A Systematic Study of the Input/Output Properties 149 
A Systematic Study of the Input/Output Properties 
of a 2 Compartment Model Neuron 
With Active Membranes 
Paul Rhodes 
University of California, San Diego 
ABSTRACT 
The input/output properties of a 2 compartment model neuron are systematically 
explored. Taken from the work of MacGregor (MacGregor, 1987), the model neuron 
compartments contain several active conductances, including a potassium conductance in 
the dendritic compartment driven by the accumulation of intradendritic calcium. 
Dynamics of the conductances and potentials are governed by a set of coupled first order 
differential equations which are integrated numerically. There are a set of 17 internal 
parameters to this model, specificying conductance rate constants, time constants, 
thresholds, etc. 
To study parameter sensitivity, a set of trials were run in which the input driving the 
neuron is kept fixed while each internal parameter is varied with all others left fixed. 
To study the input/output relation, the input to the rendrite (a square wave) was varied 
(in frequency and magnitude) while all internal parameters of the system were left fixed, 
and the resulting output firing rate and bursting rate was counted. 
The input/output relation of the model neuron studied turns out to be much more 
sensitive to modulation of certain dendritic potassium current parameters than to 
plasticity of synapse efficacy per se (the amount of current influx due to synapse 
activation). This would in turn suggest, as has been recently observed experimentally, 
that the potassium current may be as or more important a focus of neural plasticity than 
synaptic efficacy. 
INTRODUCTION 
In order to model biologically realistic neural systems, we will ultimately be seeking to 
construct networks with thousands of neurons and millions of interconnections. It is 
therefor desireable to employ basic units with sufficient computational simplicity to 
make meaningful simulations tractable, yet with sufficient fidelity to biological neurons 
that we may retain a hope of gleaning by these simulations something about the activity 
going on during biological information processing. 
150 Rhodes 
The types of neuron models employed in the computational neuroscience literature range 
from binary threshold units to sigmoid transfer functions to 1500 compartment neurons 
with Hodgkin-Huxley kinetics for a whole set of active conductanees and spines with 
rich internal structure. In principle, a model neuron's functional participation in the 
operation of a network may be fully characterized by a complete description of its 
transfer function, or input-output relation. This relation would necessarily be 
parameterized by a host of internal variables (which would include conductance rate 
constants and parameters defining the neuron's morphology) as well as a very rich space 
characterizing possible variations in input (including location of input in dentritic tree). 
In learning to judge which structural elements of highly realistic models must be 
preserved and which may be simplified, one approach will be to test the degree to which 
the input-output relation of the simplified neuron (given a physiologically relevant 
parameter range and input space) is sufficiently dose to the input-output properties of 
the highly realistic model. 
To define 'sufficiently close', we will ultimately refer to the operation of the network as 
a whole as follows: the transfer function of a simplified neuron model will be considered 
'sufficiently close' to a more realistic neuron model if a chosen information processing 
task carried out by the overall network is performed by a network built up of the 
simplified neurons in a manner close to that observed in a network of the more realistic 
neurons. 
We propose to begin by exploring the input/output properties of a greatly simplified 2 
compartment model neuron with active conductances. Even in this very simple structure 
there are many (17) internal parameters for things like time constants and activation rates 
of currents. We wish to understand the parameter sensitivity of this model system and 
characterize its input-output relation. 
