### Table 4: I/O and I/S/O representation of discrete-time linear systems

### Table 1: Simulation vs. discrete{time analysis: E[W ] Simulation Analysis

1999

"... In PAGE 8: ... Note the bath tub-like shape of the waiting time curve, which is characteristic for batch service systems. In Table1 , the mean waiting times gained from continuous{time discrete{event sim- ulation are given. We compare them to the values derived with our proposed method.... ..."

Cited by 2

### Table 1: Simulation vs. discrete{time analysis: E[W ]

1999

"... In PAGE 8: ... Note the bath tub-like shape of the waiting time curve, which is characteristic for batch service systems. In Table1 , the mean waiting times gained from continuous{time discrete{event sim- ulation are given. We compare them to the values derived with our proposed method.... ..."

Cited by 2

### Table 4 Stochastic time delays imposed on the POTS

2000

"... In PAGE 25: ... Table4 shows the time values we have used for the numerical analysis of the telephony system specification. The table lists the mean durations (first column), the coefficient of variation of the distribution (second column) and the distribution function (last column).... ..."

Cited by 31

### Table 4 Stochastic time delays imposed on the POTS

"... In PAGE 23: ... Table4 shows the time values we have used for the numerical analysis of the tele- phony system speci cation. The table lists the mean durations ( rst two columns), the coe cient of variation of the distribution (third column) and the distribution function (last column).... ..."

### Table 1: Time-delayed performance

"... In PAGE 6: ... The operator uploads a target designation, and in the next download cycle receive images from the robot, already at that target, perhaps even with its sample collection equip- ment deployed on the feature. We introduced a 6-minute delay (typical for Mars) in our communication system and drove the robot; this run is shown in Table1 , The rover first makes a long traverse, losing its target after 40 meters and then spends time taking images in various directions (a pro- cess that should be performed in one command cycle), before it drives to targets of interest. In the final drive of the field experiment we targeted the distinctive bumper of the command truck.... ..."

### Table 2.1: Discrete-time systems

### Table 1. History of the Time Delay

"... In PAGE 17: ... Table1 |Continued Reference Data Set Statistic Delay (days) Delay (years) Oscoz et al. 1996, Kundi c et al.... In PAGE 23: ... We follow exactly the analysis of L92, which is based on work by Edelson amp; Krolik (1988). A cross-correlation function was one of the rst statistical techniques used to nd the time delay (see Table1 ). The peak in the correlation between two signals in time should be a reasonable estimate of the delay between them.... In PAGE 35: ... New features have appeared that allow us to improve our estimate of the time delay. To determine the time delay, we have chosen three of the techniques referred to in Table1 : PRH 2 analysis (Press, Rybicki, amp; Hewitt, 1992a,b, hereafter PRHa, PRHb; Rybicki amp; Press 1992; Rybicki amp; Kleyna 1994), dispersion analysis (Pelt et al. 1994, Pelt et al.... In PAGE 36: ... The possibilities for B0957+561 were realized immediately, and VLA and optical monitoring for the time delay began in 1979. Table1 lists measurements of the time delay of B0957+561, showing the literature reference, light curves, and estimate of the time delay in days and in years. Note that before 1989 the results were scattered in delay, with large errors.... ..."

