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The progress in the field of neural computation hinges on the use of hardware more efficient than the conventional microprocessors. Recent works have shown that mixed-signal integrated memristive circuits, especially their passive ('0T1R') variety, may increase the neuromorphic network performance dramatically, leaving far behind their digital counterparts. The major obstacle, however, is relatively immature memristor technology so that only limited functionality has been demonstrated to date. Here we experimentally demonstrate operation of one-hidden layer perceptron classifier entirely in the mixed-signal integrated hardware, comprised of two passive 20x20 metal-oxide memristive crossbar arrays, board-integrated with discrete CMOS components. The demonstrated multilayer perceptron network, whose complexity is almost 10x higher as compared to previously reported functional neuromorphic classifiers based on passive memristive circuits, achieves classification fidelity within 3 percent of that obtained in simulations, when using ex-situ training approach. The successful demonstration was facilitated by improvements in fabrication technology of memristors, specifically by lowering variations in their I-V characteristics., Comment: 26 pages, 12 figures

We have designed, fabricated, and successfully tested a prototype mixed-signal, 28x28-binary-input, 10-output, 3-layer neuromorphic network ("MLP perceptron"). It is based on embedded nonvolatile floating-gate cell arrays redesigned from a commercial 180-nm NOR flash memory. The arrays allow precise (~1%) individual tuning of all memory cells, having long-term analog-level retention and low noise. Each array performs a very fast and energy-efficient analog vector-by-matrix multiplication, which is the bottleneck for signal propagation in most neuromorphic networks. All functional components of the prototype circuit, including 2 synaptic arrays with 101,780 floating-gate synaptic cells, 74 analog neurons, and the peripheral circuitry for weight adjustment and I/O operations, have a total area below 1 mm^2. Its testing on the common MNIST benchmark set (at this stage, with a relatively low weight import precision) has shown a classification fidelity of 94.65%, close to the 96.2% obtained in simulation. The classification of one pattern takes less than 1 us time and ~20 nJ energy - both numbers much better than for digital implementations of the same task. Estimates show that this performance may be further improved using a better neuron design and a more advanced memory technology, leading to a >10^2 advantage in speed and a >10^4 advantage in energy efficiency over the state-of-the-art purely digital (GPU and custom) circuits, at classification of large, complex patterns., 4 pages, 10 figures

This paper proposes a new numerical optimization algorithm inspired by the strawberry plant for solving complicated engineering problems. Plants like strawberry develop both runners and roots for propagation and search for water resources and minerals. In these plants, runners and roots can be thought of as tools for global and local searches, respectively. The proposed algorithm has three main differences with the trivial nature-inspired optimization algorithms: duplication-elimination of the computational agents at all iterations, subjecting all agents to both small and large movements from the beginning to end, and the lack of communication (information exchange) between agents. Moreover, it has the advantage of using only three parameters to be tuned by user. This algorithm is applied to standard test functions and the results are compared with GA and PSO. The proposed algorithm is also used to solve an open problem in the field of robust control theory. These simulations show that the proposed algorithm can very effectively solve complicated optimization problems.

BACKGROUND: Renal failure is a frequent event after coronary artery bypass grafting (CABG). Hemodynamic alterations during surgery as well as the underlying disease are the predisposing factors. We aimed to study intermittent furosemide therapy in the prevention of renal failure in patients undergoing CABG. METHODS: In a single-blind randomized controlled trial, 123 elective CABG patients, 18-75 years, entered the study. Clearance of creatinine, urea and water were measured. Patients were randomly assigned into three groups: furosemide in prime (0.3-0.4 mg/kg); intermittent furosemide during CABG (0.2 mg/kg, if there was a decrease in urinary excretion) and control (no furosemide). RESULTS: There was a significant change in serum urea, sodium and fluid balance in “intermittent furosemide” group; other variables did not change significantly before or after the operation. Post-operative fluid balance was significantly higher in “intermittent furosemide” group (2573 ± 205 ml) compared to control (1574 ± 155 ml) (P < 0.010); also, fluid balance was higher in “intermittent furosemide” group (2573 ± 205 ml) compared to “furosemide in prime” group (1935 ± 169 ml) (P < 0.010). CONCLUSION: The study demonstrated no benefit from intermittent furosemide in elective CABG compared to furosemide in prime volume or even placebo. &nbsp

Cardiac lipomas are extremely rare tumors of the heart. They are usually symptomatic and rarely may be found incidentally in autopsies. Here we describe a 23-year-old healthy man, in whose physical examination for employment a murmur was found incidentally. Transthoracic and then transesophageal echocardiographic examination showed a 4-cm oval-shaped mass in the right ventricular outflow tract. He underwent elective surgery, during which the tumor was removed under cardiopulmonary bypass and aortic-cross clamping via right atriotomy. The postoperative course was uneventful, and the patient was in good condition at 1 year’s follow-up.