254,519 research outputs found

    The Neural Particle Filter

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    The robust estimation of dynamically changing features, such as the position of prey, is one of the hallmarks of perception. On an abstract, algorithmic level, nonlinear Bayesian filtering, i.e. the estimation of temporally changing signals based on the history of observations, provides a mathematical framework for dynamic perception in real time. Since the general, nonlinear filtering problem is analytically intractable, particle filters are considered among the most powerful approaches to approximating the solution numerically. Yet, these algorithms prevalently rely on importance weights, and thus it remains an unresolved question how the brain could implement such an inference strategy with a neuronal population. Here, we propose the Neural Particle Filter (NPF), a weight-less particle filter that can be interpreted as the neuronal dynamics of a recurrently connected neural network that receives feed-forward input from sensory neurons and represents the posterior probability distribution in terms of samples. Specifically, this algorithm bridges the gap between the computational task of online state estimation and an implementation that allows networks of neurons in the brain to perform nonlinear Bayesian filtering. The model captures not only the properties of temporal and multisensory integration according to Bayesian statistics, but also allows online learning with a maximum likelihood approach. With an example from multisensory integration, we demonstrate that the numerical performance of the model is adequate to account for both filtering and identification problems. Due to the weightless approach, our algorithm alleviates the 'curse of dimensionality' and thus outperforms conventional, weighted particle filters in higher dimensions for a limited number of particles

    The Teachers’ strategies in Online Teaching of Reading Comprehension at the Covid-19 Pandemic

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    This study focused on the teachers strategy in teaching online towards reading comprehension are a gateway for the entry of scientific concepts in the brain. Reading skills focus more on reading comprehension skills, because the ability to understand reading material (text) is the main goal to be achieved in reading learning. Students must have the ability to read in order to understand the meaning contained in the reading. Without good reading skills, students cannot understand the learning process and the material being taught. Teachers must be able to fully try to play their role so that students have these abilities, especially reading skills. Since the outbreak of the pandemic caused by the Corona virus in Indonesia, many ways have been taken by the government to prevent its spread. This research is a qualitative research arranged descriptively which aims to obtain an overview of online learning being carried out in one junior high school in an effort to suppress the spread of Covid-19 in the school environment. Based on the data, the writer got the findings that: (1) Understanding Lesson Plan; (2) prepare learning materials and resources; (3) inquire students’ physical and non-physical readiness; (4) Attempt to equate it like offline learning; (5) confim student understanding; (6) determine the learning method; (7) assessment of student abilities. It is show that based in the findings, the teachers strategy in teaching online reading comprehension in pandemic Covid-19 is although previous research has discussed the strategy of the teacher in learning reading comprehension at the covid-19 pandemic, but little discussed about suitable strategy to use when online teaching

    MorphIC: A 65-nm 738k-Synapse/mm2^2 Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning

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    Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of binary weights, which were demonstrated to have a limited accuracy reduction on many applications when quantization-aware training techniques are used. In parallel, spiking neural network (SNN) architectures are explored to further reduce power when processing sparse event-based data streams, while on-chip spike-based online learning appears as a key feature for applications constrained in power and resources during the training phase. However, designing power- and area-efficient spiking neural networks still requires the development of specific techniques in order to leverage on-chip online learning on binary weights without compromising the synapse density. In this work, we demonstrate MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning rule and a hierarchical routing fabric for large-scale chip interconnection. The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF) neurons and more than two million plastic synapses for an active silicon area of 2.86mm2^2 in 65nm CMOS, achieving a high density of 738k synapses/mm2^2. MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy tradeoff on the MNIST classification task compared to previously-proposed SNNs, while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE Transactions on Biomedical Circuits and Systems journal (2019), the fully-edited paper is available at https://ieeexplore.ieee.org/document/876400
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