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R. P. Ignatius
Fig. 8 Variations of average firing rate (Hz) of the network with α and G syn when all the neurons
of the network are chaotic at D = 0.5, network activity is independent of G syn . But with increase
in α average firing rate is increased
Fig. 9 Variations of average
firing rate (Hz) of the
network with chaotic
neurons when D and G syn
are varied simultaneously, at
α = 0.5. Then the network
displayed theta firings (3–8
Hz)
3.5 Variations of Neuron Firing with α
To study the variations of firing pattern of the neurons with α, raster plots are drawn.
In Figs. 10 and 11, each dot indicates a spike. The network showed some amount of
synchronization up to α ≈ 0.7. As the value of α is increased further, the network
changes to a complete asynchronous state (Figs. 10 and 11).
From Fig. 10a, for low values of α, neurons fire synchronously but the oscillations
within the network die out fast with a burst. The neurons of the network exhibited
near-death-like spikes for α values less than or equal to 0.7 (Fig. 10a, b). With increase
in α value, the firing of the network sustained longer (Figs. 10 and 11). Large values
of characteristic exponent α of noise caused the asynchronous bursting within the
network to sustain longer (Fig. 11) (Figs. 12 and 13).
R. P. Ignatius
Fig. 8 Variations of average firing rate (Hz) of the network with α and G syn when all the neurons
of the network are chaotic at D = 0.5, network activity is independent of G syn . But with increase
in α average firing rate is increased
Fig. 9 Variations of average
firing rate (Hz) of the
network with chaotic
neurons when D and G syn
are varied simultaneously, at
α = 0.5. Then the network
displayed theta firings (3–8
Hz)
3.5 Variations of Neuron Firing with α
To study the variations of firing pattern of the neurons with α, raster plots are drawn.
In Figs. 10 and 11, each dot indicates a spike. The network showed some amount of
synchronization up to α ≈ 0.7. As the value of α is increased further, the network
changes to a complete asynchronous state (Figs. 10 and 11).
From Fig. 10a, for low values of α, neurons fire synchronously but the oscillations
within the network die out fast with a burst. The neurons of the network exhibited
near-death-like spikes for α values less than or equal to 0.7 (Fig. 10a, b). With increase
in α value, the firing of the network sustained longer (Figs. 10 and 11). Large values
of characteristic exponent α of noise caused the asynchronous bursting within the
network to sustain longer (Fig. 11) (Figs. 12 and 13).