M.Sc Thesis

M.Sc StudentNezhinsky Alexander
SubjectModeling Temporal Properties of Neural Activity
DepartmentDepartment of Biomedical Engineering
Supervisor ASSOCIATE PROF. Moshe Gur


Experiments in vivo have shown that the reliability in response to fluctuating inputs is high, whereas constant inputs produced unreliable responses. Simulations using stochastic Hodgkin-Huxley neuron models confirmed the experimental findings. It was argued that the fluctuations present in the time-varying inputs become the main force driving the cell towards a spike, attenuating the influence of the ion channels stochasticity and increasing reliability. In this work I put forward an alternative explanation, which is relevant for the neuron models near a subcritical Hopf bifurcation. Such models, termed resonators, exhibit damped sub-threshold oscillations, which set the eigenfrequency of the membrane. All resonators, in particular the Hodgkin-Huxley model, are provably sensitive to those spectral components of their inputs, which are close to the eigenfrequency. I suggest that the spectral content around the eigenfrequency is the main factor leading to high reliability. A signal possessing this spectral content elicits a relatively reliable response. Constant input is an extreme particular case of a signal lacking these spectral components, which will lead to low reliability. To test the hypothesis fluctuating signals constructed as the low-pass Gaussian white noise are band-pass and band-stop filtered around the eigenfrequency of the model, which is determined by analyzing the sub-threshold activity. The band-pass filtered signals elicited responses with high reliability measure values very close to those produced by the original inputs. In contrast, the band-stop filtered signals produced very unreliable responses with the reliability measure values only slightly exceeding those produced by the constant stimuli. These results confirm the frequency-dependence hypothesis, indicating that the spectral characteristics of the input signal have a crucial role in determining the temporal reliability and precision. A new robust measure of reliability is introduced.