20,146 research outputs found
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems
Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been
demonstrated to perform efficiently in a variety of applications, such as
dimensionality reduction, feature learning, and classification. Their
implementation on neuromorphic hardware platforms emulating large-scale
networks of spiking neurons can have significant advantages from the
perspectives of scalability, power dissipation and real-time interfacing with
the environment. However the traditional RBM architecture and the commonly used
training algorithm known as Contrastive Divergence (CD) are based on discrete
updates and exact arithmetics which do not directly map onto a dynamical neural
substrate. Here, we present an event-driven variation of CD to train a RBM
constructed with Integrate & Fire (I&F) neurons, that is constrained by the
limitations of existing and near future neuromorphic hardware platforms. Our
strategy is based on neural sampling, which allows us to synthesize a spiking
neural network that samples from a target Boltzmann distribution. The recurrent
activity of the network replaces the discrete steps of the CD algorithm, while
Spike Time Dependent Plasticity (STDP) carries out the weight updates in an
online, asynchronous fashion. We demonstrate our approach by training an RBM
composed of leaky I&F neurons with STDP synapses to learn a generative model of
the MNIST hand-written digit dataset, and by testing it in recognition,
generation and cue integration tasks. Our results contribute to a machine
learning-driven approach for synthesizing networks of spiking neurons capable
of carrying out practical, high-level functionality.Comment: (Under review
Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
We apply a machine learning algorithm, the artificial neural network, to the
search for gravitational-wave signals associated with short gamma-ray bursts.
The multi-dimensional samples consisting of data corresponding to the
statistical and physical quantities from the coherent search pipeline are fed
into the artificial neural network to distinguish simulated gravitational-wave
signals from background noise artifacts. Our result shows that the data
classification efficiency at a fixed false alarm probability is improved by the
artificial neural network in comparison to the conventional detection
statistic. Therefore, this algorithm increases the distance at which a
gravitational-wave signal could be observed in coincidence with a gamma-ray
burst. In order to demonstrate the performance, we also evaluate a few seconds
of gravitational-wave data segment using the trained networks and obtain the
false alarm probability. We suggest that the artificial neural network can be a
complementary method to the conventional detection statistic for identifying
gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure
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