799 research outputs found
Efficient Computation in Adaptive Artificial Spiking Neural Networks
Artificial Neural Networks (ANNs) are bio-inspired models of neural
computation that have proven highly effective. Still, ANNs lack a natural
notion of time, and neural units in ANNs exchange analog values in a
frame-based manner, a computationally and energetically inefficient form of
communication. This contrasts sharply with biological neurons that communicate
sparingly and efficiently using binary spikes. While artificial Spiking Neural
Networks (SNNs) can be constructed by replacing the units of an ANN with
spiking neurons, the current performance is far from that of deep ANNs on hard
benchmarks and these SNNs use much higher firing rates compared to their
biological counterparts, limiting their efficiency. Here we show how spiking
neurons that employ an efficient form of neural coding can be used to construct
SNNs that match high-performance ANNs and exceed state-of-the-art in SNNs on
important benchmarks, while requiring much lower average firing rates. For
this, we use spike-time coding based on the firing rate limiting adaptation
phenomenon observed in biological spiking neurons. This phenomenon can be
captured in adapting spiking neuron models, for which we derive the effective
transfer function. Neural units in ANNs trained with this transfer function can
be substituted directly with adaptive spiking neurons, and the resulting
Adaptive SNNs (AdSNNs) can carry out inference in deep neural networks using up
to an order of magnitude fewer spikes compared to previous SNNs. Adaptive
spike-time coding additionally allows for the dynamic control of neural coding
precision: we show how a simple model of arousal in AdSNNs further halves the
average required firing rate and this notion naturally extends to other forms
of attention. AdSNNs thus hold promise as a novel and efficient model for
neural computation that naturally fits to temporally continuous and
asynchronous applications
Determining the number of hidden units in multi-layer perceptrons using F-ratios
The hidden units in multi-layer perceptrons are believed to act as feature extractors. In other words, the outputs of the hidden units represent the features in a more traditional statistical classification paradigm. This viewpoint offers a statistical, objective approach to determining the optimal number of hidden units required. This approach is based on an F-ratio test, and proceeds in an iterative fashion. The method and its application to simulated time-series data are presented
Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach
Current grammar-based NeuroEvolution approaches have several shortcomings. On
the one hand, they do not allow the generation of Artificial Neural Networks
(ANNs composed of more than one hidden-layer. On the other, there is no way to
evolve networks with more than one output neuron. To properly evolve ANNs with
more than one hidden-layer and multiple output nodes there is the need to know
the number of neurons available in previous layers. In this paper we introduce
Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation
that overcomes the aforementioned limitations. By enabling the creation of
dynamic rules that specify the connection possibilities of each neuron, the
methodology enables the evolution of multi-layered ANNs with more than one
output neuron. Results in different classification problems show that DSGE
evolves effective single and multi-layered ANNs, with a varying number of
output neurons
Adaptive Resonance Associative Map: A Hierarchical ART System for Fast Stable Associative Learning
This paper introduces a new class of predictive ART architectures, called Adaptive Resonance Associative Map (ARAM) which performs rapid, yet stable heteroassociative learning in real time environment. ARAM can be visualized as two ART modules sharing a single recognition code layer. The unit for recruiting a recognition code is a pattern pair. Code stabilization is ensured by restricting coding to states where resonances are reached in both modules. Simulation results have shown that ARAM is capable of self-stabilizing association of arbitrary pattern pairs of arbitrary complexity appearing in arbitrary sequence by fast learning in real time environment. Due to the symmetrical network structure, associative recall can be performed in both directions.Air Force Office of Scientific Research (90-0128
Feature selection using genetic algorithms and probabilistic neural networks
Selection of input variables is a key stage in building
predictive models, and an important form of data mining. As exhaustive evaluation of potential input sets using full non-linear models is impractical, it is necessary to use simple fast-evaluating models and heuristic selection strategies. This paper discusses a fast, efficient, and powerful nonlinear input selection procedure using a combination of Probabilistic Neural Networks and repeated
bitwise gradient descent. The algorithm is compared
with forward elimination, backward elimination and genetic algorithms using a selection of real-world data sets. The algorithm has comparative performance and greatly reduced execution time with respect to these alternative approaches. It is demonstrated empirically that reliable results cannot be gained using any of these approaches without the use of resampling
Power Optimizations in MTJ-based Neural Networks through Stochastic Computing
Artificial Neural Networks (ANNs) have found widespread applications in tasks
such as pattern recognition and image classification. However, hardware
implementations of ANNs using conventional binary arithmetic units are
computationally expensive, energy-intensive and have large area overheads.
Stochastic Computing (SC) is an emerging paradigm which replaces these
conventional units with simple logic circuits and is particularly suitable for
fault-tolerant applications. Spintronic devices, such as Magnetic Tunnel
Junctions (MTJs), are capable of replacing CMOS in memory and logic circuits.
In this work, we propose an energy-efficient use of MTJs, which exhibit
probabilistic switching behavior, as Stochastic Number Generators (SNGs), which
forms the basis of our NN implementation in the SC domain. Further, error
resilient target applications of NNs allow us to introduce Approximate
Computing, a framework wherein accuracy of computations is traded-off for
substantial reductions in power consumption. We propose approximating the
synaptic weights in our MTJ-based NN implementation, in ways brought about by
properties of our MTJ-SNG, to achieve energy-efficiency. We design an algorithm
that can perform such approximations within a given error tolerance in a
single-layer NN in an optimal way owing to the convexity of the problem
formulation. We then use this algorithm and develop a heuristic approach for
approximating multi-layer NNs. To give a perspective of the effectiveness of
our approach, a 43% reduction in power consumption was obtained with less than
1% accuracy loss on a standard classification problem, with 26% being brought
about by the proposed algorithm.Comment: Accepted in the 2017 IEEE/ACM International Conference on Low Power
Electronics and Desig
RDNN for Classification and Prediction of Rock or Mine in Underwater Acoustics
Mines in the waters are just explosives that detonate upon contact with an object. The underwater submarine must foresee if it will encounter a mine or a rock. Lacking the development of the Ranging Sound Navigation approach, which utilizes particular variables to identify whether a surface or a barrier is made of a mine or rock, finding mines or rocks would have been extremely difficult. In our study, we demonstrate a technique for predicting underwater rocks and mines using SONAR waves. At 60 different angles, SONAR pings are employed to record the various frequencies of submerged objects. To identify whether the object in the ocean is a mine or just a rock, the submarine uses SONAR signals, which transmit sound and receive switchbacks. The mine and rock categories are predicted using the prediction models. To create these prediction models, Supervised Machine Learning Classification methods were employed
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