A single-stage full bridgeless boost half-bridge AC/DC converter with bidirectional switch

This paper proposes an isolated full bridgeless single stage alternating current-direct current (AC-DC) converter. The proposed converter integrates the operation of a pure bridgeless power factor correction with input boost inductor cascaded with center-tap transformer and half bridge circuit. In addition, the bidirectional switch can be driven with single control signal which further simplifies the controller circuit. It is also proved that this converter reduces the total number of components compared to some conventional circuit and semi-bridgeless circuit topologies. The circuit operation of the proposed circuit is then confirmed with the small signal model, large signal model, circuit simulation and then verified experimentally. It is designed and tested at 115 Vac, 50 Hz of input supply, and 20 Vdc output voltage with maximum output power of 100 W. In addition, the crossover distortion at the input current is minimize at high input line frequency.

Area-Efficient Post-Processing Circuits for Physically Unclonable Function with 2-Mpixel CMOS Image Sensor

In order to realize image information security starting from the data source, challenge–response (CR) device authentication, based on a Physically Unclonable Function (PUF) with a 2 Mpixel CMOS image sensor (CIS), is studied, in which variation of the transistor in the pixel array is utilized. As each CR pair can be used only once to make the CIS PUF resistant to the modeling attack, CR authentication with CIS can be carried out 4050 times, with basic post-processing to generate the PUF ID. If a larger number of authentications is required, advanced post-processing using Lehmer encoding can be utilized to carry out authentication 14,858 times. According to the PUF performance evaluation, the authentication error rate is less than 0.001 ppm. Furthermore, the area overhead of the CIS chip for the basic and advanced post-processing is only 1% and 2%, respectively, based on a Verilog HDL model circuit design.

The Drosophila Larval Locomotor Circuit Provides a Model to Understand Neural Circuit Development and Function

It is difficult to answer important questions in neuroscience, such as: “how do neural circuits generate behaviour?,” because research is limited by the complexity and inaccessibility of the mammalian nervous system. Invertebrate model organisms offer simpler networks that are easier to manipulate. As a result, much of what we know about the development of neural circuits is derived from work in crustaceans, nematode worms and arguably most of all, the fruit fly, Drosophila melanogaster. This review aims to demonstrate the utility of the Drosophila larval locomotor network as a model circuit, to those who do not usually use the fly in their work. This utility is explored first by discussion of the relatively complete connectome associated with one identified interneuron of the locomotor circuit, A27h, and relating it to similar circuits in mammals. Next, it is developed by examining its application to study two important areas of neuroscience research: critical periods of development and interindividual variability in neural circuits. In summary, this article highlights the potential to use the larval locomotor network as a “generic” model circuit, to provide insight into mammalian circuit development and function.

In situ and transcriptomic identification of synapse-associated microglia in the developing zebrafish brain

Microglia are brain resident macrophages that play vital roles in central nervous system (CNS) development, homeostasis, and pathology. Microglia both remodel synapses and engulf apoptotic cell corpses during development, but whether unique molecular programs regulate these distinct phagocytic functions is unknown. Here we identify a molecularly distinct synapse-associated microglial subset in the zebrafish (Danio rerio). We found that ramified microglia populated synapse-rich regions of the midbrain and hindbrain between 7 and 28 days post fertilization. In contrast, microglia in the optic tectum were ameboid and clustered around neurogenic zones. Using single-cell mRNA sequencing combined with metadata from regional bulk sequencing, we identified synapse-associated microglia (SAMs) that were highly enriched in the hindbrain, expressed known synapse modulating genes as well as novel candidates, and engulfed synaptic proteins. In contrast, neurogenic-associated microglia (NAMs) were enriched in optic tectum, had active cathepsin activity, and preferentially engulfed neuronal corpses. These data yielded a functionally annotated atlas of zebrafish microglia (https://www.annamolofskylab.org/microglia-sequencing). Furthermore, they reveal that molecularly distinct phagocytic programs mediate synaptic remodeling and cell engulfment, and establish zebrafish hindbrain as a model circuit for investigating microglial-synapse interactions.

Inferring the function performed by a recurrent neural network

A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics.

Penerapan Model "Circuit Learning" dalam Pembelajaran Menulis Iklan

Artikel ini akan memaparkan kemampuan peserta didik dalam menulis iklan dengan menggunakan model pembelajaran circuit learning. Metode penelitian yang digunakan adalah Penelitian Tindakan Kelas (PTK) rancangan Kemmes dan Mc Taggart, yaitu model spiral. Model tersebut terdiri atas 2 siklus dengan tahapan perencanaan, pelaksanaan tindakan, observasi serta refleksi. Penelitian dilaksanakan di kelas VIII A MTs N 6 Cianjur. Adapun metode pengumpulan data yang digunakan adalah observasi, wawancara, dan tes. Hasil penelitian menunjukkan bahwa kemampuan peserta didik dalam menulis iklan mengalami peningkatan sebesar 34,23%. Dilihat dari nilai rata-rata yang diperoleh peserta didik siklus I 41,33 sedangkan siklus II 75,56 sedangkan tingkat kreativitas peserta didik dalam menulis iklan pada siklus I 2,07 dengan kategori kurang kreatif sedangkan siklus II 3,78 dengan kategori kreaif. Sehingga dapat disimpulkan bahwa penerapan model circuit learning dapat meningkatkan kemampuan peserta didik dalam menulis iklan.Kata kunci: Circuit learning, Iklan, Menulis

Automation of Bandwidth Re-design and its Applications in Amplifier Tuned Oscillators Based on Nullors

In this paper, the method for the design automation of a narrow band-pass amplifier, and hence the amplifier tuned oscillator is discussed. A fixator approach is utilized in this method to design the narrow band-pass amplifiers and a reference circuit is required for this process. The fixator–norator pair helps to generate an extra sub-circuit, generally the feedback network; the addition of this sub-circuit in the actual amplifier circuit will modify the frequency response of the amplifier. The amplifier now behaves like an active narrow band-pass filter, which exactly follows the frequency response of the model circuit. This can be turned into an oscillator by providing positive feedback. Such a circuit possesses independent frequency and amplitude control. Hence, the re-designed circuit can be employed as an active filter or an oscillator at the selected center frequency. In addition to the technical merits, the proposed method has pedagogical importance. Few case studies are worked out in this paper to demonstrate the method.

Learning prediction error neurons in a canonical interneuron circuit

Sensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary visual cortex. Our model makes a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.

Learning prediction error neurons in a canonical interneuron circuit

Sensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary visual cortex. Our model makes a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.