1.0 DESCRIPTION OF THE MODEL NEURON 
THE MODEL NEURON CONSISTS OF A SOMA WITH A VOLTAGE-GATED 
POTASSIUM CONDUCTANCE AND A SINGLE COMPARTMENT DENDRITE 
WITH A VOLTAGE-GATED CALCIUM CONDUCTANCE AND A [CA]-GATED 
POTASSIUM CONDUCTANCE 
We will choose for this study a simple model neuron described by MacGregor (1987). It 
possesses a single compartment dendrite. This is viewed as a crude approximation to the 
lumped reduction of a dendritic tree. In this approximation, we are neglecting spatial and 
temporal summing of individual synaptic EPSP's distributed over a dendritic tree, as well 
as the spatial and temporal dispersion (smearing) due to transmission to the soma. The 
individual inputs we will be using are large enough to drive the soma to firing, and so 
would represent the summation of many relatively simultaneous individual EPSP's, 
perhaps as from the set of contacts upon a neuron's dendritic tree made by the 
arborization of one different axon. The dendritic membrane possesses a potassium 
conductance gated by intradendritic calcium concentration and a voltage gated calcium 
conductance. The soma contains its own voltage-gated potassium channels and 
membrane time constants. Electrical connection between soma and dendrite is expressed 
by an input impedance in each direction. The soma fires an action potential, simply 
expressed by raising its voltage to 50 mv for one msec after its internal voltage has been 
A Systematic Study of the Input/Output Properties 151 
driven to firing threshold. Calcium accumulation in the dendrite is modelled assuming 
accumulation proportional to calcium conductance. Calcium conductance itself increases 
in proportion to the difference between the dendrite's voltage and a threshold, and calcium 
is removed from the dendrite by means of an exponential decay. This system is modelled 
by a set of coupled fh'st order differential equations as follows: 
1.1 THE SET OF EQUATIONS GOVERNING THE DYNAMIC 
VARIABLES OF THIS MODEL 
The soma's voltage ES is governed by: 
dES/dt={ -ES +SOMAINPUT+GDS*(ED-ES)+GKS*(EK-ES)}/TS 
where SOMAINPUT is obtained by dividing the input current by the total resting 
conductance of the dendrite (therefor it has units of voltage). GDS is proportional to 
input resistance from dendrite to soma, and multiplies the difference between the 
dendrite's voltage ED and the soma's voltage ES; GKS is the soma's aggregate 
potassium conductance (modelled below); EK is the voltage of the pot_assium battery 
(assumed constant at -10mv); and TS is the soma's time constant. All potentials are 
relative to resting potential, and all conductances are dimensionless. 
The dendrite's voltage ED is govened by: 
dED/dt= { -ED+ DENDINPUT +G SD*(ES-ED )+GCA *(EC A-ED )+ GKD*(EK-ED)}/TD 
where DENDINPUT is obtained by dividing the input current by the total resting 
conductance of the dendrite and so has units of voltage. GSD is proportional to the 
input resistance from soma to dendrite, and hence multiplies the difference between ES 
and ED; GCA is the dendrite's calcium conductance (modelled below), ECA is the 
calcium battery (assumed constant at 50mv), and GKD is proportional to the dendrite's 
potassium conductance (modelled below). All potentials are relative to resting potential. 
The soma's voltage is raised artificially to 50my for 1 msec after the soma's voltage 
exceeds a (f'LXed) threshold, thus simplifying the action potential. 
The potassium conductance in the soma, GKS, is governed by: 
dGKS/dt= {-GKS+S*B }/TGK 
where S is 1 if an action potential has just fired and 0 otherwise, B is an activation rate 
constant governing the rate of increase of potassium conductance, and TGK is the time 
constant of the potassium conductance decay. This rather simplified picture of 
potassium conductance will be replaced by a more realistic version with a Markov state 
model of the potassium channel in a subsequent publication in preparation. For the 
present investigation then we are modelling the voltage dependence of the potassium 
conductance by the following: potassium conductance builds up by a fixed amount 
(proportional to B/TGK) during each action potential, and thereafter decays exponentially 
with time constant TGK. 
152 Rhodes 
The dendrite's calcium conductance is governed by: 
dGCA/dt ={-GCA+D*(ED-CSPIKETHRESH)} fIGCA 
dGCA/dt={-GCA/TGCA} 
ED>CSPIKETHRESH 
ED<CSPIKETHRESH 
where CSPIKETHRESH is the minimum dendritic voltage above which calcium 
conducting channels begin to be opened, D is an activation rate governing the rate of 
increase in calcium conductance, and TGCA is the time constant assumed to govern 
conductance decay when voltage is below threshold. 
The dendrite's internal calcium concentration [CA] is governed by: 
d[CA]/dt={-[CAI+A*GCA}/TCA 
where TCA is the time constant for the removal of internal CA, and A is a parameter 
governing the accumulation rate of increase of internal CA for a given conductance and 
time constant. A is inversely proportional to the effective relevant volume in which 
calcium is accumulating. An increase in internal calcium buffer would decrease the 
parameter A. 
