Signal Processing in Noisy Neurons: Methods and Results
Hans Plesser (Mathematical Biosciences Institute, The Ohio State University)
(October 1, 2002 4:30 PM - 5:30 PM)
I will present some of the methods I developed during my PhD-work for the study of stochastic resonance in leaky integrate-and-fire neurons. Specifically, I will present Markov-chain based methods for the calculation of the power spectral density of spike trains evoked by sinusoidal stimuli. Based on these methods, I will investigate the influence of signal frequency and background noise on the signal-to-noise ratio of the neuronal response, and discuss some scaling properties.
If anyone had an idea on how to prove that the interspike-interval density of the leaky integrate-and-fire neuron driven by sinusoidal input and Gaussian white noise is strictly positive for t>0 (except possibly at isolated points), I'd be very happy about suggestions. The proof in Section 2.2.3 of my thesis is, unfortunately, badly flawed.