Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~1011 neuron based) large neural networks.

Copper is a trace element essential for almost all living organisms. But the level of intracellular copper needs to be tightly regulated. Dysregulation of cellular copper homeostasis leading to various diseases demonstrates the importance of this tight regulation. Copper homeostasis is regulated not only within the cell but also within individual intracellular compartments. Inactivation of export machinery results in excess copper being redistributed into various intracellular organelles. Recent evidence suggests the involvement of glutathione in playing an important role in regulating copper entry and intracellular copper homeostasis. Therefore interplay of both homeostases might play an important role within the cell. Similar to copper, glutathione balance is tightly regulated within individual cellular compartments. This review explores the existing literature on the role of glutathione in regulating cellular copper homeostasis. On the one hand, interplay of glutathione and copper homeostasis performs an important role in normal physiological processes, for example neuronal differentiation. On the other hand, perturbation of the interplay might play a key role in the pathogenesis of copper homeostasis disorders.

Machine learning models are increasingly being deployed in practice. Machine Learning as a Service (MLaaS) providers expose such models to queries by third-party developers through application programming interfaces (APIs). Prior work has developed model extraction attacks, in which an attacker extracts an approximation of an MLaaS model by making black-box queries to it. We design ActiveThief – a model extraction framework for deep neural networks that makes use of active learning techniques and unannotated public datasets to perform model extraction. It does not expect strong domain knowledge or access to annotated data on the part of the attacker. We demonstrate that (1) it is possible to use ActiveThief to extract deep classifiers trained on a variety of datasets from image and text domains, while querying the model with as few as 10-30% of samples from public datasets, (2) the resulting model exhibits a higher transferability success rate of adversarial examples than prior work, and (3) the attack evades detection by the state-of-the-art model extraction detection method, PRADA.

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