Finally, the dendrite's potassium conductance is governed by: 
dGKD/dt ={-GKD+BD}/TGKD 
dGKD/dt={-GKD}/FGKD 
[CA]>CALCTHRESH 
[CA]<CALCTHRESH 
where CALCTHRESH is the internal calcium concentration threshold above which the 
calcium gated potassium channel begins to open, BD is the parameter governing the rate 
of increase of dendritic potassium conductance, and TGKD is the time constant 
governing the exponential decay of potassium conductance. 
This entire system of equations is taken from the work of MacGregor 
(MacGregor, 1987). 
The system of coupled in'st order differential equations is integrated using the exponential 
method, also discussed in MacGregor. Generally a 1 msec timestep is used, with a 
smaller timestep of. 1 msec used for the relaxation between the dendritic voltage ED and 
the somatic voltage ES. 
2.0 THE EFFECT OF CHANGES IN PARAMETERS (TIME 
CONSTANTS, CONDUCTANCE RATES, ETC.) ON THE 
MODEL NEURON'S INPUT-OUTPUT PROPERTIES WILL 
BE EXPLORED 
As is clear from a review of the above set of interrelated equations governing the 
dynamics of the state variables of the model neuron, there are quite a few externally 
specified parameters (17) even in such a simple model. Presumably the thresholds are 
fairly well measureable, and the rate constants and time constants may be specified by 
measurement of time courses in patch clamp experiments. We are nevertheless dealing 
with parameters of which some are thought to be variable and which are probably 
A Systematic Study of the Input/Output Properties 153 
modulated explicitly by normal mechanisms in neurons. Therefor we wish to explore 
the effect that variation of any of these parameters has on the input-output properties of 
the model neuron. In fact, we will find indication that the modulation of 
these parameters, in particular the rate constants governing the 
dendritic potassium current and internal calcium accumulation, may be 
very effective targets of neural plasticity. We find that the neuron's 
input-output properties are more sensitive to these parameters than to 
modulation of the efficacy of the synapse strength per se. 
2.1 PROTOCOL FOR SYSTEMATIC EXPLORATION OF THE 
EFFECT OF VARIATION IN THE MODEL'S PARAMETERS ON THE 
INPUT-OUTPUT PROPERTIES OF THE MODEL NEURON 
We started with the parameters all set to a set of benchmarks and drove the neuron with a 
constant input to the dendrite. (We could have driven the soma instead, or both soma 
and dendrite, and we could have chosen more complex input streams. See below for 
trials where we systematically vary the input but the parameter values are held steady.) 
The input was a steady command input of 35mv. The values of all the benchmark 
parameters are given in Table 1. 
We then systematically halved and doubled each of the 17 parameters in turn, while 
leaving all other parameters fixed. Note that in all cases and in fact with any driving 
input this model neuron lb'es in bursts. This is due to the long time course of the 
potassium current in the dendrite, which enforces a long refractory period (about 40- 
80msec) even during continuous stimulation. 
2.2 RESULTS OF SYSTEMATIC VARIATION OF PARAMETERS 
OF MODEL NEURON 
The results are summarized in the notes to Table 1. Following are several 
observations about the different parameters' varying degree of efficacy in modulation of 
the input-output function. 
1) The most striking finding is that variation of the activation rate of the potassium 
current, particularly the potassium current in the dendrite, is the most effective means of 
modulating the input-output properties of the model neuron. The transfer function is 
250% more sensitive to an increase in the [CA]-gated dendritic potassium current 
activation rate than it is to an increase in synaptic efficacy per se. 
2) Changing the time constant of the [CA]-gated potassium current in the dendrite is 
the only parameter change which effectively modulates the number of bursts per 
second (see Figure 1). Changing the time constant of the voltage-gated potassium 
current in the soma, does not have any effect on the number of bursts per second. 
154 Rhodes 
3.0 MEASUREMENT OF THE INPUT/OUTPUT RELATION 
OF THE MODEL NEURON 
The input/output relation was determined by the following protocol: The input was 
supplied in the form of a square wave of current injected into the dendritie compartment, 
and the frequency of the pulses and their magnitude was systematically varied. 
The output of the soma, in the form of action potentials fired per second, was plotted 
against the input rate, defined as the product of the square wave frequency and the 
magnitude of the injected current. The duration of pulses was kept fixed at 20 msec (but 
see below), all internal parameters were f'Lxed at their benchmark levels. 
