Functional count data arise when recurrent events are recorded repeatedly over time and scientific interest centers on how event rates evolve over time and across experimental conditions. Standard mixed effects models for counts have interpretable mean structures but require a parametric conditional distribution, which can be restrictive in the presence of overdispersion, unusual zero frequencies and heavy tails. To address these challenges, we develop the functional count DPGLM (fcDPGLM), a semiparametric Bayesian mixed model that combines GLM-style regression with a nonparametric count distribution. Parametric condition-specific mean trajectories and subject-specific random effects determine the conditional mean exactly, allowing the interpretation of contrasts as usual, while remaining details of the count distribution are learned nonparametrically from the data. Poisson and negative-binomial mixed models are recovered as limiting cases. A gamma completely random measure representation allows for efficient posterior simulation. In simulations mimicking a rat seizure study, fcDPGLM estimated mean contrasts as accurately as a correctly specified negative-binomial mixed model, and more accurately otherwise, and recovered zero and exceedance probabilities that the parametric models misrepresent. In the seizure study, seizure rates rise and then decline in the early period after induced status epilepticus but remain high and nearly flat in the chronic period, and rats differ substantially. Poisson and negative-binomial mixed models estimate similar mean trajectories and period contrasts, but only fcDPGLM matches the observed zero frequency, variance and high-burden days.

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