Textbook
An Introductory Course in Computational Neuroscience
by Paul Miller, Brandeis University (MIT Press 2018)
Available from MIT Press.
Table of Contents
Codes for Figures and Tutorials
Expand All
- Figure 2.2. Exponential decay of the membrane potential.
exponential.m
- Figure 2.3. Behavior of the leaky integrate-and-fire neuron.
LIF_model.m
- Figure 2.4. Three methods for incorporating a refractory period in an LIF model.
LIF_model_3trefs.m
- Figure 2.5. Spike-rate adaptation in a model LIF neuron.
LIF_model_sra.m
- Figure 2.6. Spike generation in the ELIF model.
LIFspike_ELIF.m
- Figure 2.7. Response of the AELIF model to a current step.
AELIF_code.m
- Figure 2.8. Firing rate curve for AELIF model.
AELIF_code_loop.m
- Figure 2.9. Response of an altered AELIF model to produce a refractory period.
Tutorial_2_3_Q3.m
- 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 ‘birth-death’ model of neural activity.
avalanche_data.m
- Figure 8.1. Alternative forms of Hebbian plasticity.
plasticity_four_rules.m
- Figure 8.2. Plasticity rule with quadratic dependence on postsynaptic firing rate.
quadratic_plasticity_figure.m
- Figure 8.3. Pattern completion and pattern separation of corrupted exemplars following Hebbian learning.
Tutorial_8_1.m
- Figure 8.6. Encoding of sequences and paired associations using triplet STDP.
STDP_recurrent_final.m
- Figure 8.7. NMDA receptor activation as a temporal-order detector.
NMDA_activation.m
- Figure 8.8. Example of the need for homeostasis in a neuron that responds to coincident inputs.
CS_3spikesin.m
- Figure 8.9. Need for homeostasis to produce intrinsic bursts.
PR_euler_5cellversions.m
- Figure 8.10. Need for homeostasis to control frequency of an intrinsically bursting neuron.
- Figure 8.13. Rate-dependence of STDP with nearest-neighbor rule.
STDP_Poisson_nn.m
- Required for Tutorial 3.1, Chapter 3, spike-triggered average
STA.m
- Required for Tutorial 3.1, Chapter 3, downsampling function
expandbin.m
- Required for Tutorial 3.2 (Part B), Chapter 3, produces Poisson spike counts
alt_poissrnd.m
- Required for Tutorial 4.2, Chapter 4, contains gating variables for post-inhibitory rebound
PIR_Vdependence.m
- Required for Tutorial 4.2, Chapter 4, post-inhibitory rebound
PIR.m
- Required for Tutorial 8.4, Chapter 8, balanced excitatory-inhibitory chaotic network
highD_chaos.m