254,519 research outputs found
The Neural Particle Filter
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
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/mm Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
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 2.86mm in 65nm CMOS, achieving a high density of 738k synapses/mm.
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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