12.07.2015 Views

Dynamical Systems in Neuroscience:

Dynamical Systems in Neuroscience:

Dynamical Systems in Neuroscience:

SHOW MORE
SHOW LESS
  • No tags were found...

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

82 One-Dimensional <strong>Systems</strong>membrane potential, V (mV)40200-20-40-60-80-100-120excited statesthreshold statessaddle-node(fold) bifurcationrest statessaddle-node(fold) bifurcationI (V)16-890membrane potential, V (mV)40200-20-40-60-80-100-120-1000 -500 0<strong>in</strong>jected dc-current, I (pA)-1000 -500 0<strong>in</strong>jected dc-current, I (pA)Figure 3.32: Bifurcation diagram of the I Na,p -model (3.5).the phase portrait of the system, as we illustrate <strong>in</strong> Fig. 3.32, right. Each po<strong>in</strong>t wherethe branches fold (max or m<strong>in</strong> of I ∞ (V )) corresponds to a saddle-node bifurcation.S<strong>in</strong>ce there are two such folds, at I = 16 pA and at I = −890 pA, there are two saddlenodebifurcations <strong>in</strong> the system. The first one, studied <strong>in</strong> Fig. 3.25, corresponds to thedisappearance of the rest state. The other one, illustrated <strong>in</strong> Fig. 3.33, correspondsto the disappearance of the excited state. It occurs because I becomes so negativethat the Na + <strong>in</strong>ward current is no longer strong enough to balance the leak outwardcurrent and the negative <strong>in</strong>jected dc-current to keep the membrane <strong>in</strong> the depolarized(excited) state.Below the reader can f<strong>in</strong>d more examples of bifurcation analysis of the I Na,p - andI Kir -models, which have non-monotonic I-V relations and can exhibit multi-stabilityof states. The I K - and I h -models have monotonic I-V relations and hence only oneequilibrium state. These models cannot have saddle-node bifurcations, as the readeris asked to prove <strong>in</strong> Ex. 13 and 14.3.3.8 Quadratic <strong>in</strong>tegrate-and-fire neuronLet us consider the topological normal form for the saddle-node bifurcation (3.9). From0 = I + V 2 we f<strong>in</strong>d that there are two equilibria, V rest = − √ |I| and V thresh = + √ |I|when I < 0. The equilibria approach and annihilate each other via saddle-node bifurcationwhen I = 0, so there are no equilibria when I > 0. In this case, ˙V ≥ I and V (t)<strong>in</strong>creases to <strong>in</strong>f<strong>in</strong>ity. Because of the quadratic term, the rate of <strong>in</strong>crease also <strong>in</strong>creases,result<strong>in</strong>g <strong>in</strong> a positive feedback loop correspond<strong>in</strong>g to the regenerative activation ofNa + current. In Ex. 15 we show that V (t) escapes to <strong>in</strong>f<strong>in</strong>ity <strong>in</strong> a f<strong>in</strong>ite time, whichcorresponds to the up-stroke of the action potential. The same up-stroke is generatedwhen I < 0, if the voltage variable is pushed beyond the threshold value V thresh .

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!