The name is absent

39

Figure 2.6: Renderings of the three morphologies used in this paper: A) a forked neuron; B)
cell AR-l-20-04-A, a projection neuron from rat entorhinal cortex (Martinez); C) cell n408, a rat
hippocampal cell from region CAl (Pyapali et al., 1998).

quite rapidly (Figure 2.7A). To emphasize this, we note that the smallest floating point
number e_mach such that 1 + e_mach gives a value different than 1 is commonly called
machine precision and has a value of about IO^-15. Indeed, σ_n < e_mach for n > 65,
indicating that a reduction of at least 20-fold can be obtained. Numerically we observe
in Figure 2.7B that, compared to the computed quasi-active soma potential, nearly 5
digits of accuracy can be obtained using only 12 HSVs, a reduction of fully two orders
of magnitude. The BT dynamics also track the dynamics of the soma potential from
the nonlinear system qualitatively well, though the error is larger (Figure 2.7C).

Tonic synapses have a significant effect on the dynamics, but no effect on the

accuracy of the reduced model versus the quasi-active one. We demonstrate the
impact of tonic synapses by randomly choosing 10% of the compartments to have

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