The name is absent



93

Table 3.8: Accuracy of coarsened full systems (top section) and reduced systems (bottom
section) for the projection interneuron AR-l-20-04-A with the MIG channel model. Simu-
lations were conducted using 2000 random current injections of duration and magnitude as
in §3.4.1, and
kv = kf for the reduced systems.

h (μm)

Speed-up

% Matched

% Mismatched

Γ

5

4.6×

74.1

49.2

0.595

2

2.0×

77.9

42.4

0.656

ʌ'l)

60

12.3×

87.8

22.3

0.821

75

8.1×

88.6

21.6

0.829

90

5.9×

82.4

12.2

0.849

105

4.3×

93.9

10.2

0.917

Table 3.9: Specifications and performance of reduced systems (kυ = kf) for the morpholo-
gies shown in Figure 3.10 using the MIG channel model. Cell mp_tb_40984_gcl is marked
with a ‘*’ because the reduced system became unstable before
kv was large enough to cap-
ture any significant dynamics, thus this was the only cell for which the reduced system
failed for the MIG channel model.

Cell

Model

B

N

N∕kv

Speed-up

Γ

AR-1-20-04-A

MIG

35

2233

120

18.6×

3.3×

0.933

951005a

MIG

44

1106

75

14.7×

4.3×

0.893

12299402

MIG

61

5021

150

33.4×

4.6×

0.882

100103a

MIG

32

2707

120

22.5×

4.0×

0.886

*mp_tb_40984_gc 1

MIG

54

2541

60

42.4×

14.7×

0.471

512882

MIG

35

4655

105

44.3×

9.2×

0.928

P8-DEV66

MIG

47

1712

75

22.8×

6.7×

0.856

systems are still successful at capturing the spiking dynamics of most cells. However,
larger reduced systems must be used to resolve these dynamics, so smaller speed-ups
are observed. The MIG channel model also shows us a case of utter failure for the first
time: for the retinal ganglion cell in Figure 3.10e, we find that numerical instabilities,
most likely due to poor snapshots, in the reduced system occur for larger dimensions.



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