Natural Dolphin Echo Recognition _Using an Integrator 
Gateway Network 
Herbert L. Roitblat 
Department of Psychology, University 
of Hawaii, Honolulu, HI 96822 
Patrick W. B Moore, Paul E. 
Nachtigall, & Ralph H. Penner 
Naval Ocean Systems Center, Hawaii 
Laboratory, Kailua, Hawaii, 96734 
Abstract 
We have been studying the performance of a bottlenosed dolphin on 
a delayed matching-to-sample task to gain insight into the processes and 
mechanisms that the animal uses during echolocation. The dolphin 
recognizes targets by emitting natural sonar signals and listening to the 
echoes that return. This paper describes a novel neural network 
architecture, called an integrator gateway network, that we have de- 
veloped to account for this performance. The integrator gateway 
network combines information from multiple echoes to classify targets 
with about 90% accuracy. In contrast, a standard backpropagation 
network performed with only about 63% accuracy. 
1. INTRODUCTION 
The study of animals can provide a very important source of information for the de- 
sign of automated artificial systems such as robots and autonomous vehicles. Animals 
have evolved in a real world, solving real problems, such as gathering and interpreting 
essential information. We call the process of using animal studies to inform the de- 
sign of artificial systems biomimetics because the artificial systems are designed as 
mimics of biological ones. 
273 
274 Roitblat, Moore, Nachtigall, and Penner 
2. INVESTIGATIONS OF DOLPHIN ECHOLOCATION PERFOR- 
MANCE 
Dolphin echolocation clicks emerge from the rounded forehead or melon as a highly 
directional sound beam with 3 dB (half power) beamwidths of approximately 10  in 
both the vertical and horizontal planes (Au, et al., 1986). Echolocation clicks have 
peak energy at frequencies from 40 to 130 kHz with source levels of 220 dB re: 1 u Pa 
at I m (Au, 1980; Moore & Pawloski, 1990). Bottlenosed dolphins have excellent di- 
rectionally selective hearing (Au & Moore, 1984), spanning over 7 octaves, and can 
detect frequencies as high as 150 kHz (Johnson, 1966). 
3. BEHAVIORAL METHODS 
We have been studying the performance of a bottlenosed dolphin on an echolocation 
delayed matching-to-sample (DMTS) task (e.g., Nachtigall, 1980; Nachtigall, et al., 
1985; Roitblat, et al., 1990a; Moore, ct al., 1990). In this task a sample stimulus is 
presented underwater to a blindfolded dolphin. The dolphin is allowed to echolocate 
on this object ad lib. The object is then removed from the water, and after a short 
delay, three alternative objects are presented (the comparison stimuli). One of these 
objects is identical to (matches) the sample object, and the dolphin is required to in- 
dicate the matching stimulus by touching a response wand in front of it. The object 
that serves as sample and the location of the correct match vary randomly from trial 
to trial. 
Recent work has concentrated on performance with three sample and comparison 
stimuli: (a) a PVC plastic tube, (b) a water-filled stainless steel sphere, and (c) a solid 
aluminum cone (see Roitblat, et al., 1990a). On average the dolphin used 37.2 clicks 
to identify the sample, and an average of 4.2 scans to examine the three comparison 
stimuli. A scan is a train of clicks to a single stimulus ended either by the initiation of 
a scan to another stimulus or by a cessation of clicking 
The dolphin's scanning patterns were modeled using sequential sampling theory (see 
also Roitblat, 1984). Simulations based on this model provide a reasonably good ap- 
proximation of the dolphin's performance (Roitblat, et al., 1990a). The simulation 
differed from the dolphin's actual performance, however, in that it was less variable 
than the live dolphin. We return to the problem of accounting for this difference in 
variability below after considering some models of the details of echo recognition. 
4. ARTIFICIAL NEURAL NETWORKS 
We have developed a series of neural-network models of dolphin echolocation pro- 
cessing (see also Gorman and Sejnowski, 1988). We (Moore, et al., 1990; Roitblat, et 
al., 1989) trained a counterpropagation network (Hecht-Nielsen, 1987, 1988) to clas- 
sify echoes represented by their spectra into categories corresponding to each of the 
stimuli in our current stimulus set. The network correctly classified more than 95% of 
these spectra. This classification suggests two things. First, the spectral information 
Natural Dolphin Echo Recognition Using an Integrator Gateway Network 275 
FEATURE    
INTEGRATOR    
GATLNAY 
INPUT 
present in the echoes was sufficient to identify the targets on which the dolphin was 
echolocating. Second, only a single echo was necessary to classify the target. Al- 
though the network could identify the target with only a single echo, the dolphin con- 
currently performing the same task emitted many clicks in identifying the same tar- 
gets. Further investigation revealed that the clicks emitted by the dolphin were more 
variable than our initial sample suggested (Roitblat, et al., 1990b). This variability 
provides one possible explanation for the high performance level, and low variability 
of our initial model. 
