Entrainment of a Reciprocal Inhibition Neural Network Model to a Periodic Pulse Train

1998 ◽  
pp. 477-482 ◽  
Author(s):  
Hirofumi Nagashino ◽  
Kazumi Achi ◽  
Yohsuke Kinouchi
2004 ◽  
Vol 14 (05) ◽  
pp. 1559-1575 ◽  
Author(s):  
KATSUMI TATENO ◽  
HIDEYUKI TOMONARI ◽  
HATSUO HAYASHI ◽  
SATORU ISHIZUKA

We studied multistable oscillatory states of a small neural network model and switching of an oscillatory mode. In the present neural network model, two pacemaker neurons are reciprocally inhibited with conduction delay; one pacemaker neuron inhibits the other via an inhibitory nonpacemaker interneuron, and vice versa. The small network model shows bifurcations from quasi-periodic oscillation to chaos via period 3 with increase in the synaptic weight of the reciprocal inhibition. The route to chaos in the network model is different from that in the single pacemaker neuron. The network model exhibits several multistable states. In a regime of a weak inhibitory connection, in-phase beat, out-of-phase beat (period 3), and chaotic oscillation coexist at the multistable state. We can switch an oscillatory mode by an excitatory synaptic input to one of the pacemaker neurons through an afferent path. In a strong inhibitory connection regime, in-phase beat and out-of-phase beat (period 4) coexist at the multistable state. An excitatory synaptic input through the afferent path leads to the transition from the in-phase beat to the out-of-phase beat. The transition from the out-of-phase beat to the in-phase beat is induced by an inhibitory synaptic input via interneurons. A conduction delay, furthermore, causes the spontaneous transition from the in-phase beat to the out-of-phase beat. These transitions can be explained by phase response curves.


Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.


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