---
title: "Suggested further readings"
source: Neuromatch Computational Neuroscience
source_url: https://github.com/NeuromatchAcademy/course-content
licence: CC-BY-4.0
domain: foundations
subdomain: neuromatch-compneuro
date_added: 2026-04-25
---

# 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: [proceedings.neurips.cc/paper/2007/hash/46ba9f2a6976570b0353203ec4474217-Abstract.html](https://proceedings.neurips.cc/paper/2007/hash/46ba9f2a6976570b0353203ec4474217-Abstract.html).

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](https://doi.org/10.1016/j.pneurobio.2019.01.008) {{ closed_access }} (postprint: [europepmc.org/articles/pmc8454059?pdf=render](https://europepmc.org/articles/pmc8454059?pdf=render) {{ open_access }}).

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](https://doi.org/10.1523/ENEURO.0506-19.2020) {{ open_access }}.

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](https://doi.org/10.1016/j.neuron.2017.03.025) {{ open_access }}.

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](https://doi.org/10.7554/eLife.47012) {{ open_access }}.

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: [papers.nips.cc/paper/2011/hash/7143d7fbadfa4693b9eec507d9d37443-Abstract.html](https://papers.nips.cc/paper/2011/hash/7143d7fbadfa4693b9eec507d9d37443-Abstract.html).

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](https://doi.org/10.1371/journal.pcbi.1006291) {{ open_access }}.

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](https://doi.org/10.1371/journal.pcbi.1003143) {{ open_access }}.

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](https://doi.org/10.1088/0954-898X/15/4/002) {{ closed_access }}.

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](https://doi.org/10.1016/j.neuron.2016.12.036) {{ open_access }}.

Park, I. M., and Pillow, J. (2011). Bayesian spike-triggered covariance analysis. Advances in neural information processing systems, 24. url: [papers.nips.cc/paper/2011/hash/6395ebd0f4b478145ecfbaf939454fa4-Abstract.html](https://papers.nips.cc/paper/2011/hash/6395ebd0f4b478145ecfbaf939454fa4-Abstract.html).

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](https://doi.org/10.1371/journal.pcbi.1002219) {{ open_access }}.

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](https://doi.org/10.1038/nn.3800) {{ closed_access }} (postprint: [europepmc.org/articles/pmc4176983?pdf=render](https://europepmc.org/articles/pmc4176983?pdf=render) {{ open_access }}).

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](https://doi.org/10.1523/JNEUROSCI.3305-05.2005) {{ open_access }}.

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](https://doi.org/10.1038/nature07140) {{ closed_access }} (postprint: [europepmc.org/articles/pmc2684455?pdf=render](https://europepmc.org/articles/pmc2684455?pdf=render) {{ open_access }}).

Pillow, J., and Scott, J. (2012). Fully Bayesian inference for neural models with negative-binomial spiking. Advances in neural information processing systems, 25. url: [proceedings.neurips.cc/paper/2012/hash/b55ec28c52d5f6205684a473a2193564-Abstract.html](https://proceedings.neurips.cc/paper/2012/hash/b55ec28c52d5f6205684a473a2193564-Abstract.html).

Shlens, J. (2014). Notes on generalized linear models of neurons. arXiv preprint [arxiv:1404.1999](https://arxiv.org/abs/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](https://doi.org/10.1371/journal.pcbi.1002775) {{ open_access }}.

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](https://doi.org/10.1152/jn.00697.2004) {{ closed_access }}.

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](https://doi.org/10.1007/s10827-011-0376-2) {{ closed_access }} (postprint: [europepmc.org/articles/pmc3560841?pdf=render](https://europepmc.org/articles/pmc3560841?pdf=render) {{ open_access }}).

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](https://doi.org/10.1162/neco_a_01021) {{ closed_access }} (preprint: [arxiv:1602.07389](http://arxiv.org/abs/1602.07389) {{ open_access }}).

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](https://doi.org/10.1162/neco.2010.03-09-982) {{ closed_access }}.
