1,111 research outputs found
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
Artificial neural networks in geospatial analysis
Artificial neural networks are computational models widely used in geospatial analysis for data classification, change detection, clustering, function approximation, and forecasting or prediction. There are many types of neural networks based on learning paradigm and network architectures. Their use is expected to grow with increasing availability of massive data from remote sensing and mobile platforms
A Heterosynaptic Learning Rule for Neural Networks
In this article we intoduce a novel stochastic Hebb-like learning rule for
neural networks that is neurobiologically motivated. This learning rule
combines features of unsupervised (Hebbian) and supervised (reinforcement)
learning and is stochastic with respect to the selection of the time points
when a synapse is modified. Moreover, the learning rule does not only affect
the synapse between pre- and postsynaptic neuron, which is called homosynaptic
plasticity, but effects also further remote synapses of the pre- and
postsynaptic neuron. This more complex form of synaptic plasticity has recently
come under investigations in neurobiology and is called heterosynaptic
plasticity. We demonstrate that this learning rule is useful in training neural
networks by learning parity functions including the exclusive-or (XOR) mapping
in a multilayer feed-forward network. We find, that our stochastic learning
rule works well, even in the presence of noise. Importantly, the mean learning
time increases with the number of patterns to be learned polynomially,
indicating efficient learning.Comment: 19 page
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