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| Paper IPM / Cognitive / 11147 |
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| Abstract: | |
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We apply the point process theory for modeling of single neuron and apply it in to the real data which we recorded from Inferior temporal cortex of monkey. In this paper the state space point process filter theory will be used to estimate the conditional intensity of point process observation as a time varying firing rate. The parametric model of the conditional intensity function will be used to define the effect of biophysical property of neuron such as history effect and stimulus effect as well. The model parameters will be estimated by expectation maximization algorithms. The goodness-of-fit will be assessed using the time rescale theorem. Finally I will apply the point process modeling on real data from inferior temporal cortex of macaque monkey. I will try to capture the time scale effect and stimulus modulated effects for individual neuron separately. It also helps me to captures the biophysical property of neuron and the stimulus effect for each trial and demonstrates it dynamically.
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