4.1 THE INTEGRATOR GATEWAY NETWORK 
Our integrator gateway network incorporates features of the sequential sampling 
model described earlier, including the assumptions that the dolphin averages or sums 
spectral information from successive echoes and continues to emit clicks and collect 
returning echoes until it can classify the target producing those echoes with sufficient 
confidence. It mimics the dolphin's strategy of using multiple echoes to identify each 
target. Figure 1 shows schematic of the Integrator Gateway Network. 
Network inputs were 30-dimensional spectral vectors containing echo amplitudes in 
1.95 kHz wide frequency bins. The echoes were captured and digitized during the dol- 
phin's matching-to-sample performance. In addition to the 30 bins of spectral infor- 
mation, each echo was also marked as to whether the echo was (1.00) or was not 
276 Roitblat, Moore, Nachtigall, and Penner 
(0.00) at the start of an echo train. Recall that the dolphin directs a series of clicks to 
one target at a time, so it seemed plausible to include information marking the start of 
a click train. The frequency inputs were then passed to a scalar unit and to the inte- 
grator layer. The integrator layer also contained 30 units, connected to the frequency 
units in the input layer in a corresponding one-to-one pattern. The connections to the 
scalar unit were fixed at I/n, where n is the number of frequency inputs. The weights 
to the integrator layer were fixed at 1.00. The output of the scalar unit, i.e., the sum 
of all of its inputs, was passed to each unit in the integrator layer via a fixed weight of 
-1.00. The effect of this scalar unit was to subtract the average activity of the input 
layer (neglecting the start-of-train marker) from the inputs to the integrator layer. 
This subtraction preserved all of the relative activity information present in the inputs, 
but kept the inputs within a manageable range. 
The elements in the integrator layer computed a cumulative sum of the inputs they 
received. The role of this layer was to accumulate and integrate information from 
successive echo spectra. The outputs of the integrator layer were passed via fixed 
connections with 1.00 weights to corresponding units in the gateway layer. The inte- 
grator layer and the gateway layer each contained the same number of units. Each 
unit in the gateway layer acted as a reset for the corresponding unit in the integrator 
layer, and connected back to its corresponding unit with a weight of -1.00. Each unit 
in the gateway layer employed a multiplicative transfer function that multiplied the 
input from its corresponding unit in the integrator layer with the value of the start-of- 
train marker. Because this marker had 1.00 activity at the start of a scan and 0.00 ac- 
tivity otherwise, it functioned as a reset signal, causing the units in the integrator layer 
to be reset to 0.00 at the start of every scan; their previous activation level was sub- 
tracted from their input. 
The output of the integrator layer also led via variable-weight connections to each of 
the elements in the feature layer. The same kind of scalar unit that intervened be- 
tween the input layer and integrator layer was also used between the integrator layer 
and feature layer to subtract the average activity of the integrator layer, again to keep 
activations within a manageable range. The outputs of the feature layer led via vari- 
able-weight connections to the classifier layer. The elements in these two layers con- 
tained sigmoid transfer functions and were trained using a standard cumulative back- 
propagation algorithm with the epoch duration set to the number of training samples 
(60). 
The training set consisted of six sets of ten successive echoes each, selected from the 
ends of haphazardly chosen echo trains. An equal number of cone, tube, and sphere 
echoes were used. The training set was a relatively small subset (4%) of the total set 
of available echoes (1,335). 
4.2 INTEGRATOR GATEWAY RESULTS AND DISCUSSION 
Figure 2 shows the results of generalization testing of the network in the form of a de- 
rived confidence measure. The network was given all 30 scans (10 scans of each tar- 
Natural Dolphin Echo Recognition Using an Integrator Gateway Network 277 
.o Sre 
1,1 
i l,I 
I,I 
{{,,ii 
Figure 2. Results of generalization testing of the network in the form of the confi- 
dence of the network in assigning the echo train to the proper category. See text. 
get for a total of 1,335 sequential echoes), and was required to classify each echo 
train. "Confidence" was defined as the ratio of the activation level of the correct clas- 
sification versus the total output of the three classification units. A confidence ratio of 
1.00 indicates that only the correct unit is active. Confidence of 0.00 indicates that the 
correct unit is entirely inactive. Intermediate confidences correspond to intermediate 
likelihood ratios (Qian & Sejnowski, 1988). 
