An Analog VLSI Saccadic Eye Movement 
System 
Timothy K. Horiuchi Brooks Bishofberger and 
Computation and Neural Systems Program 
California Institute of Technology 
MS 139-74 
Pasadena, CA 91125 
Christof Koch 
Abstract 
In an effort to understand saccadic eye movements and their rela- 
tion to visual attention and other forms of eye movements, we -- 
in collaboration with a number of other laboratories -- are carry- 
ing out a large-scale effort to design and build a complete primate 
oculomotor system using analog CMOS VLSI technology. Using 
this technology, a low power, compact, multi-chip system has been 
built which works in real-time using real-world visual inputs. We 
describe in this paper the performance of an early version of such 
a system including a 1-D array of photoreceptors mimicking the 
retina, a circuit computing the mean location of activity represent- 
ing the superior colliculus, a saccadic burst generator, and a one 
degree-of-freedom rotational platform which models the dynamic 
properties of the primate oculomotor plant. 
I Introduction 
When we look around our environment, we move our eyes to center and stabilize 
objects of interest onto our fovea. In order to achieve this, our eyes move in quick 
jumps with short pauses in between. These quick jumps (up to 750 deg/sec in hu- 
mans) are known as saccades and are seen in both exploratory eye movements and 
as reflexive eye movements in response to sudden visual, auditory, or somatosen- 
sory stimuli. Since the intent of the saccade is to bring new objects of interest onto 
the fovea, it can be considered a primitive attentional mechanism. Our interest 
582 
An Analog VLSI Saccadic Eye Movement System 583 
lies in understanding how saccades are directed and how they might interact with 
higher attentional processes. To pursue this goal, we are designing and building a 
closed-loop hardware system based on current models of the saccadic system. 
Using traditional software methods to model neural systems is difficult because 
neural systems are composed of large numbers of elements with non-linear char- 
acteristics and a wide range of time-constants. Their mathematical behavior can 
rarely be solved analytically and simulations slow dramatically as the number and 
coupling of elements increases. Thus, real-time behavior, a critical issue for any 
system evolved for survival in a rapidly changing world, becomes impossible. Our 
approach to these problems has been to fabricate special purpose hardware that 
reflects the organization of real neural systems (Mead, 1989; Mahowald and Dou- 
glas, 1991; Horiuchi e! al., 1992.) Neuromorphic analog VLSI technology has many 
features in common with nervous tissue such as: processing strategies that are fast 
and reliable, circuits that are robust against noise and component variability, local 
parameter storage for the construction of adaptive systems and low-power consump- 
tion. Our analog chips and the nervous system both use low-accuracy components 
and are significantly constrained by wiring. 
The design of the analog VLSI saccadic system discussed here is part of a long-term 
effort of a number of laboratories ( Douglas and Mahowald at Oxford University, 
Clark at Harvard University, Sejnowski at UCSD and the Salk Institute, Mead and 
Koch at Caltech) to design and build a complete replica of the early mammalian 
visual system in analog CMOS VLSI. 
The design and fabrication of all circuits is carried out via the US-government 
sponsored silicon service MOSIS, using their 2 ttrn line process. 
2 An Analog VLSI Saccadic System 
Photoreceptor 
& Centroid Chip 
Lens 
Burst 
Generator 
Chip 
Motors 
Figure 1: Diagram of the current system. 
The system obtains visual inputs from a photoreceptor array, computes the target 
location within a model of the superior colliculus and outputs the saccadic burst 
command to drive the eyeball. While not discussed here, an auditory localization 
584 Horiuchi, Bishofberger, and Koch 
input is being developed to trigger saccades to acoustical stimuli. 
2.1 The Oculomotor Plant 
The oculomotor plant is a one degree-of-freedom turntable which is driven by a pair 
of antagonistic-pulling motors. In the biological system where the agonist muscle 
pulls against a passive viscoelastic force, the fixation position is set by balancing 
these two forces. In our system, the viscoelastic properties of the oculomotor plant 
are simulated electronically and the fixation point is set by the shifting equilibrium 
point of these forces. In order maintain fixation off-center, like the biological system, 
a tonic signal to the motor controller must be maintained. 
2.2 Photoreceptors 
The front-end of the system is an adaptive photoreceptor array (Delbriick, 1992) 
which amplifies small changes in light intensity yet adapts quickly to gross changes in 
lighting level. The current system uses a 1-D array of 32 photoreceptors 40 microns 
apart. This array provides the visual input to the superior colliculus circuitry. 
