366 
NEURONAL MAPS FOR SENSORY-MOTOR 
CONTROL IN THE BARN OWL 
C.D. Spence, J.C. Pearson, JJ. Gelfand, and R.M. Peterson 
David Samoff Research Center 
Subsidiary of SRI International 
CN5300 
Princeton, New Jersey 08543-5300 
W.E. Sullivan 
Department of Biology 
Princeton University 
Princeton, New Jersey 08544 
ABSTRACT 
The barn owl has fused visual/auditory/motor representations of 
space in its midbrain which are used to orient the head so that visu- 
al or auditory stimuli are centered in the visual field of view. We 
present models and computer simulations of these structures which 
address various problems, includi. g. the construction of a map of 
space from auditory sensory info'lation, and the problem of driv- 
ing the motor system from these maps. We compare the results 
with biological data. 
INTRODUCTION 
Many neural network models have little resemblance to real neural structures, part- 
ly because the brain's higher functions, which they attempt to imitate, are not yet 
experimentally accessible. Nevertheless, some neural-net researchers are finding that 
the accessible structures are interesting, and that their functions are potentially use- 
ful. Our group is modeling a part of the barn owl's nervous system which orients 
the head to visual and auditory stimuli. 
The barn owl's brain stem and midbrain contains a system that locates visual and 
auditory stimuli in space. The system constructs an auditory map of spatial direction 
from the non-spatial information in the output of the two cochlea. This map is in 
the external nucleus of the inferior colliculus, or ICx [Knudsen and Konishi, 1978]. 
The ICx, along with the visual system, projects to the optic tectum, producing a 
fused visual and auditory map of space [Knudsen and Knudsen, 1983]. The map in the 
tectum is the sotace of target position used by the motor system for orienting the 
head. 
In the last fifteen years, biologists have determined all of the structures in the sys- 
tem which produces the auditory map of space in the ICx. This system provides sev- 
Neuronal Maps for Sensory-Motor Coritrol in the Barn Owl 367 
eral examples of neuronal maps, regions of tissue in which the response properties 
of neurons vary continuously with position in the map. (For reviews, see Knudsen, 
1984; Knudsen, du Lac, and Estedy, 1987; and Konishi, 1986.) Unfortunately, the 
motor system and the projections from the rectum are not well known, but experi- 
mental study of them has recently begun [Masino and Knudsen, 1988]. We should 
eventually be able to model a complete system, from sensory input to motor output. 
In this paper we present several models of different parts of the head orientation sys- 
tem. Fig. 1 is an overview of the stmcmre we'll discuss. In the second section of 
this paper we discuss models for the construction of the auditory space map in the 
ICx. In the third section we discuss how the optic tectum might drive the motor 
system. 
CONSTRUCTION OF AN AUDITORY MAP OF SPACE 
The barn owl uses two binaural cues to locate objects in space: azimuth is derived 
from inter-aural time or phase delay (not onset time difference), while elevation is 
derived from inter-aural intensity difference (due to vertical asymmetries in sensitiv- 
Acoustic 
Core of the Itc 
Intensity < el  
>' iro;;'o, m 
the IC (ICx) 
VLVp 
Visual 
Retina 
Figure 1. Overview of the neuronal system for target localization in the barn owl 
(head orients towards potential targets for closer scrutiny). The illustration 
focuses on the functional representations of the neuronal computation, and does 
not show all of the relevant connections. The grids represent the centrally synthe- 
sized neuronal maps and the patterns within them indicate possible patterns of neu- 
ronal activation in response to acoustic stimuli. 
368 Spence, Pearson, Gelland, Peterson and Sullivan 
ity). Corresponding to these two cues are two separate processing streams which pro- 
duce maps of the binaural cues, which are shown in Figs. 2-5. The information on 
these maps must be merged in order to construct the map of space in the ICx. 
/TD  
Azimuth 
f 
Figure 2. Standard Model for the construction of an auditory map of space from 
maps of the binaural cues. Shading represents activity level. IID is Inter-aural 
Intensity Difference, ITD is Inter-aural Time Delay. 