3.1 THE SHAPE OF THE INPUT/OUTPUT RELATION 
Figure 2 depicts the above described plot in the case where all the internal parameters 
were fixed at purported "benchmark" values except for the parameters governing 
intradendritic calcium accumulation.. It is clearly not strictly monotonic (there are 
resonance points) though a smoothed version is monotonic, and it does not faithfully 
render a sigmoid. 
3.2 THE INPUT/OUTPUT RELATION IS UNCHANGED IF THE 
SQUARE SHAPE OF THE EPSP DRIVING THE DENDRITE IS 
REPLACED BY AN ALPHA FUNCTION 
The trials in this study were largely conducted using a square wave as the input driving 
the dendritic compartment. In order to check whether the unphysical square shape of the 
envelope of this current injection was coloring the results, the input/output relation was 
measured in a set of trials wherein the alpha function commonly used to model the time 
course of EPSP's replaced the square pulse. The total current injected per pulse was kept 
uniform. The results, shown in Figure 3, are surprising: The 
input/output relation was almost completely unaltered by the 
substitution. This suggests that the detailed shape and fourier 
spectrum of the time course of synaptic input has nearly no effect of 
the neuron's output. Thus it is suggested that very adequate models can be built 
without the need for a strict modelling of the synaptic EPSP. I expect this effect is due 
to the temporal integration ongoing in the summation of input to this system, which 
blurs the exact shape of any input envelope. 
3.3 MODULATION OF THE INPUT/OUTPUT RELATION BY 
VARIATION OF INTERNAL MODEL PARAMTERS 
Figure 1 portrays the input/output relation measured in three cases in which all internal 
parameters are identical except the rate of accumulation of intradendric calcium. The 
lower curve is the case where the calcium accumulation rate is highest. Since [Ca] 
accumulation drives the dendritic potassium current, the activation of which in turn 
hyperpolarizes the dendrite and thus indirecfiy suppresses firing in the soma, we expect 
output in this case to be lower for a given input as is indeed the result observed. Note 
that the parameter being varied would be expected to be inversely proportional to the 
amount of available intradendritic calcium buffer. Hence the amount of 
A Systematic Study of the Input/Output Properties 155 
intradendritic buffer has a profound ability to modulate the transfer 
function of the system. 
4.0 CONCLUSIONS 
As regards the shape of the transfer function itself, we have found it to be non- 
monotonic (there are resonance points) unless it is smoothed. The shape of the transfer 
function appears little effected by the envelope of the EPSP (i.e. square pulse input 
produces nearly the same transfer function as the case where alpha functions are 
substituted for the square pulses in modelling the EPSP). 
A parameter sensitivity analysis of a 2 compartment model neuron with active 
membranes reveals some unexpected results. For example, the input/output (transfer) 
function of the neuron is 250% more sensitive to the activation rate of the [CA]-gated 
dendritic potassium current than it is to synaptic efficacy per se. This in turn suggests 
that, as has indeed been observed (Alkon etal, 1988; Hawkins, 1989; Olds et al, 1989), 
nature might employ mechanisms other than simply increasing synaptic conductance 
during the EPSP to enhance the efficacy of the transfer function. 
References
Alkon, D.L. et al, J. Neurochemistry, Volume 51,903, (1988). 
Hawkins, R. D. in Computational Models of Learning in Simple Neural Systems, 
Hawkins and Bower, Eds., Academic Press, (1989). 
MacGregor, R., Neural and Brain Modelling, Academic Press, (1988). 
Olds, J. L. et al, Science, Volume 245, 866, (1989). 
TABLE 1 
RESULTS OF PARAMETER SENSITIVITY ANALYSIS 
PROTOCOL: EACH OF THE 17 INTERNAL PARAMETERS OF THE MODEL 
NEURON WAS VARIED IN TURN, WHILE ALL THE OTHERS WERE KEPT 
FIXED AT BENCHMARK VALUES. THE DENDRITE WAS DRIVEN IN 
EACH CASE WITH A STEADY FIXED INPUT AND THE RESULTING 
BURSTING RATE AND FIRING RATE WAS COUNTED. IN THE FINAL 
TRIAL, ALL THE PARAMETERS WERE LEFT FIXED AND THE INPUT 
MAGNITUDE WAS VARIED, TO SIMULATE FOR COMPARISON THE 
EFFECT OF MODULATION OF SYNAPTIC EFFICACY. 