### Table 2 Performance decrease of control circuits compared with data-based circuits

"... In PAGE 4: ... For the case of 40 input channels, we carried out simulations with lower input connectivity for input stream 1 while keeping the product of PSP amplitude and connection probability constant. The results about performance differences between data- based circuits and amorphous control circuits [see Table2 ] are largely invariant to these changes, even if the connection probabilities for external input neurons are scaled down to 1/5th of the previously given values.) This is roughly in the range suggested by experimental measurements of the variability of excitatory postsynaptic potentials (EPSPs) in simple cells of cat visual cortex with varying levels of lat- eral geniculate nucleus (LGN) stimulation (Ferster 1987) and cross- correlation experiments between monosynaptically linked cells of the LGN and cat visual cortex (Tanaka 1983), which suggest that at least 10 LGN cells provide input to each simple cell.... In PAGE 9: ... In the last type of control circuit we replaced all dynamic synapses by static synapses (with weights rescaled so that the mean firing rate in layer 5 stayed fixed). A summary of the performance of all 7 different types of control circuits is shown in Table2 . The small-world property increases the performance of amorphous circuits to some extent, but a more important structural feature is the degree distribution defined by data-based circuits.... In PAGE 9: ... If this degree distribution matches the degree distribution of data-based circuits for each single layer, and therefore matches also the specific input and output topology of data-based circuits, the average performance is comparable with the performance of data-based circuits. Table2 also shows (see column 5) that a data-based assignment of synapse types (according to Table 1) is essential for good computational performance. The last column shows that circuits with static synapses also have inferior computational properties.... In PAGE 10: ...oefficients for least square fits for sums of Gaussians and power law distributions are gt;0.96 and lt;0.08, respectively. Thus, none of these circuits is scale free, which shows that their difference in performance cannot be explained on the basis of this concept. The computational analysis (see Table2 ) implies that the varying locations of peaks for different layers of data-based circuits are essential for their superior computational performance. Page 10 of 14 Lamina-Specific Cortical Microcircuit Models d... In PAGE 13: ...eural systems in vivo (Fig. 11). We have also analyzed which aspect of the connectivity structure of data-based laminar circuits is responsible for their better computational perfor- mance. We have arrived (on the basis of the results reported in Table2 ) at the conclusion that their particular distribution of degrees of nodes (relative to circuit inputs and projection neurons) is primarily responsible, more so than the small-world property of data-based circuits. We propose that this computa- tional superiority of laminar circuits can be understood in terms of the properties of the dynamical system, which is defined by such microcircuit models.... ..."

### Table 2 Performance decrease of control circuits compared with data-based circuits

2007

"... In PAGE 4: ... For the case of 40 input channels, we carried out simulations with lower input connectivity for input stream 1 while keeping the product of PSP amplitude and connection probability constant. The results about performance differences between data- based circuits and amorphous control circuits [see Table2 ] are largely invariant to these changes, even if the connection probabilities for external input neurons are scaled down to 1/5th of the previously given values.) This is roughly in the range suggested by experimental measurements of the variability of excitatory postsynaptic potentials (EPSPs) in simple cells of cat visual cortex with varying levels of lat- eral geniculate nucleus (LGN) stimulation (Ferster 1987) and cross- correlation experiments between monosynaptically linked cells of the LGN and cat visual cortex (Tanaka 1983), which suggest that at least 10 LGN cells provide input to each simple cell.... In PAGE 9: ... In the last type of control circuit we replaced all dynamic synapses by static synapses (with weights rescaled so that the mean firing rate in layer 5 stayed fixed). A summary of the performance of all 7 different types of control circuits is shown in Table2 . The small-world property increases the performance of amorphous circuits to some extent, but a more important structural feature is the degree distribution defined by data-based circuits.... In PAGE 9: ... If this degree distribution matches the degree distribution of data-based circuits for each single layer, and therefore matches also the specific input and output topology of data-based circuits, the average performance is comparable with the performance of data-based circuits. Table2 also shows (see column 5) that a data-based assignment of synapse types (according to Table 1) is essential for good computational performance. The last column shows that circuits with static synapses also have inferior computational properties.... In PAGE 10: ...oefficients for least square fits for sums of Gaussians and power law distributions are gt;0.96 and lt;0.08, respectively. Thus, none of these circuits is scale free, which shows that their difference in performance cannot be explained on the basis of this concept. The computational analysis (see Table2 ) implies that the varying locations of peaks for different layers of data-based circuits are essential for their superior computational performance. 158 Lamina-Specific Cortical Microcircuit Models d... In PAGE 13: ...eural systems in vivo (Fig. 11). We have also analyzed which aspect of the connectivity structure of data-based laminar circuits is responsible for their better computational perfor- mance. We have arrived (on the basis of the results reported in Table2 ) at the conclusion that their particular distribution of degrees of nodes (relative to circuit inputs and projection neurons) is primarily responsible, more so than the small-world property of data-based circuits. We propose that this computa- tional superiority of laminar circuits can be understood in terms of the properties of the dynamical system, which is defined by such microcircuit models.... ..."