Suggested further readings

Hidden Markov Models

HMMs can be used to understand the statistical structure of birdsong. Katahira, K., Suzuki, K., Okanoya, K., & Okada, M. (2011). Complex sequencing rules of birdsong can be explained by simple hidden Markov processes. PloS one, 6(9), e24516. doi: 10.1371/journal.pone.0024516 .

Kalman Filter

KFs have been used to decode cursor movement from neural activity in brain-computer interfaces. Wu, W., Black, M., Gao, Y., Serruya, M., Shaikhouni, A., Donoghue, J., & Bienenstock, E. (2002). Neural decoding of cursor motion using a Kalman filter. Advances in neural information processing systems, 15. URL: https://proceedings.neurips.cc/paper/2002/file/169779d3852b32ce8b1a1724dbf5217d-Paper.pdf.

Decision making

Drift-diffusion models are really used as models of decision making! Mormann, M. M., Malmaud, J., Huth, A., Koch, C., & Rangel, A. (2010). The drift diffusion model can account for the accuracy and reaction time of value-based choices under high and low time pressure. Judgment and Decision Making, 5(6), 437-449. doi: 10.2139/ssrn.1901533 .

But things might be more complicated! Zoltowski, D. M., Latimer, K. W., Yates, J. L., Huk, A. C., & Pillow, J. W. (2019). Discrete stepping and nonlinear ramping dynamics underlie spiking responses of LIP neurons during decision-making. Neuron, 102(6), 1249-1258. doi: 10.1016/j.neuron.2019.04.031 .

Technical aspects of the models

Chen, Y., & Gupta, M. R. (2010, February). Em demystified: An expectation-maximization tutorial. In Electrical Engineering. URL: https://vannevar.ece.uw.edu/techsite/papers/documents/UWEETR-2010-0002.pdf.