The gain control occurs locally at each pixel of the image and thus the maximum 
sensitivity is maintained everywhere in the image in contrast to traditional imaging 
arrays which may provide washed out or blacked-out areas of an image when the 
contrast within an image is too large. In order to trigger reflexive, visually-guided 
saccades, the output of the photoreceptor array is coupled to the superior colliculus 
model by a luminance change detection circuit. A change in luminance somewhere 
in the image sends a pulse of current to the colliculus circuit which computes the 
center of this activity. This coupling passes a current signal which is proportional to 
the absolute-value of the temporal derivative of a photoreceptor's voltage output, 
(i.e. I]dI(x,t)/dt]l where I(x,t) is the output of the photreceptor array). While we 
are initially building a 1-D system, 2-D photoreceptor arrays have been built in 
anticipation of a two degree-of-freedom system. While these photoreceptor circuits 
have been successfully constructed, we do not discuss the results here since the 
performance of these circuits are described in the literature (Delbriick 1992). 
2.3 Superior Colllculus Model 
The superior colliculus, located on the dorsal surface of the midbrain, is a key area 
in the behavioral orientation system of mammals. The superficial layers have a 
topographic map of visual space and the deeper layers contain a motor map of 
saccadic vectors. Microstimulation in this area initiates saccades whose metrics 
are related to the location stimulated. This type of representation is known as a 
population coding. Many neurons in the deeper layers of superior colliculus are 
multisensory and will generate saccades to auditory and somatosensory targets as 
well as visual targets. 
While it is clear that the superior colliculus performs a multitude of integrative 
functions between sensory modalities and attentional processes, our initial model 
of superior colliculus simply computes the center of activity from the population 
code seen in the superficial layers (i.e. the photoreceptor array) using the weighted 
average techniques developed by DeWeerth (1991) for computing the centroid of 
An Analog VLSI Saccadic Eye Movement System 585 
2.6 
2.2 
2.O 
Centtold Circuit Output vs. Target Error 
1.8 
-40 -0 - -10 0 10 20 30 
T  
Figure 2: Output of the centroid circuit for a flashed red LED target at different 
angles away from the center position. Note that the output of the circuit was 
sampled 1 msec. after stimulus onset to account for capacitive delays. 
brightness. The results of the photoreceptor/centroid circuits are shown in Fig. 2. 
In the case of visually-guided saccades, retinal error translates directly into motor 
error and thus we can use the photoreceptors directly as our inputs. This simplified 
retina/superior colliculus model provides the motor error which is then passed on 
to the burst generator. 
2.4 Saccadic Burst Generator 
The burst generator model (Fig. 3) driving the oculomotor plant receives as its 
input, desired change in eye position from the superior colliculus model and creates 
a two-component signal, a pulse and a step (Fig. 4). A pair of these pulse/step 
signals drive the two muscles of the eye which in turn moves the retinal array, thus 
closing the loop. The burst generator model is a double integrator model based on 
the work by Jiirgens, et al (1981) and Lisberger et al (1987) which uses initial motor 
error as the input to the system. This motor error is injected into the "integrating" 
burst neuron which has negative feedback onto itself. This arrangement has the 
effect of firing a number of spikes proportional to the initial value of motor error. In 
the circuit, this integrator is implemented by a 1.9 pF capacitor. This burst of spikes 
serves to drive the eye rapidly against the viscosity. The burst is also integrated by 
the "neural integrator" (another 1.9 pF capacitor) which holds the local estimate of 
the current eye position from which the tonic, or holding signal is generated. Figs. 
4 and 5 show output data from the burst generator chip and the response of the 
physical mechanism to this output. The inputs to the burst generator chip are 1) a 
voltage indicating desired eye position and 2) a digital trigger signal. The outputs 
are a pair of asynchronous digital pulse trains which carry the pulse/step signals 
which drive the left and right motors. 
586 Horiuchi, Bishofberger, and Koch 
3 Discussion 
As we are still in the formative stages of our project, our first goal has been to 
demonstrate a closed-loop system which can fixate a particular stimulus whose im- 
age is falling onto its photoreceptor array. The first set of chips represent dramat- 
ically simplified circuits in order to capture the first-order behavior of the system 
while using known representations. Owing to the large number of parameters that 
must be set, and their sensitivity to variations, we have begun a study to investigate 
biologically plausible approaches to automatic parameter-setting. 
In the short term we intend to dramatically refine the models used at each stage, 
most notably the superior colliculus which is involved in the integration of non-visual 
sources of saccade targets (e.g. memory or audition), and in the mechanisms used 
for target selection or fixation. In the longer run, we plan to model the interaction 
of this system with other oculomotor processes such as smooth pursuit, VOR, OKR, 
AND vergence eye movements. 
