A four neuron circuit accounts for change sensitive 
inhibition in salamander retina 
Jeffrey L. Teeters 
Lawrence Livermore Lab 
PO Box 808, L-426 
Livermore CA 94550 
Frank H. Eeckman 
Lawrence Livermore Lab 
PO Box 808, L-270 
Livermore CA 94550 
Frank S. Werblin 
UC-Berkeley 
Room 145, LSA 
Berkeley CA 94720 
Abstract 
In salamander retina, the response of On-Off ganglion cells to a central 
flash is reduced by movement in the receptive field surround. Through 
computer simulation of a 2-D model which takes into account their 
anatomical and physiological properties, we show that interactions 
between four neuron types (two bipolar and two amacrine) may be 
responsible for the generation and lateral conductance of this change 
sensitive inhibition. The model shows that the four neuron circuit can 
account for previously observed movement sensitive reductions in 
ganglion cell sensitivity and allows visualization and prediction of the 
spatio-temporal pattern of activity in change sensitive retinal cells. 
INTRODUCTION 
In the salamander retina, the response of transient (On-Off) ganglion cells to a central 
flash is reduced by movement in the receptive field surround (Werblin, 1972; Werblin & 
Copenhagen, 1974) as illustrated in Fig 1. This phenomenon requires the detection of 
change in the surround and the lateral transmission of this change sensitive inhibition to 
the ganglion cell dendrites. Wunk & Werblin (1979) showed that all ganglion cells 
receive change-sensitive inhibition, and Barnes & Werblin (1987) implicated a change- 
sensitive amacrine cell with widely distributed processes. The change-sensitivity of these 
amacrine cells has been traced in part to a truncation of synaptic release from the bipolar 
terminals that presumably drive them (Maguire et al., 1989). The transient response of 
these amacrine cells, mediated by voltage gated currents (Barnes & Werblin, 1986; Eliasof 
et al., 1987) also contributes to this change sensitivity. 
These and other experiments suggest that interactions between four neuron types underlie 
both the change detection and the lateral transmission of inhibition (Werblin et al., 1988; 
Maguire et al., 1989). To test this hypothesis and make predictions that could be 
compared with later experiments we have constructed a computational model of the four 
neuron circuit and incorporated it into an overall model of the retina. This model allows 
us to simulate the effect of inhibition generated by the four neuron circuit on ganglion 
cells. 
384 
Stimulus: Ganglion Cell Response: 
Windm ill with 
central test spot 
Stationary 
windmill Spinning 
I I windmill 
 /  1second 
Normal - -"/- :- '--- '""w-- - .... 1'". .... 
Spinning 
Test spot on--  r'"-'-I 
Figure 1: Change-Sensitive Inhibition. Data is from Werblin (1972). 
2 IMPLEMENTING THE HYPOTHETICAL CIRCUIT 
The proposed change-sensitive circuit (Werblin et al., 1988; Maguire et al., 1989) is 
reproduced in Figure 2. This is meant to describe a very local region of the retina where 
the receptive fields of the two bipolar cells are spatially overlapping. When a visual 
target enters this receptive field, the bipolar cells are both depolarized. The sustained 
bipolar cell activates the narrow field amacrine cell that, in turn feeds back to the synaptic 
terminal of the transient bipolar cell to truncate transmitter release after a brief (ca. 100 
msec) delay. Because the signal reaching the wide field amacrine cell is truncated after 
about 100 msec, the wide field amacrine cell will receive excitation when the target enters 
the receptive field, but will not continue to respond in the presence of the target. 
The spatial profiles of synaptic input and output for the cell types involved in the model 
are summarized in Figure 3. The bipolar and narrow field amacrine cell sensitivities 
extend over a region corresponding roughly to their dendrific spread. The wide field 
amacrine cell appears to receive input over a local region near the cell body, but delivers 
its inhibitory output over a much wider region corresponding the the full extent (ca. 500 
mm) of its processes. 
Figure 4 shows the electrical circuit model for each cell type, and illustrates the 
interactions between cells that are implemented in the model. In Figure 4, boxes contain 
the circuit for each cell and arrows between them represent synaptic interactions thought 
:NarrOW"::,'fi":eld?!:!am::a:Crine'- 
_ To GanglionS_ cells.- 
Figure 2: Circuitry to be Analyzed 
Bipolar/ (Input and output) 
Narrow fleld-" (Input and output) 
amacrlne / 
Wide field 
a mac r i n eut) 
I I I 
5 0 0 Distance from 0 cell center (Ixm) 5 0 0 
Figure 3: Spatial Profiles of Input Sensitivity and Output Transmission 
to occur as determined through experiments in which a neurotransmitter is puffed onto 
bipolar dendrites. Bipolar cells are modeled using two compartments, corresponding to 
the cell body and axon terminal as suggested in Maguire et al. (1989). Amacrine cells are 
modeled using only one compartment as in Eliasof et al. (1987). 
