Inhalt des Dokuments
Emergence of two
physical phenomena in neural systems:
Inverse Stochastic Resonance
and Vibrational Resonance
Held by Prof. Muhammet Uzuntarla (Bulent Ecevit University,
17.02.2015, 12:15 Uhr
Inverse stochastic resonance (ISR) is a recently reported phenomenon in which the spiking activity of a single neuron subject to external noise exhibits a pronounced minimum as the noise intensity increases. We clarify the mechanism that underlies ISR and show that its most surprising features are a consequence of the dynamical structure of the model neuron. We show that the ISR effect depends strongly on the procedures used to measure it. Furthermore, we investigate the possible emergence of ISR in an extended neural medium, with a complex network topology. Our results confirm that ISR can appear in these networked systems for a wide range of parameters, and is robust for both electrical and chemical synapses and for balanced networks with excitatory and inhibitory neurons. This is the first time that this intriguing phenomenon has been reported in an extended neural medium. The possible dynamical mechanisms behind ISR in such networked systems is also studied. Moreover, we study the computational implications of these findings and the system requirements concerning neuron, synapse and network topology, for the emergence of such phenomenon in actual neural media, as for instance, neuron cultures. We believe that the reported results might be important for the experimentalist who seeks to observe the ISR phenomenon.
Vibrational resonance (VR) is another interesting phenomenon
whereby the response of some dynamical systems to a weak low-frequency
signal can be maximized with the assistance of an optimal intensity of
another high-frequency signal. In our work, we study the VR in a
heterogeneous neural system having a complex network topology. We
consider a scale-free network of neurons where the heterogeneity is in
the intrinsic excitability of the individual neurons. It is shown that
emergence of VR in heterogeneous neuron population requires less
energy than a homogeneous population. We also find that electrical
coupling strength among neurons plays a key role in determining the
weak signal processing capacity of the heterogeneous population.
Furthermore, we demonstrate that the energy needed to obtain the
resonance grows with the increase in interneuronal link
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