CA1 pyramidal cell diversity is rootedin the time of neurogenesis

Cellular diversity supports the computational capacity and flexibility of cortical circuits. Accordingly, principal neurons at the CA1 output node of the murine hippocampus are increasingly recognized as a heterogeneous population. Their genes, molecular content, intrinsic morphophysiology, connectivity, and function seem to segregate along the main anatomical axes of the hippocampus. Since these axes reflect the temporal order of principal cell neurogenesis, we directly examined the relationship between birthdate and CA1 pyramidal neuron diversity, focusing on the ventral hippocampus. We used a genetic fate-mapping approach that allowed tagging three groups of age-matched principal neurons: pioneer, early- and late-born. Using a combination of neuroanatomy, slice physiology, connectivity tracing and cFos staining in mice, we show that birthdate is a strong predictor of CA1 principal cell diversity. We unravel a subpopulation of pioneer neurons recruited in familiar environments with remarkable positioning, morpho-physiological features, and connectivity. Therefore, despite the expected plasticity of hippocampal circuits, given their role in learning and memory, the diversity of their main components is also partly determined at the earliest steps of development.

A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication

Forecasting the cycle time of each job is a critical task for a factory. However, recent studies have shown that it is a challenging task, even with state-of-the-art deep learning techniques. To address this challenge, a selectively fuzzified back propagation network (SFBPN) approach is proposed to estimate the range of a cycle time, the results of which provide valuable information for many managerial purposes. The SFBPN approach is distinct from existing methods, because the thresholds on both the hidden and output layers of a back propagation network are fuzzified to tighten the range of a cycle time, while most of the existing methods only fuzzify the threshold on the output node. In addition, a random search and local optimization algorithm is also proposed to derive the optimal values of the fuzzy thresholds. The proposed methodology is applied to a real case from the literature. The experimental results show that the proposed methodology improved the forecasting precision by up to 65%.

PRAKIRAAN HARGA MEAT BONE MEAL (MBM) MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION

XYZ is a poultry feed producer in Banyuwangi Regency, East Java. The problem in developing poultry feed at PT XYZ was the fluctuating price of poultry feed. Meat bone meal (MBM) or what is called meat flour is one of the raw materials for poultry feed that affects the final price of poultry feed products. The price of MBM was greatly influenced by the exchange rate of the rupiah against the dollar. Forecasting is one way that needs to be done in dealing with MBM price fluctuations. The aim of this study was to estimate the price of MBM using backpropagation neural networks (BNN). The data used in this study was the price of MBM in the period January 2016-October 2018. Based on the results of the study, the best BNN architecture for the estimated MBM price was12-10-1 (12 input nodes, 10 hidden nodes, and 1 output node). This architecture has reached the training target of 0.002 with a MAPE test value of 13.93%. Based on forecasts with the BNN the highest MBM price in May 2019 and the lowest MBM price in January 2019.

Multi-attribute Recognition,the key to Universal Neural Network

<p>To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly. The deep neural network I use is the most common convolution neural network. Through test,we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The concrete network(generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition,and nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs. I guess that the phenomenon of synaesthesia is the result of multi-input and multi-output. I guess that connection in mind can realize through the universal network and sending the output into input.<b></b></p>

Improved Frequency Compensation Technique for Three-Stage Amplifiers

Improved frequency compensation is proposed for a three-stage amplifier with reduced total capacitance, improved slew rate, and reduced settling time. The proposed compensation uses an auxiliary feedback to increase the total effective compensation capacitance without loading the output node. The proposed compensation scheme is validated in simulation by implementing a three-stage amplifier driving 10 pF load capacitor in a 0.18 μm CMOS process. A detailed comparison of the compensation with a conventional nested Miller compensation is also presented. The simulation results showed a reduction in total compensation capacitance and improvement in slew rate compared to conventional nested Miller compensation and the other reported techniques in the literature.

Multi-attribute Recognition,the key to Universal Neural Network

<p>To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly. The deep neural network I use is the most common convolution neural network. Through test,we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The concrete network(generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition,and nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs. I guess that the phenomenon of synaesthesia is the result of multi-input and multi-output. I guess that connection in mind can realize through the universal network and sending the output into input.<b></b></p>

CA1 pyramidal cell diversity is rooted in the time of neurogenesis

Cellular diversity supports the computational capacity and flexibility of cortical circuits. Accordingly, principal neurons at the CA1 output node of the hippocampus are increasingly recognized as a heterogeneous population. Their genes, molecular content, intrinsic morpho-physiology, connectivity, and function seem to segregate along the main anatomical axes of the hippocampus. Since these axes reflect the temporal order of principal cell neurogenesis, we directly examined the relationship between birthdate and CA1 pyramidal neuron diversity, focusing on the ventral hippocampus. We used a genetic fate-mapping approach that allowed tagging three groups of age-matched principal neurons: pioneer, early-and late-born. Using a combination of neuroanatomy, slice physiology, connectivity tracing and cFos staining, we show that birthdate is a strong predictor of CA1 principal cell diversity. We unravel a subpopulation of pioneer neurons recruited in familiar environments with remarkable positioning, morpho-physiological features, and connectivity. Therefore, despite the expected plasticity of hippocampal circuits, given their role in learning and memory, the diversity of their main components is significantly predetermined at the earliest steps of development.

KLECTOR: Design of Low Power Static Random-Access Memory Architecture with reduced Leakage Current

A novel approach called Keeper in LEakage Control Transistor (KLECTOR) is presented in this paper to reduce leakage currents in SRAM architecture. The SRAM is significantly affected by the leakage current during the "standby mode", which is caused by the fabric which has a lower threshold voltage. KLECTOR circuit employs less power consumption by restricting the flow of current through devices of less voltage drops and relies heavily on the self-controlled transistor at the output node. It has been found from the presented results that static (leakage) power in the write operation is reduced to 63% and 69 % for the read operation. This proposed approach is designed and simulated using the Virtuoso, Cadence EDA tool.

A two-stage power amplifier design for ultra-wideband applications

In this paper, a two-stage 0.18 μm CMOS power amplifier (PA) for ultra-wideband (UWB) 3 to 5 GHz based on common source inductive degeneration with an auxiliary amplifier is proposed. In this proposal, an auxiliary amplifier is used to place the 2nd harmonic in the core amplified in order to make up for the gain progression phenomena at the main amplifier output node. Simulation results show a power gain of 16 dB with a gain flatness of 0.4 dB and an input 1 dB compression of about -5 dBm from 3 to 5 GHz using a 1.8 V power supply consuming 25 mW. Power added efficiency (PAE) of around 47% at 4 GHz with 50 Ω load impedance was also observed.

Multi-attribute Recognition,The key to Universal Neural Network

<p><b>To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly.</b><b> </b><b>The deep neural network I use is the most common convolution neural network. Through test,we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The </b><b>c</b><b>oncrete network(generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition,and nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs.</b><b> I guess that t</b><b>he phenomenon of synaesthesia is the result of multi-input and multi-output. </b><b>I guess that c</b><b>onnection in mind can realize through the universal network and sending the output into input.</b><b></b></p>