---
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

### Text book:

Dayan, P., and Abbott, L. F. (2005). Chapter 5. In: Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT press.

Gerstner, W., Kistler, W. M., Naud, R., and Paninski, L. (2014). Chapter In: Dynamics: From single neurons to networks and models of cognition. Cambridge University Press.

## Hodgkin-Huxley neuron model (how spike is generated):

Hodgkin, A. L., and Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology 117(4): 500. doi: [10.1113/jphysiol.1952.sp004764](https://doi.org/10.1113/jphysiol.1952.sp004764) {{ open_access }}.

Hodgkin, A. L., Huxley, A. F., and Katz, B. (1952). Measurement of current-voltage relations in the membrane of the giant axon of Loligo. The Journal of physiology 116(4): 424. doi: [10.1113/jphysiol.1952.sp004716](https://doi.org/10.1113/jphysiol.1952.sp004716) {{ open_access }}.

### But what is a spike

Izhikevich, E. M., and FitzHugh, R. (2006). Fitzhugh-nagumo model. Scholarpedia 1(9): 1349. doi: [10.4249/scholarpedia.1349](http://doi.org/10.4249/scholarpedia.1349).

FitzHugh, R. (1955). Mathematical models of threshold phenomena in the nerve membrane. The bulletin of mathematical biophysics 17(4): 257-278. doi: [10.1007/BF02477753](https://doi.org/10.1007/BF02477753) {{ closed_access }}.

### Point neuron model

Brunel, N., and Van Rossum, M. C. (2007). Lapicque’s 1907 paper: from frogs to integrate-and-fire. Biological cybernetics 97(5): 337-339. doi: [10.1007/s00422-007-0190-0](https://doi.org/10.1007/s00422-007-0190-0) {{ closed_access }}.

Brunel, N., Hakim, V., and Richardson, M. J. (2014). Single neuron dynamics and computation. Current opinion in neurobiology 25: 149-155. doi: [10.1016/j.conb.2014.01.005](https://doi.org/10.1016/j.conb.2014.01.005) {{ closed_access }}.

Gerstner, W., and Naud, R. (2009). How good are neuron models?. Science 326(5951): 379-380. doi: [10.1126/science.1181936](https://doi.org/10.1126/science.1181936) {{ closed_access }} (preprint: [infoscience.epfl.ch/record/142067/files/Naud09.pdf](https://infoscience.epfl.ch/record/142067/files/Naud09.pdf) {{ open_access }}).

Jolivet, R., Kobayashi, R., Rauch, A., Naud, R., Shinomoto, S., and Gerstner, W. (2008). A benchmark test for a quantitative assessment of simple neuron models. Journal of neuroscience methods 169(2): 417-424. doi: [10.1016/j.jneumeth.2007.11.006](https://doi.org/10.1016/j.jneumeth.2007.11.006) {{ closed_access }} (postprint: [infoscience.epfl.ch/record/118680/files/Jolivet08.pdf](https://infoscience.epfl.ch/record/118680/files/Jolivet08.pdf) {{ open_access }}).

Jolivet, R., Lewis, T. J., and Gerstner, W. (2004). Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. Journal of neurophysiology 92(2): 959-976. doi: [10.1152/jn.00190.2004](https://doi.org/10.1152/jn.00190.2004) {{ open_access }}.

### Extending simplified neuron models

Larkum, M. E., Nevian, T., Sandler, M., Polsky, A., and Schiller, J. (2009). Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle. Science 325(5941): 756-760. doi: [10.1126/science.1171958](https://doi.org/10.1126/science.1171958) {{ closed_access }}.

Poirazi, P., Brannon, T., and Mel, B. W. (2003). Pyramidal neuron as two-layer neural network. Neuron 37(6): 989-999. doi: [10.1016/S0896-6273(03)00149-1](https://doi.org/10.1016/S0896-6273(03)00149-1) {{ open_access }}.

### Synapse models

Kuhn, A., Aertsen, A., and Rotter, S. (2004). Neuronal integration of synaptic input in the fluctuation-driven regime. Journal of Neuroscience 24(10): 2345-2356. doi: [10.1523/JNEUROSCI.3349-03.2004](https://doi.org/10.1523/JNEUROSCI.3349-03.2004) {{ open_access }}.

### Short-term dynamics of synapses

Markram, H., and Tsodyks, M. (1996). Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature 382(6594): 807-810. doi: [10.1038/382807a0](https://doi.org/10.1038/382807a0) {{ closed_access }}.

Markram, H., Wang, Y., and Tsodyks, M. (1998). Differential signaling via the same axon of neocortical pyramidal neurons. Proceedings of the National Academy of Sciences 95(9): 5323-5328. doi: [10.1073/pnas.95.9.5323](https://doi.org/10.1073/pnas.95.9.5323) {{ open_access }}.

Stevens, C. F., and Wang, Y. (1995). Facilitation and depression at single central synapses. Neuron 14(4): 795-802. doi: [10.1016/0896-6273(95)90223-6](https://doi.org/10.1016/0896-6273(95)90223-6) {{ open_access }}.

### Synaptic timing dependent plasticity

Abbott, L. F., and Nelson, S. B. (2000). Synaptic plasticity: taming the beast. Nature neuroscience 3(11): 1178-1183. doi: [10.1038/81453](https://doi.org/10.1038/81453) {{ closed_access }}.

Bi, G. Q., and Poo, M. M. (1998). Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of neuroscience 18(24): 10464-10472. doi: [10.1523/JNEUROSCI.18-24-10464.1998](https://doi.org/10.1523/JNEUROSCI.18-24-10464.1998) {{ open_access }}.

Song, S., Miller, K. D., and Abbott, L. F. (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature neuroscience 3(9): 919-926. doi: [10.1038/78829](https://doi.org/10.1038/78829) {{ closed_access }}.