Each compartment has a voltage (Vbs, Vbst, Vbtt, Van, Vaw). The cell body for the 
sustained and transient bipolar are assumed to be the same. Batteries in the figure 
correspond to excitatory (E+, Ena) or inhibitory reversal potentials (E-, Ek, Ecl). 
Resistors represent ionic conductances. Circles and arrows through resisters indicate 
n'ansmitter dependent conductances which are controlled by the voltage of a presynapfic or 
same cell. Functions relating voltages to conductances are mostly linear with a threshold. 
More details are given in Teeters et al. (1991). 
Sustained 
Neurotransmitter 
Bipolar 
Input 
Transient Bi 
Narrow field 
amacrine 
Wide field 
amacrine 
Figure 4: Details of Circuitry 
A Four Neuron Circuit Accounts for Change Sensitive Inhibition 387 
3 TESTING THE COMPUTATIONAL MODEL 
Computer simulation was used to tune model parameters, and test whether the single cell 
properties and proposed interactions between cells shown in Figure 4 are consistent with 
the responses recorded from the neurons during applications of a neurotransmitter puff. 
Results are shown in Figure 5. Voltage clamp experiments electrically clamp the cell 
membrane potential to a constant voltage and determine the current required to maintain 
the voltage over time. Downward traces indicate that current is flowing into the cell; 
upward traces indicate outward current. For simplicity, scales are not shown, but in all 
cases the magnitude of the simulated response is close to that of the observed response. 
The simulated and observed responses voltage clamps of the wide field amacrine shown in 
the fourth row vary because there is a sustained outward current observed experimentally 
that is not apparent in the simulations. This shows that the model is not perfect and is 
something that needs further investigation. 
This difference between the model and observed response does not prevent the 
hypothesized function of the circuit from being simulated. This is shown on the bottom 
row where both the observed and simulated voltage responses from the wide field amacrine 
are transient. 
4 SIMULATING INHIBITION TO GANGLION CELLS 
Figure 5 illustrates that we have, to a large degree, succeeded in combining the 
characteristics of single cells into a model which can explain many of the observed 
properties thought to be due to the interaction between these cells in a local region. 
Experiment Observed response Simulated Response 
Neurotransm itter 
Puff Input   - 
Voltage clamp of 
bipolar call body   
Voltage clamp of 
narrow field amacrine   
Wide field amacrine 
Voltage clamp   
Voltage clamp with  
plcrotoxin block  
Voltageresponse, 
Figure 5: Example Puff Simulations 
388 Teeters, Eeckman, and Werblin 
The next step in our analysis is to investigate how this circuit influences the response of 
ganglion cells. To do this requires simulating the input to the bipolar dendrites and 
simulating the ganglion cells which receive the transient inhibition generated by the wide 
field amacrine. This amounts to a integrated model of an entire patch of retina, including 
receptors, horizontal cells, the four neuron circuit discussed earlier, and ganglion cells. 
The manner in which we accomplish this is illustrated in Figure 6. 
The left side of figure 6 shows the model elements. Receptors and horizontal cells are 
modeled as low pass filters with different time constants and different spatial inputs. The 
ganglion cell model receives a transient excitatory input generated phenomenologically by 
a thresholded high pass filter from the transient bipolar. Inhibitory input to the ganglion 
cell is implemented as coming from the transient wide field amacrine cells described 
previously. For simplicity, voltage gated currents and spiking are not implemented in the 
ganglion cell model, and only the off bipolar pathways are simulated. 
The right hand of Figure 6 illustrates how the model is implemented spatially. The 
circuit for each cell type is duplicated across the retina patch in a matrix format. The 
known spatial properties of each cell, such as the spatial range of transmitter sensitivity 
and release are incorporated into the model. Details are given in Teeters et al. 1991. 
5 SIMULATING INHIBITION TO GANGLION CELLS 
To test if the model can account for the observed reduction in ganglion cell response 
during movement in the receptive field surround, we simulated the experiment depicted in 
Figure 1, mainly the flashing of a central light during the presence of a stationary and 
spinning windmill. The results are shown in Figure 7. 
Model Elements 
Spatial Implementation 
Receptor 
Horizontal Cell 
Previous circuitry 
Threshold 
Hic 
filter 
amacrine 
Wide field amacrine 
Figure 6: Integrated Retinal Model 
A Four Neuron Circuit Accounts for Change Sensitive Inhibition 389 
Rather than displaying a single curve representing the response of a single unit over time, 
Figure 7 shows the simultaneous pattern of activity in an array of neurons spatially 
distributed across the retina patch at an instant in time (just after a central light spot is 
turned on). The neuron responses are the transient bipolar terminal, the wide field 
amacrine neurotransmitter release, and the ganglion cell voltage response. On the left 
column is shown the response to a flashing spot when the windmill is stationary. On the 
right is shown the response to the same flashing spot but with a spinning windmill. 
