Dynamical Systems in Neuroscience:

Dynamical Systems in Neuroscience: Dynamical Systems in Neuroscience:

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12.07.2015 Views

274 ExcitabilityK + activation gate, n0.10.05V-nullclineseparatrixn-nullclinesaddleADP-65 -60 -55 -50 -45membrane potential, V (mV)membrane potential, V (mV)0-20-40-60ADP-800 10 20 30 40time (ms)Figure 7.53: A long afterdepolarization (ADP) in the I Na,p +I K -model without any slowcurrents. Parameters as in Fig. 6.52.a long-lasting effect appears because the trajectory follows the separatrix, comes closeto the saddle point, and spends some time there before returning to the stable restingstate.An example in Fig. 7.54 shows the membrane potential of a model neuron slowlypassing through a saddle-node on invariant circle bifurcation. Because the vector fieldis small at the bifurcation, which takes place around t = 70 ms, the membrane potentialis slowly increasing along the limit cycle and then slowly decreasing along the locus ofstable node equilibria, thereby producing a slow ADP. In Chap. 9 we will show that suchADPs exist in 4 out of 16 basic types of bursting neurons, including thalamocorticalrelay neurons and R 15 bursting cells in the abdominal ganglion of the mollusk Aplysia.

Excitability 275membrane potential, V (mV)0-10-20-30-40-50-60saddle-node on invariant circlebifurcationinvariant circleADPsaddlenode-70-800 20 40 60 80 100 120 140 160 180 200time (ms)Figure 7.54: Afterdepolarization in the I Na,p +I K -model passing slowly through saddlenodeon invariant circle bifurcation, as the magnitude of the injected current rampsdown.Review of Important Concepts• A neuron is excitable because, as a dynamical system, it is near abifurcation from resting to spiking activity.• The type of bifurcation determines the neuron’s computational properties,summarized in Fig. 7.15.• Saddle-node on invariant circle bifurcation results in Class 1 excitability:the neuron can fire with arbitrary small frequency andencode the strength of input into the firing rate.• Saddle-node off invariant circle and Andronov-Hopf bifurcations resultin Class 2 excitability: the neuron can fire only within a certainfrequency range.• Neurons near saddle-node bifurcation are integrators: they preferhigh-frequency excitatory input, have well-defined thresholds, andfire all-or-none spikes with some latencies.• Neurons near Andronov-Hopf bifurcation are resonators: they haveoscillatory potentials, prefer resonant-frequency input, and can easilyfire post-inhibitory spikes.

274 ExcitabilityK + activation gate, n0.10.05V-nullcl<strong>in</strong>eseparatrixn-nullcl<strong>in</strong>esaddleADP-65 -60 -55 -50 -45membrane potential, V (mV)membrane potential, V (mV)0-20-40-60ADP-800 10 20 30 40time (ms)Figure 7.53: A long afterdepolarization (ADP) <strong>in</strong> the I Na,p +I K -model without any slowcurrents. Parameters as <strong>in</strong> Fig. 6.52.a long-last<strong>in</strong>g effect appears because the trajectory follows the separatrix, comes closeto the saddle po<strong>in</strong>t, and spends some time there before return<strong>in</strong>g to the stable rest<strong>in</strong>gstate.An example <strong>in</strong> Fig. 7.54 shows the membrane potential of a model neuron slowlypass<strong>in</strong>g through a saddle-node on <strong>in</strong>variant circle bifurcation. Because the vector fieldis small at the bifurcation, which takes place around t = 70 ms, the membrane potentialis slowly <strong>in</strong>creas<strong>in</strong>g along the limit cycle and then slowly decreas<strong>in</strong>g along the locus ofstable node equilibria, thereby produc<strong>in</strong>g a slow ADP. In Chap. 9 we will show that suchADPs exist <strong>in</strong> 4 out of 16 basic types of burst<strong>in</strong>g neurons, <strong>in</strong>clud<strong>in</strong>g thalamocorticalrelay neurons and R 15 burst<strong>in</strong>g cells <strong>in</strong> the abdom<strong>in</strong>al ganglion of the mollusk Aplysia.

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