In this thesis, the transmission of spike trains in a neuron model is studied in order to obtain a better understanding of the role played by stochastic activity, i.e. uncorrelated spikes, in the auditory pathway. Fluctuations of the neuron membrane potential are given by a first-order stochastic differential equation, using a leaky integrate-and-fire model. In contrast to most previous studies the model has a finite number of synapses, and the usual diffusion approximation does not hold. / The input signal is modeled by spike trains with spiking times described by inhomogenous Poisson processes. The membrane potential is a shot noise process for which statistical properties are derived with a Gaussian approximation. The statistics of the output spike train are obtained by using the property that a pool of a large number of output spike trains can be modeled by an inhomogeneous Poisson process. It is shown that, under certain conditions, the addition of uncorrelated input spikes, i.e. noise, can enhance the transmission of periodic temporal information. This phenomenon, called stochastic resonance, is demonstrated analytically and supported by computer simulations. / Results are compared with those obtained from the traditional leaky integrate-and- fire neuron receiving a continuous waveform input. The shot-noise property of the membrane potential, which implies that its variance is de facto modulated by the input stimulus, is shown to enhance the phenomenon of stochastic resonance. Indeed, for a given average noise level, a modulated noise gives a higher output signal-to-noise ratio than an unmodulated noise with the same average amplitude. / The derivation is then extended to certain polyperiodic stimuli mimicking vowel sounds. The fact that the addition of uncorrelated input spikes can enhance the transmission of information is discussed in the context of cochlear implants. The results provide supportive evidence to the postulate that a cochlear implant speech coding strategy that elicits stochastic firing neural activity might benefit the user.
| Identifer | oai:union.ndltd.org:ADTP/245193 |
| Creators | Hohn, Nicolas |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
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