FIRING FREQ. 
BURSTS SPIKES/ FIRING AS % OF 
PARAMETER SYMBOL VALUE SEC BURST FREQ. BENCHMARK 
SOMATIC MEMBRANE 
TIME CONSTANT 
DENDRITIC MEMBRANE 
TIME CONSTANT 
TS 
BENCHMARK 5.0 13.51 2 27.03 100.0% 
LOW 2.5 13.70 2 27.40 101.4% 
HIGH 10.0 12.82 2 25.64 94.9% 
BENCHMARK 5.0 13.51 2 27.03 100.0% 
LOW 2.5 13.5 ! 2 27.03 100.0% 
HIGH 10.0 12.66 2 25.32 93.7% 
156 Rhodes 
PARAMETER SYMBOL 
VALUE 
BURSTS SPIKES/ FIRING 
SEC BURST FREQ. 
FIRING FREQ. 
AS % OF 
BENCHMARK 
THRESHOLD FOR CALCTHRESH 
[CA]-GATED POTASSIUM 
CURRENT IN DENDRITE (1) 
ACTIVATION RATE OF 
SOMATIC POTASSIUM 
CURRENT (2) 
B 
ACTIVATION RATE OF 
DENDRITIC [CA]-GATED 
POTASSIUM CURRENT 
TIME CONSTANT OF 
SOMATIC POTASSIUM 
CURRENT (2) 
TGK 
TIME CONSTANT OF 
DENDRITIC POTASSIUM 
CURRENT (3) 
ACTIVATION RATE OF 
CALCIUM CONDUCTANCE 
D 
TIME CONSTANT 
OF DENDRITIC CALCIUM 
CONDUCTANCE 
ACCUMULATION RATE OF 
CALCIUM FOR A GIVEN 
CALCIUM CONDUCTANCE (4) 
TGC 
A 
BENCHMARK 20.0 13.51 2 27.03 100.0% 
LOW 10.0 12.82 I 12.82 47.4% 
HIGH 40.0 13.51 3 40.54 150.0% 
BENCHMARK 33.0 13.51 2 27.03 100.0% 
LOW 16.5 12.99 3 38.96 144.2% 
HIGH 66.0 13.51 I 13.51 50.0% 
BENCHMARK 75.0 13.51 2 27.03 100.0% 
LOW 37.5 12.35 4 49.38 182.7% 
HIGH 150.0 13.16 2 26.32 97.4% 
BENCHMARK 3.5 13.51 2 27.03 100.0% 
LOW 1.8 13.51 2 27.03 100.0% 
HIGH 7.0 13.33 2 26.67 98.7% 
BENCHMARK 10,0 13.51 2 27.03 100.0% 
LOW 5.0 21.74 2 43.48 160.9% 
HIGH 20,0 8.00 3 24.00 88.8% 
BENCHMARK 2.2 13.51 2 27.03 100.0% 
LOW 1,1 14.71 2 29.41 108.8% 
HIGH 4.4 11,11 4 44.44 164.4% 
BENCHMARK 5.0 13.51 2 27.03 100.0% 
LOW 2.5 14.29 2 28.57 105.7% 
HIGH 10.0 12.82 2 25.64 94.9% 
BENCHMARK 2.0 13.51 2 27.03 100.0% 
LOW 1.0 13.51 3 40.54 150.0% 
HIGH 4.0 12.99 I 12.99 48.1% 
TIME CONSTANT FOR 
CALCIUM ACCUMULATION 
TCA 
INPUT CONDUCTANCE FROM GDS 
DENDRITE TO SOMA (5) 
INPUT CONDUCTANCE FROM GSD 
SOMA TO DENDRITE 
SOMATIC FIRING 
THRESHOLD 
THRESHOLD 
CA SPIKE THRESHOLD CSPKTHRESH 
IN DENDRITE (6) 
SYNAPTIC INPUT TO INPUT 
DENDRITE (8) 
BENCHMARK 5.0 13.51 2 27.03 100.0% 
LOW 2.5 14.71 I 14.71 54.4% 
HIGH 10.0 11.76 3 35.29 130.6% 
BENCHMARK 5.0 13.51 2 27.03 100.0% 
LOW 2.5 11.90 I 11.90 44.0% 
HIGH 10.0 14.29 4 57.14 211,4% 
BENCHMARK 5.0 13.51 2 27.03 100.0% 
LOW 2.5 13.89 2 27.78 102.8% 
HIGH 10.0 10.75 2 21.51 79.6% 
BENCHMARK 12.0 13.51 2 27.03 100.0% 
LOW 6.0 15.38 4 61.54 227.7% 
HIGH 24.0 13.16 I 13.16 48.7% 
BENCHMARK 12.0 13.51 2 27.03 100.0% 
LOW 6.0 14.08 2 28.17 104.2% 
HIGH 24.0 13.70 2 27.40 101.4% 
BENCHMARK 35.0 13.51 2 27.03 100.0% 
LOW (7) 27.0 11.63 2 23.26 86.0% 
HIGH 70.0 16.95 2 33.90 125.4% 
A Systematic Study of the Input/Output Properties 157 
NOTES TO PARAMETER SENSITIVITY ANALYSIS 
(1) The number of spikes per burst is altered by modulating the internal calcium 
concentration required to trigger the dendritic potassium current. In an observation 
repeated several times herein, it seems clear that modulating the hyperpolarizing 
potassium current has a marked effectiveness in modulating the neuron's output. 
