# A textbook by Paul Miller, Brandeis
University (MIT Press 2018)

## Chapter 7: Codes for Figures and Tutorials

- Figure 7.1. Stable and unstable fixed points in a single-variable firing rate model.

drdt_plot.m
- Figure 7.2. Increase of feedback strength causes loss of low firing-rate fixed point.

drdt_plot_manyW.m
- Figure 7.3. A bifurcation curve shows the stable and unstable states as a function of a control parameter.

bifurcation_varyW.m
- Figure 7.4. Nullclines showing the fixed points of a bistable system with threshold-linear firing-rate units.

nullcline_bistable.m
- Figure 7.5. Vector fields indicate the dynamics of a system on a phase plane.

vector_field.m
- Figure 7.6. The surprising response to inputs of an inhibition-stabilized network.

vector_field.m
- Figure 7.8. Transitions between attractor states in a bistable system.

bistable_percept.m
- Figure 7.9. The FitzHugh Nagumo model.

FHNmodel.m
- Figure 7.10. The FitzHugh-Nagumo model behaves like a Type-II neuron.

FHNmodel.m
- Figure 7.11. Example of a saddle point in a circuit with cross-inhibition.

vector_field_saddle.m
- Figure 7.12. Chaotic behavior of a circuit of three firing-rate model units.

chaotic_3units.m
- Figure 7.13. Chaotic neural activity in high dimensions.

highD_chaos.m
- Figure 7.14. The divergence of firing rates grows exponentially following a small perturbation in a chaotic system.

highD_chaos.m
- Figure 7.16. Analysis of avalanche data from a simple â€˜birth-deathâ€™ model of neural activity.

avalanche_data.m

Last modified August 22, 2017
Paul Miller, pmiller@brandeis.edu
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