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Cortical neurons in vivo can operate in a continuum between low-conductance (LC) and high-conductance (HC) states. We investigate how changing the ratio, r, of the mean inhibitory conductance to a fixed value of the mean excitatory conductance affects the regularity of spontaneous cortical firing between the two extreme states. We show that, in general, spike regularity becomes smaller for larger r, thus as the neuron approaches the HC state. Furthermore, in the HC state, spike regularity consistently increases with an increase of the synaptic input variability, whereas with small r, thus LC states, regularity first decreases and th . . .en increases with an increase in the input variability. We suggest that this qualitative difference may reflect a state-dependent dominance of spike output driven by the average membrane potential, versus that driven by fluctuations in the membrane potential
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We study firing rate propagation in a feedforward network with multiple layers. Neurons in the network are modeled by using stochastic Hodgkin-Huxley neuronal model, which considers stochastic behaviour of voltage-gated ion channels embedded in neuronal membranes. In the model, ion channel noise due to its stochastic behaviour is related to the cell size in such a way that the noise strength increases with decreasing the cell size, mimicking the actual biophysical conditions. An external additive current noise is also injected into the first layer of the network. Therefore, neurons in the first layer are subject to both internal and . . . external noise while neurons in the subsequent layers are subject to only internal noise. It is shown that the efficient transmission of firing rates requires an appropriate intrinsic noise level in the network if the input firing rate or the external noise strength is higher than a critical value
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Quantitative description of finger-tapping movements provides more insight into the disturbances in execution of normal motor rhythm, which might be associated with brain damage. Therefore, development of finger-tapping test devices and software is of great importance. In this study, we introduce new computer software to apply a finger-tapping test through a computer keyboard. Free and preconditioned finger-tapping tests can be carried out, and test results can be analyzed within the software and saved in the software database. The test results can also be exported on any spreadsheet platform for an advanced data analysis.
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