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 Open Access publication.

  • 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:

  • 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 Closed Access publication.

  • 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 Open Access publication.

  • 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: