Suggested further readings

Hidden Markov Models

Katahira, K., Suzuki, K., Okanoya, K., and 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 .

• HMMs can be used to understand the statistical structure of birdsong.

Kalman Filter

Wu, W., Black, M., Gao, Y., Serruya, M., Shaikhouni, A., Donoghue, J., and Bienenstock, E. (2002). Neural decoding of cursor motion using a Kalman filter. Advances in neural information processing systems, 15. url: papers.nips.cc/paper/2002/hash/169779d3852b32ce8b1a1724dbf5217d-Abstract.html.

• KFs have been used to decode cursor movement from neural activity in brain-computer interfaces.

Decision making

Mormann, M. M., Malmaud, J., Huth, A., Koch, C., and 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 .

• Drift-diffusion models are really used as models of decision making!

Zoltowski, D. M., Latimer, K. W., Yates, J. L., Huk, A. C., and 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 .

• But things might be more complicated!

Technical aspects of the models

Chen, Y., and Gupta, M. R. (2010). EM demystified: An expectation-maximization tutorial. In Electrical Engineering. url: vannevar.ece.uw.edu/techsite/papers/documents/UWEETR-2010-0002.pdf.