![]() We developed an efficient Bayesian computational scheme to fit HSMMs to spike train data. When more general probability distributions are used to describe the state transitions the two-state point process model becomes a hidden semi-Markov model (HSMM). Because observed bursting may in some cases be fairly regular-exhibiting inter-burst intervals with small variation-we relaxed this assumption. ![]() For HMMs, the state transitions follow exponential distributions, and are highly irregular. If in (2) the transitions from non-burst to burst and burst to non-burst states are memoryless, this becomes a hidden Markov model (HMM). A natural description of the phenomenon assumes (1) there are two hidden states, which we label “burst” and “non-burst,” (2) the neuron evolves stochastically, switching at random between these two states, and (3) within each state the spike train follows a time-homogeneous point process. Various methods of detecting bursts in extracellular spike trains have appeared in the literature, the most widely used apparently being Poisson Surprise (PS). Neurons in vitro and in vivo have epochs of bursting or “up state” activity during which firing rates are dramatically elevated.
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