When the windmill is stationary, the transient bipolar terminal responds only to the 
center flash. Responses to the windmill vanes are suppressed by the narrow field 
amacrine cell causing the appearance of four regions of hyperpolarizing responses around 
the center. The wide field amacrine responds to the central test flash and releases 
transmiuer as shown in the second row. The army of ganglion cells responds to both the 
excitatory input generated by the spot at the bipolar terminals and the inhibitory input 
generated by the wide field amacrines. Because the wide field inhibition has not yet taken 
effect at this point in time, the ganglion cells respond well to the flashing spot. 
When the windmill is spinning, as is shown on the right hand column, the transient 
bipolar terminals generate a response to the leading edge of the windmill vanes. The wide 
field amacrine cells receive excitatory input from the transient bipolar terminal responses 
to the vane, and consequently release inhibitory neurotmnsmitter over a wide area as 
shown in in the right column. Because inhibition is being continuously generated by the 
spinning windmill, the response of the ganglion cells across the retinal patch has a large 
Stationary Windmill 
Transient 
I Spinning windmill 
Bipolar Terminal 
Wide field 
Ganglion cell 
Fig. 7 - Ganglion Cell Inhibition Caused By Spinning Windmill 
bowl shaped area of hyperpolarization which reduces the ganglion cell response of the 
cells to the central test flash. This is seen by the fact that the height of depolarization in 
the centrally located ganglion cells is much smaller under conditions of a spinning 
windmill than if the windmill is stationary. This is consistent with the results found 
experimentally which are illustrated in Figure 1. Experimental data not yet attained, but 
which are predicted by the model simulations illustrated in Figure 7, are the spatial 
patterns of activity generated in the bipolar, amacrine, and ganglion cells in response to 
the different stimuli. 
6 SUMMARY 
Using computer simulation of a neurophysiologically based model, we demonstrate that 
the experimental data describing properties of four neurons in the inner retina are 
compatible with the hypothesis that these neurons are involved in the detection of change 
and the feedforward of change-sensitive inhibition to ganglion cells. First, we build a 
computational model of the hypothesized four neuron circuit and determine that the 
proposed interactions between them are sufficient to reproduce many of the observed 
network properties in response to a puff of neurotransmitter. Next, we integrate this 
model into a full retina model to simulate their influence on ganglion cell responses. 
The model verifies the consistency of presently available data, and allows formation of 
predictions of neural activity are subject to refutation or verification by new experiments. 
We are currently recording the spatio-temporal response of ganglion cells to moving 
stimuli so that direct comparisons to these model predictions can be made. 
References 
Barnes, S. and Werblin, F.S. (1986). Gated currents generate single spike activity in 
amacrine cells of the tiger salamander. Proc. Natl. Acad. Sci. USA 83:1509 - 1512. 
Barnes, S. and Werblin, F.S. (1987). Direct excitatory and lateral inhibitory synaptic 
inputs to amacrine cells in the tiger salamander retina. Brain Res. 406:233 - 237. 
Eliasof S., Barnes S. and Werblin, F.S. (1987). The interaction of ionic currents 
mediating single spike activity in retinal amacrine cells of the tiger salamander. J. 
Neurosci. 7:3512 - 3524. 
Maguire, G., Lukasiewicz, P. and Werblin F.S. (1989). Amacrine cell interactions under- 
lying the response to change in the tiger salamander retina. J. Neurosci. 9:726 - 735. 
Teeters, J.L., Eeckman, F.H., Werblin F.S. (1991). A computer model to visualize 
change sensitive responses in the salamander retina. In MA. Arbib and J-P. Ewert (eds.) 
Visuomotor Coordination: Amphibians, Comparisons, Models and Robots. Plenum. 
Werblin, F.S. (1972). Lateral interactions at inner plexiform layer of a vertebrate retina: 
antagonistic response to change. Science. 175:1008 - 1010. 
Werblin, F.S. and Copenhagen, D.R. (1974). Control of retinal sensitivity. III. Lateral 
interactions at the inner plexiform layer. J. Gen. Physiol. 63:88 - 110. 
Werblin, F.S., Maguire, G., Lukasiewicz, P., Eliasof, S., and Wu, S. (1988). Neural 
interactions mediating the detection of motion in the retina of the tiger salamander. Visual 
Neurosci. 1: 317 - 329. 
Wunk, D.F. and Werblin, F.S. (1979). Synaptic inputs to ganglion cells in the tiger 
salamander retina. J. Gen. Physiol. 73:265 - 286. 
