-isothermal crystallization temperatures at various heating rates indicated a lower activation energy of crystallization for the amorphous solid M than that of MQ. These results support the differentiation between two amorphous states with different physical and chemical properties.</p

    A Spiking Neural Network Model of Model-Free Reinforcement Learning with High-Dimensional Sensory Input and Perceptual Ambiguity

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    <div><p>A theoretical framework of reinforcement learning plays an important role in understanding action selection in animals. Spiking neural networks provide a theoretically grounded means to test computational hypotheses on neurally plausible algorithms of reinforcement learning through numerical simulation. However, most of these models cannot handle observations which are noisy, or occurred in the past, even though these are inevitable and constraining features of learning in real environments. This class of problem is formally known as partially observable reinforcement learning (PORL) problems. It provides a generalization of reinforcement learning to partially observable domains. In addition, observations in the real world tend to be rich and high-dimensional. In this work, we use a spiking neural network model to approximate the free energy of a restricted Boltzmann machine and apply it to the solution of PORL problems with high-dimensional observations. Our spiking network model solves maze tasks with perceptually ambiguous high-dimensional observations without knowledge of the true environment. An extended model with working memory also solves history-dependent tasks. The way spiking neural networks handle PORL problems may provide a glimpse into the underlying laws of neural information processing which can only be discovered through such a top-down approach.</p></div

    The structures of the spiking neural networks.

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    <p>State neurons are used for the MDP task. Observation neurons are used for the PORL tasks instead of state neurons. Memory architecture (bounded by dashed line in the figure) is introduced only for the history-dependent PORL task.</p

    Digit center reaching task.

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    <p>(A) A set of digits used in the training. (B) The cumulative reward obtained with test dataset. (C, D) The activation of hidden neurons projected on the first two principal components in different reward settings. (C) The reward setting is the same as in the simple task. (D) The agent always gets reward of 2000 for any states and actions. Each point shows the hidden activation for each state using test digit dataset.</p

    Comparison between the SNN and the original RBM.

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    <p>Red colored symbols and lines indicate rightward actions, blue colored symbols and lines indicate leftward actions. (A) Spike counts of action neurons in the SNN (left) and the negative free-energy in the original RBM (right) for each state. Differences of spike counts (SNN) and negative free-energies (RBM) for action selection (middle). (B) Negative iFE of the SNN and negative free-energy of the equivalent RBM for certain state-action pairs. (C) Correlations between hidden neuron spike counts and the posterior over hidden nodes for each action (left) and when the weights are scaled (right, solid lines) and randomized (right, dotted lines). (D) Spike counts of hidden neurons in the SNN (top panel) and the posterior of the original RBM (middle and bottom panels).</p

    The XRD pattern corresponding to <i>foraminifera,</i> β-TCP and Zn-TCP showing peaks matching calcium carbonate (JCPDS 5-0586) and tricalcium phosphate (JCPDS 9-169).

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    <p>The XRD pattern corresponding to <i>foraminifera,</i> β-TCP and Zn-TCP showing peaks matching calcium carbonate (JCPDS 5-0586) and tricalcium phosphate (JCPDS 9-169).</p

    SEM images showing the morphological structure of Zn-TCP (a) before, (c) after and β-TCP (b) before, (d) after 4 week implantation in OVX mice.

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    <p>From the images there is no significant structural change to the surface morphology with the exception of tissue growth around the sample material.</p
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