A simple model for combining the two maps is shown in Fig. 2. It has not been 
described explicitly in the literature, but it has been hinted at [Knudsen, et al, 
1987]. For this reason we have called it the standard model. Here all of the neurons 
representing a given time delay or azimuth in the ITD vs. frequency map project to 
all of the neurons representing that azimuth in the space map. Thus a stimulus with 
a certain ITD would excite a strip of cells representing the associated azimuth and 
all elevations. Similarly, all of the neurons representing a given intensity difference 
or elevation in the lid vs. frequency map project to all of the neurons representing 
that elevation in the space map. (The map of IID vs. frequency is constructed in the 
nucleus ventralis lemnisci lateralis pars posterior, or VLVp. VLVp neurons are said 
to be sensitive to intensity difference, that is they fire if the intensity difference is 
great enough. Neurons in the VLVp are spatially organized by their intensity differ- 
ence threshold [Manley, et al, 1988]. Thus, intensity difference has a bar-chart-like 
representation, and our model needs some mechanism to pick out the ends of the 
bars.) Only the neurons at the intersection of the two strips will fire if lateral inhi- 
bition allows only those neurons receiving the most stimulation to fire. In the third 
section we will present a model for connections of inhibitory inter-neurons which 
can be applied to this model. 
Part of the motivation for the standard model is the problem with phase ghosts. 
Phase ghosts occur when the barn owl's nervous system incorrectly associates the 
wave fronts arriving at the two ears at high frequency. In this case, neurons in the 
map of ITD vs. frequency become active at locations representing a time delay which 
Neuronal Maps for Sensory-Motor Control in the Barn Owl 369 
differs from the true time delay by an integer multiple of the period of the sound. 
Because the period varies inversely with the frequency, these phase ghosts will have 
apparent time delays that vary with frequency. Thus, for stimuli that are not pure 
tones, ff the barn owl can compare the activities in the map at different frequencies, 
it can eliminate the ghosts. The standard model does this (Fig. 2). In the ITD vs. fre- 
quency map there are more neurons firing at the position of the true ITD than at the 
ghost positions, so space map neurons representing the true position will receive the 
most stimulation. Only those neurons representing the true position will fire 
because of the lateral inhibition. 
There is another kind of ghost which we call the multiple-source ghost (Fig. 3). If 
two sounds occur simultaneously, then space map neurons representing the time 
delay of one source and the intensity difference of the other will receive a large 
amount of stimulation. Lateral inhibition may suppress these ghosts, but if so, the 
owl should only be able to locate one source at a time. In addition, the true sources 
might be suppressed. The barn owl may actually suffer from this problem, although 
it seems unlikely if the owl has to function in a noisy environment. The relevant 
behavioral experiments have not yet been done. 
Experimental evidence does not support the standard model. The ICx receives most 
of its input from the lateral shell of the central nucleus of the inferior colliculus 
ITD  
I 
Azimuth 
f 
lid 
Figure 3. Multiple-source ghosts in the standard model for the construction of 
the auditory space map. For clarity, only two pure tone stimuli are represented, 
and their frequencies and locations are such that the "phase ghost" problem is 
not a factor. The black squares represent regions of cells that are above thresh- 
old. The circled regions are those that are f'ffing in response to the ITD of one 
stimulus and the IID of another. These regions correspond to phantom targets. 
370 Spence, Pearson, Gelfand, Peterson and Sullivan 
(lateral shell of the ICc) [Knudsen, 1983]. Neurons in the lateral shell are tuned to 
frequency and time delay, and these parameters are mapped [Wagner, Takahashi, and 
Konishi, 1987]. However, they are also affected by intensity difference [Knudsen and 
Konishi, 1978, I. Fujita, private communication]. Thus the lateral shell does not fit 
the picture of the input to the ICx in the standard model, rather it is some interme- 
diate stage in the processing. 
We have a model, called the lateral shell model, which does not suffer from multi- 
pie-source ghosts (Fig. 4). In this model, the lateral shell of the ICc is a three- 
dimensional map of frequency vs. intensity difference vs. time delay. A neuron in 
the map of time delay vs. frequency in the ICc core projects to all of the neurons in 
ITD 
f 
lid 
ICx 
Azimuth 
Figure 4. Lateral shell model for the construction of the auditory map of space 
in the ICx. f: frequency, ITD: inter-aural time delay, lID: inter-aural intensity 
difference. 
