Suggested further readings

Gerwinn, S., Bethge, M., Macke, J. H., and Seeger, M. (2007). Bayesian inference for spiking neuron models with a sparsity prior. Advances in neural information processing systems, 20. url:

Glaser, J. I., Benjamin, A. S., Farhoodi, R., and Kording, K. P. (2019). The roles of supervised machine learning in systems neuroscience. Progress in neurobiology 175: 126-137. doi: 10.1016/j.pneurobio.2019.01.008 Closed Access publication (postprint: Open Access publication).

Glaser, J. I., Benjamin, A. S., Chowdhury, R. H., Perich, M. G., Miller, L. E., and Kording, K. P. (2020). Machine learning for neural decoding. Eneuro 7(4). doi: 10.1523/ENEURO.0506-19.2020 Open Access publication.

Hardcastle, K., Maheswaranathan, N., Ganguli, S., and Giocomo, L. M. (2017). A multiplexed, heterogeneous, and adaptive code for navigation in medial entorhinal cortex. Neuron 94(2): 375-387. doi: 10.1016/j.neuron.2017.03.025 Open Access publication.

Latimer, K. W., Rieke, F., and Pillow, J. W. (2019). Inferring synaptic inputs from spikes with a conductance-based neural encoding model. Elife 8: e47012. doi: 10.7554/eLife.47012 Open Access publication.

Macke, J. H., Buesing, L., Cunningham, J. P., Yu, B. M., Shenoy, K. V., and Sahani, M. (2011). Empirical models of spiking in neural populations. Advances in neural information processing systems, 24. url:

Maheswaranathan, N., Kastner, D. B., Baccus, S. A., and Ganguli, S. (2018). Inferring hidden structure in multilayered neural circuits. PLoS computational biology 14(8): e1006291. doi: 10.1371/journal.pcbi.1006291 Open Access publication.

McCullagh, P., and Nelder, J. A. (1989). Generalized linear models. Chapman and Hall. London, UK.

McFarland, J. M., Cui, Y., and Butts, D. A. (2013). Inferring nonlinear neuronal computation based on physiologically plausible inputs. PLoS computational biology 9(7): e1003143. doi: 10.1371/journal.pcbi.1003143 Open Access publication.

Paninski, L. (2004). Maximum likelihood estimation of cascade point-process neural encoding models. Network: Computation in Neural Systems 15(4): 243. doi: 10.1088/0954-898X/15/4/002 Closed Access publication.

Panzeri, S., Harvey, C. D., Piasini, E., Latham, P. E., and Fellin, T. (2017). Cracking the neural code for sensory perception by combining statistics, intervention, and behavior. Neuron 93(3): 491-507. doi: 10.1016/j.neuron.2016.12.036 Open Access publication.

Park, I. M., and Pillow, J. (2011). Bayesian spike-triggered covariance analysis. Advances in neural information processing systems, 24. url:

Park, M., and Pillow, J. W. (2011). Receptive field inference with localized priors. PLoS computational biology 7(10): e1002219. doi: 10.1371/journal.pcbi.1002219 Open Access publication.

Park, I. M., Meister, M. L., Huk, A. C., and Pillow, J. W. (2014). Encoding and decoding in parietal cortex during sensorimotor decision-making. Nature neuroscience, 17(10), 1395-1403. doi: 10.1038/nn.3800 Closed Access publication (postprint: Open Access publication).

Pillow, J. W., Paninski, L., Uzzell, V. J., Simoncelli, E. P., and Chichilnisky, E. J. (2005). Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. Journal of Neuroscience, 25(47), 11003-11013. doi: 10.1523/JNEUROSCI.3305-05.2005 Open Access publication.

Pillow, J. W., Shlens, J., Paninski, L., Sher, A., Litke, A. M., Chichilnisky, E. J., and Simoncelli, E. P. (2008). Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature, 454(7207), 995-999. doi: 10.1038/nature07140 Closed Access publication (postprint: Open Access publication).

Pillow, J., and Scott, J. (2012). Fully Bayesian inference for neural models with negative-binomial spiking. Advances in neural information processing systems, 25. url:

Shlens, J. (2014). Notes on generalized linear models of neurons. arXiv preprint arxiv:1404.1999.

Simoncelli, E. P., Paninski, L., Pillow, J., and Schwartz, O. (2004). Characterization of neural responses with stochastic stimuli. The cognitive neurosciences, 3(327-338), 1. In M. S. Gazzaniga (Ed.), The Cognitive Neurosciences III. The MIT Press.

Stevenson, I. H., London, B. M., Oby, E. R., Sachs, N. A., Reimer, J., Englitz, B., …, and Kording, K. P. (2012). Functional connectivity and tuning curves in populations of simultaneously recorded neurons. PLoS computational biology 8(11): e1002775. doi: 10.1371/journal.pcbi.1002775 Open Access publication.

Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P., and Brown, E. N. (2005). A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. Journal of neurophysiology 93(2): 1074-1089. doi: 10.1152/jn.00697.2004 Closed Access publication.

Vidne, M., Ahmadian, Y., Shlens, J., Pillow, J. W., Kulkarni, J., Litke, A. M., …, and Paninski, L. (2012). Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. Journal of computational neuroscience 33(1): 97-121. doi: 10.1007/s10827-011-0376-2 Closed Access publication (postprint: Open Access publication).

Weber, A. I., and Pillow, J. W. (2017). Capturing the dynamical repertoire of single neurons with generalized linear models. Neural computation 29(12): 3260-3289. doi: 10.1162/neco_a_01021 Closed Access publication (preprint: arxiv:1602.07389 Open Access publication).

Zhao, M., and Iyengar, S. (2010). Nonconvergence in logistic and poisson models for neural spiking. Neural computation 22(5): 1231-1244. doi: 10.1162/neco.2010.03-09-982 Closed Access publication.