443 research outputs found

    Freedom through work: the psychosocial, affect and work

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    Ultrastructural features and synaptic connections of hilar ectopic granule cells in the rat dentate gyrus are different from those of granule cells in the granule cell layer.

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    Several investigators have shown the existence of dentate granule cells in ectopic locations within the hilus and molecular layer using both Golgi and retrograde tracing studies but the ultrastructural features and synaptic connections of ectopic granule cells were not previously examined. In the present study, the biocytin retrograde tracing technique was used to label ectopic granule cells following injections into stratum lucidum of CA3b of hippocampal slices obtained from epileptic rats. Electron microscopy was used to study hilar ectopic granule cells that were located 20-40 microm from the granule cell layer (GCL). They had ultrastructural features similar to those of granule cells in the GCL but showed differences, including nuclei that often displayed infoldings and thicker apical dendrites. At their origin, these dendrites were 6 microm in diameter and they tapered down to 2 microm at the border with the GCL. Both biocytin-labeled and unlabeled axon terminals formed exclusively asymmetric synapses with the somata and proximal dendrites of hilar ectopic granule cells. The mean number of axosomatic synapses for these cells was three times that for granule cells in the GCL. Together, these data indicate that hilar ectopic granule cells are postsynaptic to mossy fibers and have less inhibitory input on their somata and proximal dendrites than granule cells in the GCL. This finding is consistent with recent physiological results showing that hilar ectopic granule cells from epileptic rats are more hyperexcitable than granule cells in the GCL

    Statistical analysis driven optimized deep learning system for intrusion detection

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    Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018
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