Textbook

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 3.2. PSTH for an example of oscillating activity.
PSTH_osc.m.
Figure 3.5. Model of a spatio-temporal receptive field of a V1 simple cell.
spatial_AELIF_randomI.m
Figure 3.7. Spike-trains, ISI distributions, and statistics.
spikes_cv_isis.m
Figure 3.8. The ROC curve.
AELIF_noise_ROC.m
Figure 3.10. ROC analysis to reveal the stimulus-response distribution.
ROC_distributions.m

Figure 4.2. Stages of the action potential.
HH_old_base.m
Figure 4.3. Rate constants for the gating variables of the Hodgkin-Huxley model.
HHab_plotnewV.m
Figure 4.4. Steady state of the gating variables in the Hodgkin-Huxley model.
HHab_plotnewV.m
Figure 4.5. Dynamics of gating variables during a single spike.
HH_old_base.m
Figure 4.6. Type-II behavior of the Hodgkin-Huxley model.
HH_manyItrials.m
Figure 4.7. Firing rate curve of Hodgkin-Huxley model.
HH_new_f_I_bi.m
Figure 4.8. Anode break, a spike from hyperpolarization.
HH_old_anode_break_3plot.m
Figure 4.9. Subthreshold oscillations following step changes in current.
HH_old_sub_thresh.m
Figure 4.10. Resonant response to a frequency sweep or â€œZAPâ€ current.
HH_new_zap.m
Figure 4.11. The Connor-Stevens model.
CS_figure.m
Figure 4.12. Deinactivation of T-type calcium channels produces post-inhibitory rebound.
PIR_steps.m
Figure 4.15. bursting in a variant of the PR model.
PR_euler_final.m
PR_soma_gating.m
PR_dend_gating.m
Figure 4.16. Dynamics of a single burst.
PR_euler_final.m
PR_soma_gating.m
PR_dend_gating.m
Figure 4.17. I_h gating variables.
IH_PR_loop.m
PR_soma_gating.m
PR_dend_gating.m
Figure 4.18. Impact of the hyperpolarization-activated current (I_h) on bursting.
IH_PR_loop.m
PR_soma_gating.m
PR_dend_gating.m
Figure 4.19. Neural activity depends on location of inputs.
PR3branch.m
PR_soma_gating.m
PR_dend_gating.m

Figure 5.3. Model of synaptic transmission by graded release.
- graded_release.m
- PR_VS.mat
Figure 5.9A. The half-center oscillator.
coupled_PIR.m
Figure 5.9B. The tri-phasic rhythm.
three_coupled_PIR.m
Figure 5.11. Phase response curves of simulated neurons.
1. Hodgkin-Huxley model. Phase_Response_HH.m
2. Connor-Stevens model. Phase_Response_CS.m
3. The Pinsky-Rinzel model +I_h.
4. Thalamic rebound
  Phase_Response_PIR.m

Figure 6.1. Examples of input-output functions for firing rate models.
firing_rate_curves.m
Figure 6.4. Recurrent feedback strength determines the stable states of a circuit.
bistable1.m
Figure 6.5. Persistent activity at lower rates via synaptic saturation.
bistable2.m
Figure 6.6. Activity states in a circuit with excitatory feedback and short-term synaptic dynamics.
bistable_fac_dep.m
Figure 6.7. Connection between integration and parametric memory.
integrator_cartoon.m
Figure 6.9. Dynamics of a decision-making circuit.
decision_making_dynamics_figure.m
Figure 6.10. The speed-accuracy tradeoff.
decision_making_vary_g_stim.m
Figure 6.12. Gamma frequency oscillations via a PING mechanism.
oscillator_single_stim.m
Figure 6.13. High-frequency oscillations in an all-inhibitory network with delayed feedback.
ineuron_oscillator_single.m
Figure 6.14. Background input affects the frequency and amplitude of an oscillating circuit.
EI_oscillator.m
Figure 6.18. Pinwheel arrangement of neurons in V1.
pin_wheel.m
Figure 6.19. Local excitation and surround inhibition produced in the ring model.
ring_attractor.m
Figure 6.20. Stationary and non-stationary states in a ring model.
ring_attractor.m
Figure 6.21. Angular integration in a ring attractor network.
angular_integration.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 3.2 (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 8.4, Chapter 8, balanced excitatory-inhibitory chaotic network
highD_chaos.m

Department of Biology