Particle filtering of point processes observation with application on the modeling of visual cortex neural spiking activity

Recording of neural response to specific stimulus in a repeated trial is very common in neuroscience protocol. The perstimulus time histogram (PSTH) is a standard tool for analysis of neural response. However it could not capture the non-deterministic properties of the neuron especially in higher level cortical area such as inferior temporal cortex. The stochastic state point process filter theory is used for the estimation of the conditional intensity of the point process observation as a time varying firing rate and the particle filter is used to numerically estimate this density in time. The particle filters were applied to the results of the point process observation for compensating the Gaussian assumption. The results of applying point process modeling on a real data from inferior temporal cortex of macaque monkey indicates that, based on the assessment of goodness-of-fit, the neural spiking activity and biophysical property of neuron could be captured more accurately in compare to conventional methods.