10 research outputs found
Method of increasing the information capacity of associative memory of oscillator neural networks using high-order synchronization effect
Computational modelling of two- and three-oscillator schemes with thermally
coupled -switches is used to demonstrate a novel method of pattern
storage and recognition in an impulse oscillator neural network (ONN) based on
the high-order synchronization effect. The method ensures high information
capacity of associative memory, i.e. a large number of synchronous states
. Each state in the system is characterized by the synchronization order
determined as the ratio of harmonics number at the common synchronization
frequency. The modelling demonstrates attainment of of several orders
both for a three-oscillator scheme ~650 and for a two-oscillator scheme
~260. A number of regularities are obtained, in particular, an optimal
strength of oscillator coupling is revealed when has a maximum. A general
tendency toward information capacity decrease is shown when the coupling
strength and switch inner noise amplitude increase. An algorithm of pattern
storage and test vector recognition is suggested. It is also shown that the
coordinate number in each vector should be one less than the switch number to
reduce recognition ambiguity. The demonstrated method of associative memory
realization is a general one and it may be applied in ONNs with various
mechanisms and oscillator coupling topology.Comment: 18 pages, 8 figure
A Bio-Inspired Chaos Sensor Based on the Perceptron Neural Network: Concept and Application for Computational Neuro-science
The study presents a bio-inspired chaos sensor based on the perceptron neural
network. After training, the sensor on perceptron, having 50 neurons in the
hidden layer and 1 neuron at the output, approximates the fuzzy entropy of
short time series with high accuracy with a determination coefficient R2 ~ 0.9.
The Hindmarsh-Rose spike model was used to generate time series of spike
intervals, and datasets for training and testing the perceptron. The selection
of the hyperparameters of the perceptron model and the estimation of the sensor
accuracy were performed using the K-block cross-validation method. Even for a
hidden layer with 1 neuron, the model approximates the fuzzy entropy with good
results and the metric R2 ~ 0.5-0.8. In a simplified model with 1 neuron and
equal weights in the first layer, the principle of approximation is based on
the linear transformation of the average value of the time series into the
entropy value. The bio-inspired chaos sensor model based on an ensemble of
neurons is able to dynamically track the chaotic behavior of a spiked biosystem
and transmit this information to other parts of the bio-system for further
processing. The study will be useful for specialists in the field of
computational neuroscience.Comment: 12 pages, 22 figures, 4 table