Noise-induced transitions in slow wave neuronal dynamics

Sukbin Lim, NUY


Many neuronal systems exhibit slow random alternations in activity
states. We analyze the noise-induced transitions and statistical properties in
relaxation oscillator models for such systems. We find that the
statistical properties can be used to distinguish among biophysical
mechanisms, such as multiplicative or additive negative feedback:
different slow negative feedbacks may lead to a particular pattern of
temporal correlations. Applications to models of cellular pacemaker
neurons and of spontaneously active networks will be discussed.