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Dynamical Systems in Neuroscience:

Dynamical Systems in Neuroscience:

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One-Dimensional <strong>Systems</strong> 57currents<strong>in</strong>wardoutwardgat<strong>in</strong>gactivation, m<strong>in</strong>activation, hI Na,pnegativeconductanceI hI(V)I(V)VVI KI KirI(V)I(V)VnegativeconductanceVFigure 3.3: Typical currentvoltage(I-V) relations of the fourcurrents considered <strong>in</strong> this chapter.Shaded boxes correspond tonon-monotonic I-V relations hav<strong>in</strong>ga region of negative conductance(I ′ (V ) < 0) <strong>in</strong> the biophysicallyrelevant voltage range.us<strong>in</strong>g any dynamical systems theory. The models might also appear too simple tomathematicians, who can easily understand their dynamics just by look<strong>in</strong>g at thegraphs of the right-hand side of (3.4) without us<strong>in</strong>g any electrophysiological <strong>in</strong>tuition.In fact, the models provide an <strong>in</strong>valuable learn<strong>in</strong>g tool, s<strong>in</strong>ce they establish a bridgebetween electrophysiology and dynamical systems.In Fig. 3.3 we plot typical steady-state current-voltage (I-V) relations of the fourcurrents considered above. Notice that the I-V curve is non-monotonic for I Na,p andI Kir but monotonic for I K and I h , at least <strong>in</strong> the biophysically relevant voltage range.This subtle difference is an <strong>in</strong>dication of the fundamentally different roles these currentsplay <strong>in</strong> neuron dynamics: The I-V relation <strong>in</strong> the first group has a region of “negativeconductance”, i.e., I ′ (V ) < 0, which creates positive feedback between the voltage andthe gat<strong>in</strong>g variable (Fig. 3.4), and plays an amplify<strong>in</strong>g role <strong>in</strong> neuron dynamics. Werefer to such currents as amplify<strong>in</strong>g currents. In contrast, the currents <strong>in</strong> the secondgroup have negative feedback between voltage and gat<strong>in</strong>g variable, and they often result<strong>in</strong> damped oscillation of the membrane potential, as we show <strong>in</strong> the next chapter. Werefer to such currents as resonant currents. Most neural models <strong>in</strong>volve a comb<strong>in</strong>ationof at least one amplify<strong>in</strong>g and one resonant current, as we discuss <strong>in</strong> Chap. 5. Theway these currents are comb<strong>in</strong>ed determ<strong>in</strong>es whether the neuron is an <strong>in</strong>tegrator or aresonator.3.1.2 Leak + <strong>in</strong>stantaneous I Na,pTo ease our <strong>in</strong>troduction <strong>in</strong>to dynamical systems, we will use the I Na,p -modelC ˙V = I − g L (V − E L ) −<strong>in</strong>stantaneous I Na,p{ }} {g Na m ∞ (V ) (V − E Na ) (3.5)withm ∞ (V ) = 1/(1 + exp {(V 1/2 − V )/k})

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