(2) Modulating the activation rate (B) of the somatic potassium current strongly effects 
firing, but changing the time constant of this current has almost no effect either on 
bursts/second or spikes/burst. 
(3) However, note that, among all 17 parameters of this model neuron, it is only the 
time constant of the [CA]-gated dendritic potassium current which is effective in 
modulating the rate of bursting (whereas th  somatic potassium current time constant 
does not seem to effect the model neuron's output at all). 
(4) This quantity, the accumulation rate of calcium in the dendrite per unit calcium 
conductance, would increase as the effectiveness of calcium buffers within the dendrite 
(5) Despite its efficacy in modulating the neuron's output, this parameter is presumably 
not a likely candidate for plasticity, because it depends on the axial resistance of the 
cytoplasm, the cross section of the base of the dendrite, and the volume of the soma, all 
of which seem unlikely to be the subject to modulation. 
(6) Surprisingly, the overall input-output relation for the neuron is not much effected by 
changing the threshold for the voltage gated calcium spike activity in the dendrite. 
(7) The minimum dendritic input required to produce any spike activity (that is, to 
increase the voltage in the soma above firing threshold) may be calculated to be 26.4 
with all the other parameters at benchmark values. Hence 27 is an input level that is 
only 2% above the minimum level to get any firing at all. Note that it appears a 2 
spike burst is always produced (with the internal parameters set at the benchmark levels) 
if any firing at all is elicited. The number of spikes per burst, then, is modulated by 
conductance activation rates and calcium accumulation rates but not by input. Tables 2 
and 3 demonstrate this over a wide range of inputs. 
(8) Note that doubling the synaptic input to the dendrite only increases the model 
neuron's firing rate by 25.4%, but that, for example, doubling the activiation rate of the 
dendritic calcium current increases the firing rate by 64.4%. Hence we suggest that 
modulation of synaptic efficacy is not the only choice or even the most effective choice 
for the mechanism underlying plasticity. Alkon (1988,1989) and others have in fact 
recently reported that an increase in protein kinase C, leading to a reduction in calcium- 
activated potassium current, is observed to be associated with conditioning in 
Hermissenda and rabbit. Thus, plasticity in the nervous system may indeed operate via a 
whole set of internal dynamic parameters, of which synapse efficacy is only one. 
158 Rhodes 
DENDRITIC K-CURRENT TIME CONST: 5 MSEC 
FIRING RATE=43.4B BURST RATE=21.74 
VOLT. 
VOLT. 
 N 
DENDRITIC K-CURRENT TIME CONST: EO MSEC 
FIRING RATE=24.00 BURST RATE=B.00 
o 
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! 
k4A VOLT, 
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Figure 1 
A Systematic Study of the Input/Output Properties 159 
CA 
THE INPUT/OUTPUT RE!ATION 
ACCUMATION RATE SET AT 3 I.EVELS 
LJ 
0  fiX} 200 300 400 50 (tO 700 
Figure 2 
AVE. 
LOW 
COMPARISON OF INP,/OP RELATION 
EPSP SQUARE PUISE VS ALPHA FLrNCTION 
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Figure 3 
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