Recall that echo trains varied in length under control of the dolphin. Therefore, it is 
not entirely clear how to measure the network performance. According to sequential 
sampling theory (see Roitblat, et al., 1990a) a rational decision maker collects echo 
evidence only until a sufficiently confident classification is available and then stops. 
Table 1 shows the number of clicks in each train that were required to reach a confi- 
dence ratio of 0.96 and the classification that the network derived. Some of the scans 
ended before the network could achieve this confidence level. Three erroneous clas- 
sifications were made (90% correct). 
278 Roitblat, Moore, Nachtigall, and Penner 
Table 1 
Number of Clicks to Network Confidence Criterion 
Target Scanned 
Sphere Cone Tube Sphere Cone Tube 
Integrator Gateway Backpropagation 
16S 20C 40C iS 1C 3S 
9S 4C 18C 6S 30C 1S 
7S 2C 20T IS 1C 11S 1 
6S 6C 23T 5S 2C 1T 
19S 14C 5T 14S 2C 14T 
19S 6C 4T 14S 30S 1 iT 
34S 6C 4T 3S 32S 1 1T 
7S 4C 4T 1S 57S IT 
23C 6C 5T 40T 22S 2T 
3C 4T 22S 1T 
11C 27T 
Note: Entries are the number of clicks needed by the network to achieve the 0.96 
confidence criterion. C indicates a Cone decision, S indicates a Sphere decision, T in- 
dicates a Tube decision. 1Indicates that the dolphin stopped echolocating before the 
network reached its confidence criterion. On these scans, the decision is the one with 
the highest confidence at the end of the scan. 
4.3 A SIMPLE BACKPROPAGATION NETWORK 
The integrator gateway network reflects the assumption of sequential sampling theory 
that the dolphin combines information from successive echoes in deriving its identifi- 
cation. In contrast, a standard backpropagation network does not integrate over suc- 
cessive echoes, but instead attempts to identify each echo independently. A back- 
propagation network can be used as a model of a system that emits multiple clicks be- 
cause the echoes vary in quality. Rather than integrating the echoes, it simply waits 
for a single adequate echo that allows it to meet its confidence criterion. 
We trained a backpropagation network (using the fast-backpropagation algorithm to 
adjust the weights (Samad, 1988) on the same data that were submitted to the inte- 
grator network in order to determine whether the additional structure of the integra- 
tor network contributed to its performance accuracy. The network contained exactly 
the same number of inputs, hidden units, outputs, and adjustable connections as the 
integrator network. The networks differed only in absence of the integration appara- 
tus in the backpropagation network. 
Natural Dolphin Echo Recognition Using an Integrator Gateway Network 279 
.- Sphere 
) 0 / .... : : 
Successive Echoes 
o Cone 
I 
Successive Echoes 
.o Tube 
fi:: 6.! 
 o., N-tO 
t--O.a 
o 
Successive Echoes 
Figure 3. Confidence of the backpropagation network in assigning the echo train to 
the proper category as a function of the number of echoes received. 
4.4 BACKPROPAGATION RESULTS 
Figure 3 shows the confidence of the backpropagation network in assigning the echo 
train to the proper category as a function of the number of echoes received. Com- 
pared to the categorization performance of the integrator network, the backpropaga- 
tion network was much more variable. As Figure 3 shows, the individual echoes were 
highly variable, and frequently assigned to an erroneous category. 
The performance of the backpropagation network when judged by the standards of 
sequential sampling theory are also shown in Table 1. This table shows the number of 
clicks necessary to first reach a classification with greater then 0.96 confidence. On 
average the backpropagation network (11.57 echoes) reached its confidence criterion 
in the about the same number of clicks (t (df = 58) = 0.03, p> .05) as the integrator 
network (11.67 echoes), but it produced more errors (X 2 (df= 1) = 5.96). 
These data suggest that the integrator network added significantly to the ability to 
classify sequentially produced echoes. By implementing a signal "averaging" mecha- 
nism in the neural network the system could take advantage of the redundancy inher- 
ent in the use of multiple echoes from the same source and in the stochastic proper- 
ties of the noise in which those echoes are embedded. In contrast, the backpropaga- 
280 Roitblat, Moore, Nachtigall, and Penner 
tion network is required to process not only the characteristics of the echoes them- 
selves, but also the characteristics of the noise. This results in many spurious classifi- 
cations. 
The gateway integrator network adds a level of complexity to the standard backprop- 
agation network architecture that contributes substantially to its performance. Its de- 
sign is inspired by properties of the dolphin's performance (Nachtigall & Moore, 
1988) and it represents one step along a development path that seeks to include more 
and more of the mechanisms that we can identify from the neurobiology of echoloca- 
tion and from the performance of dolphins in their aquatic environment. 
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