Neuronal Maps for Sensory-Motor Control in the Barn Owl 371 
the three-dimensional map which represent the same time delay and frequency. As in 
the stand_a_rd model, a strip of neurons is stimulated, but now the frequency tuning 
is preserved. The map of intensity difference vs. frequency in the nucleus ventralis 
lemnisci lateralis pars posterior (VLVp) [Manley, et al, 1988] projects to the three- 
dimensional map in a similar fashion. Lateral inhibition picks out the regions of 
intersection of the strips. Neurons in the space map in the ICx receive input from 
the strip of neurons in the three-dimensional map which represent the appropriate 
time delay and intensity difference, or equivalently azimuth and elevation. Phase 
ghosts will be present in the three-dimensional map, but in the ICx lateral inhibi- 
tion will suppress them. 
Multiple-source ghosts are eliminated in the lateral shell model because the sources 
are essentially tagged by their frequency spectra. If two sources with no common 
frequencies are present, there are no neurons which represent the time delay of one 
source, the intensity difference of another, and a frequency common to both. In the 
more likely case in which some frequencies are common to both sources, there will 
be fewer neurons fufng at the ghost positions than at the real positions, so again lat- 
eral inhibition in the ICx can suppress firing at the ghost positions, exactly as it 
suppresses the phase ghosts. The fact that intensity and time delay information is 
combined before frequency tuning is lost in the ICx suggests that the owl handles 
multiple-source ghosts by frequency tagging. A three dimensional map is not essen- 
tial, but it is conceptually simple. 
Before ending this section, we should mention that others have independently 
thought of this model. In particular, M. Konishi and co-workers have looked for a 
spatial organization or mapping of intensity response properties in the lateral shell, 
but they have not found it. They also have said that they can't yet rule it out [M. 
Konishi, I. Fujita, private communication]. 
DRIVING THE MOTOR SYSTEM FROM MAPS 
As mentioned before, all of the parts of the auditory system in the brain stem and 
midbrain are known, up to the optic tectum. The optic ttum has a map of spatial 
direction which is common to both the visual and auditory systems. In addition, it 
drives the motor system, so if the tectum is stimulawat at a point, the barn owl's 
head will move to face a certain direction. The new orientation is mapped in register 
with the auditory/visual map of spatial direction, e.g., stimulating a location which 
represents a stimulus eight degrees to the right of the current orientation of the head 
will cause the head to turn eight degrees to the right. 
Little is known about the projections of the tectum, although work has started 
[Masino and Knudsen, 1988]. There was one earlier experiment. Two electrodes were 
placed in one of the recta at positions representing sensory stimuli eight degrees and 
sixty degrees toward the side of the head opposite to this rectum. When either posi- 
tion was stimulated by itself, the alert owl moved its head as expected, by eight or 
372 Spence, Pearson, Gelland, Peterson and Sullivan 
sixty degrees. When both were stimulated together, the head moved about forty 
degrees [du Lac and Knudsen, 1987]. 
The averaging of activity in the tectum is easy to explain. In some motor models it 
should be produced naturally by the activation of an agonist-antagonist muscle pair 
(see, for example, Grossberg and Kuperstein, 1986). In the presence of two stimuli, 
the tension in each muscle is the sum of the tensions it would have for either stimu- 
lus alone, so the equilibrium position should be about the average position. 
We have a different model, which produces a map of the average position. The con- 
nection strengths from tectal cells to an averaging map cell decrease quadratically 
with the difference in represented direction. A quadratic distribution of stimulation 
is very broad, however, so lateral inhibition is required to make the active region 
fairly narrow. 
1-D tectum -- 
25 
0 
I I 
, .,:? .'.. .. . .. 
"' ' ." %.r'' ' ... . . 
Position 
Figure 5. Averaging map simulation. The upper thin rectangle is a one-dimen- 
sional version of the space map in the optic rectum. The two marks represent the 
position of stimulating electrodes that are simultaneously active. The lower 
rectangle is the averaging map, with position represented horizontally and time 
increasing vertically. The sqsres represent cell firings. Note that the activity 
quickly becomes centered at the average position of the two active positions in 
the teeturn. 
We have simulated a one-dimensional version of this model, with 128 cells in the 
tectum, and in the averaging map, 128 excitatory and 128 inhibitory cells. The exci- 
tatory cells and the inhibitory cells both receive the same quadratically weighted 
input from the tectum. Each inhibitory cell inhibits all of the other cells in the aver- 
aging map, both excitatory and inhibitory, except for those in a small local neigh- 
borhood. (The weights are actually proportional to one minus a gaussian with a max- 
imum of one.) An excitatory cell receives exactly the same input as the inhibitory 
Neuronal Maps for Sensory-Motor Control in the Barn Owl 373 
cell at the same location, so their voltages are the same. Because of this we only 
show the excitatory cells in Fig. 5. This figure shows cell position horizontally and 
time increasing vertically. The black squares are plotted at the time a given cell fires. 
