TY - JFULL
AU - Kelvin Rozier and Vladimir E. Bondarenko
PY - 2012/6/
TI - Some Remarkable Properties of a Hopfield Neural Network with Time Delay
T2 - International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering
SP - 512
EP - 518
EM - krozier3@student.gsu.edu, vbondarenko@gsu.edu
VL - 6
SN - 1307-6892
UR - http://waset.org/publications/2754
PU - World Academy of Science, Engineering and Technology
NX - International Science Index 65, 2012
N2 - It is known that an analog Hopfield neural network
with time delay can generate the outputs which are similar to the
human electroencephalogram. To gain deeper insights into the
mechanisms of rhythm generation by the Hopfield neural networks
and to study the effects of noise on their activities, we investigated
the behaviors of the networks with symmetric and asymmetric
interneuron connections. The neural network under the study consists
of 10 identical neurons. For symmetric (fully connected) networks all
interneuron connections aij = +1; the interneuron connections for
asymmetric networks form an upper triangular matrix with non-zero
entries aij = +1. The behavior of the network is described by 10
differential equations, which are solved numerically. The results of
simulations demonstrate some remarkable properties of a Hopfield
neural network, such as linear growth of outputs, dependence of
synchronization properties on the connection type, huge
amplification of oscillation by the external uniform noise, and the
capability of the neural network to transform one type of noise to
another.
ER -