While the biological microcircuits of the superior colliculus and brainstem burst 
generator are not well known, more is understood about the representations found 
in these areas. By exploring the advantages and disadvantages of various compu- 
tational models in a working system, it is hoped that a truly robust system will 
emerge as well as better models to explain the biological data. The construction of 
a compact hardware system which operates in real-time can often provide a more 
intuitive understanding of the closed-loop system. In addition, a visually-attentive 
hardware system which is physically small and low-power has numerous applications 
in the real world such as in mobile robotics or remote surveillance. 
4 Acknowledgements 
Many thanks go to Prof. Carver Mead and his group for developing the foundations 
of this research. Our laboratory is partially supported by grants from the Office 
of Naval Research and the Rockwell International Science Center. Tim Horiuchi is 
supported by a grant from the Office of Naval Research. 
5 References 
T. Delbriick and C. Mead, (1993) Ph.D. Thesis, California Institute of Technology. 
S. P. DeWeerth, (1991) Ph.D. Thesis, California Institute of Technology. 
T. Horiuchi, W. Bair, B. Bishofberger, A. Moore, J. Lazzaro, C. Koch, (1992) Int. 
J. Computer Vision 8:3,203-216. 
R. Jiirgens, W. Becker, and H. H. Kornhuber, (1981) Biol. Cybern. 39:87-96. 
10:97- 
S. G. Lisberger, E. J. Morris, and L. Tychsen, (1987) Ann. Rev. Neurosci. 
129. 
M. Mahowald, and R. Douglas, (1991) Nature 354:515-518. 
C. Mead, (1989) Analog VLSI and Neural Systems, Addison-Wesley. 
An Analog VLSI Saccadic Eye Movement System 587 
Left Burst Neuron 
Left Motor Neuron 
Motor Error < 0 
Motor Error > 0 
Other inputs: 
VOR/OKR 
Smooth Pursuit 
Neural 
Integrator 
Right Burst Neuron 
Ill 
Right Motor Neuron 
Figure 3: Schematic diagram of the burst generator. The burst neuron "samples" 
the motor error when it receives a trigger signal (not shown) and begins firing 
as a sigmoidal function of the motor error. The spikes feedback and discharge the 
integrator and the burst is shut down. This "pulse" signal drives the eye against the 
viscosity. This signal is also integrated by the neural integrator which contributes 
the "step" portion of the motor command to hold the eye in its final position. The 
neural integrator has additional velocity inputs for other oculomotor behavior such 
as smooth pursuit, VOR and OKR. Note that the burst neuron for the other muscle 
is silent in this direction. 
588 Horiuchi, Bishofberger, and Koch 
I 
-0.25 2.25 
0.00 0.25 
0.50 
I I I I I I 
0.75 1.00 1.25 1.50 1.75 2.00 
re (sonds) (10 '2 ) 
Figure 4: Spike signals in the circuit during a small saccade. (7.5 degrees to the 
right, starting from 4.8 degrees to the right.) Top: Burst neuron, Middle: Neural 
Integrator, Bottom: Motor neuron. (one of the outputs of the chip) Note that 
the "neuron" circuit currently used increases both its pulse frequency and pulse 
duration for large input currents, causing the voltage saturation seen in the bottom 
trace. 
80' 
6O 
4O 
 2O 
.-6O 
Eye Position vs. 
-8o I I I I I I I I I I 
-0.025 0,000 0.025 0.050 0.075 0.100 0.125 0.150 0.175 0.200 0.225 
ame (sonds) 
Figure 5: Horizontal position vs. time for 21 different saccades. Peak angular 
velocity achieved for the 60 degree saccade to the right was approximately 870 
degrees per second. The input command was changed uniformly from -60 to +60 
degrees. 
An Analog VLSI Saccadic Eye Movement System 589 
S0' 
60' 
40' 
-60 ' 
-80 
1 
Final Eye Position vs. Burst Command Voltage 
I I I I I I I I I 
1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 
Input Voltage to Burst Ocnerator (center = 2.5v) 
Figure 6: Linearity of the system for the position data given in the previous figure. 
Final eye position was computed as the average eye position during the last 20 msec. 
of each trace. 
30' 
25 
 2O 
 1o 
Average of 10 Saccades from "center" to 30 deg. R 
-5 I I I I I I I I I I 
-0.025 0.000 0.025 0.050 0.075 0.100 0,125 0.150 0.175 0.200 0.225 
time (seconds) 
Figure 7: Repeatability: The solid line shows averaged eye position (relative to cen- 
ter) vs. time for 10 identical saccades. The dashed lines show a standard deviation 
on each side of the mean. Most of the variability is attributed to problems with 
friction. 