We are interested in whether an architecture like this is biologically plausible. For 
this reason we have tried to be fairly realistic in our model. The cells obey a mem- 
brane equation: 
dv 
C ' = -gl (v - Vl) - ge (v - Ve) - gi (v - v i) 
in which C is the capacitance, the g's are conductances, l refers to leakage quantities, 
e refers to excitatory quantifies, and i refers to inhibitory quantities. The output of 
a cell is not a real number, but spikes that occur randomly at an average rate that is 
a monotonic function of the voltage. We used the usual sigmoidal function in this 
simulation, although the membrane equation automatically limits the voltage and 
hence the firing rate. A cell that spikes on a given time step affects other cells by 
affecting the appropriate conductance. To get the effect of a post synaptic potential, 
the conductances obey the equation of a damped harmonic oscillator: 
d-g __ . o-2g 
dt 2 
When a spike from an excitatory cell arrives, we increment the time derivative of g 
by some amount. If the oscillator is overdamped or critically damped, the conduc- 
tance goes up for a time and then decreases, approaching zero exponentially. 
We are not suggesting that a damped harmonic oscillator exists in the membranes of 
neurons, but it efficiently models the dynamics of synaptic transmission. The equa- 
tions for the conductances also have the nice property that the effects of multiple 
spikes at different times add. 
With values for the cell parameters that agree well with experimental data, it takes 
about twenty milliseconds for the simulated map to settle into a fairly steady state, 
which is a reasonable time for the function of this map. Also, there was no need to 
fine tune the parameters; within a fairly wide range the effect of changing them is to 
change the width of the region of activity. 
We tried another architecture for the inhibitory interneurons, in which they received 
their input from the excitatory neurons and did not inhibit other inhibitory neurons. 
The voltages in this architecture oscillated for a very long time, without picking out 
a maximum. The architecture we are now using is apparently superior. Since it is 
quick to pick out a maximum of a broad distribution of stimulation, it should work 
very well in other models requiring lateral inhibition, such as the lateral shell mod- 
el discussed earlier. 
374 Spence, Pearson, Gelfand, Peterson and Sullivan 
CONCLUSION 
We have presented models for two parts of the barn owl's visual/auditory localiza- 
tion and head orientation system. These models make experimentally testable predic- 
tions, and suggest architectures for artificial systems. One model constructs a map 
of stimulus position from maps of inter-aural intensity and timing differences. This 
model solves potential problems with ghosts, i.e., the representation of false 
sources in the presence of certain kinds of real sources. 
Another model computes the average value of a quantity represented on a neural map 
when the activity on the map has a complex distribution. This model explains recent 
physiological experiments. A simulation with fairly realistic model neurons has 
shown that a biological structure could perform this function in this way. 
A common feature of these models is the use of neuronal maps. We have only men- 
tioned a few of the maps in the barn owl, and they are extremely common in other 
nervous systems. We think this architecture shows great promise for applications in 
artificial processing systems. 
Acknowledgments 
This work was supported by internal funds of the David Samoff Research Center. 
References 
Grossberg, S. and M. Kuperstein (1986) Neural dynamics of adaptive motor control, 
North-Holland. 
Knudsen, E.I. (1983) J. Comp. Neurology, 218:174-186. 
Knudsen, E.I. (1984) in Dynamical Aspects of Neocortical Function, G.M. Edel- 
man, W.E. Gall, and W.M. Cowan, editors, Wiley, New York. 
Knudsen, E.I. and P.F. Knudsen (1983) J. Comp. Neurology, 218:187-196. 
Knudsen, E.I. and M. Konishi (1978) J. Neurophys., 41:870-884. 
Knudsen, E.I., S. du Lac, and S. Esterly (1987) Ann. Rev. Neurosci., 10:41-56. 
Konishi, M. (1986) Trends in Neuroscience, April. 
du Lac, S. and E.I. Knudsen (1987) Soc. for Neurosci. Abstr., 112.10. 
Manicy, G.A., A.C. Koeppl, and M. Konishi (1988) J. Neurosci. 8:2665-2676 
Masino, T., and E.I. Knudsen (1988) Soc. for Neurosci. Abstr., 496.16. 
Wagner, H., T. Takahashi, and M. Konishi (1987) $. Neurosci. 10:3105-3116 
