Dopaminergic Neuromodulation Brings a 
Dynamical Plasticity to the Retina 
Eric Boussard 
Jean-Fran;ois Vibert 
B3E, INSERM U263 
Facult6 de m6decine Saint-Antoine 
27 rue Chaligny 
75571 Paris cedex 12 
Abstract 
The fovea of a mammal retina was simulated with its detailed bio- 
logical properties to study the local preprocessing of images. The 
direct visual pathway (photoreceptors, bipolar and ganglion cells) 
and the horizontal units, as well as the D-amacrine cells were sim- 
ulated. The computer program simulated the analog non-spiking 
transmission between photoreceptor and bipolar cells, and between 
bipolar and ganglion cells, as well as the gap-junctions between hor- 
izontal cells, and the release of dopamine by D-amacrine cells and 
its diffusion in the extra-cellular space. A 64x64 photoreceptors 
retina, containing 16,448 units, was carried out. This retina dis- 
played contour extraction with a Mach effect, and adaptation to 
brightness. The simulation showed that the dopaminergic amacrine 
cells were necessary to ensure adaptation to local brightness. 
1 INTRODUCTION 
The retina is the first stage in visual information processing. One of its functions 
is to compress the information received from the environment by removing spatial 
and temporal redundancies that occur in the light input signal. Modelling and 
computer simulations present an efficient means to investigate and characterize the 
physiological mechanisms that underlie such a complex process. In fact, filtering 
depends on the quality of the input image (van Hateren, 1992): 
559 
560 Boussard and Vibert 
1.High mean light intensity (high signal to noise ratio). A high-pass filter en- 
hances the edges (contour extraction) and the temporal changes of the input. 
2.Low mean light intensity (low signal to noise ratio). The sensitivity of high- 
pass filters to noise makes them inefficient in this case. A low-pass filter, averaging 
the signal over several receptors, is required to extract the relevant information. 
There are three aspects in the filtering adaptivity displayed by the retina: adaptivity 
to i) the global spatial changes in the image, ii) the local spatial changes in the 
image, iii) the temporal changes in the image. We will focus on the second feature. 
A biologically plausible mammalian retina was modelled and simulated to explore 
the local preprocessing of the images. A first model (Bedfer &: Vibert, 1992), that 
did not take into account the dopamine neuromodulation, reproduced some of the 
behaviors found in the living retina, like a progressive decrease of ganglion cells' 
firing rate in response to a constant image presented to photoreceptors, reversed 
post-image, and optic illusion (Hermann grid). The model, however, displayed a 
poor local adaptivity. It could not give both a good contrast rendering and a Mach 
effect. The Mach effect is a psychophysical law that is characterized by an edge 
enhancement (Ratlift, 1965). The retina network produces a double lighter and 
darker contour from the frontier line between two areas of different brightness in 
the stimulus. This phenomenon is indispensable for contour extraction. This paper 
will first present the conditions in which high-pass filtering and low-pass filtering 
occur exclusively in the retina model. These results are then compared to those 
obtained with a model that includes dopamine neuromodulation, thus illustrating 
the role played by dopamine in local adaptivity (Besharse &: Iuvone, 1992). 
2 METHODS 
The retina is an unusual neural structure: i) the photoreceptors respond to light by 
an hyperpolarization, ii) signal transmission from photoreceptors to bipolar units 
does not involve spikes, neurotransmitter release at these synapses is a continu- 
ous function of the membrane potential (Buser & Imbert, 1987). Only ganglion 
cells generate spikes. Furthermore, horizontal cells are connected by dopamine 
dependent gap-junctions. Dopamine is an ubiquitous neurotransmitter and neuro- 
modulator in the central nervous system. In the visual pathway, dopamine affects 
several types of retinal neurons (Witkovsky &: Dearry, 1992). Dopamine is released 
by stimulated D-amacrine and interplexiform cells. It diffuses in the extra-cellular 
space, and produces: cone shortening and rod elongation, reduced permeability of 
gap-junctions, increased conductance of glutamate-induced current among horizon- 
tal cells, increased conductance of the cone-to- horizontal cell synapse, and retro- 
inhibition on D-amacrine cells (Djamgoz &: Wagner, 1992). Our model focused on 
the adaptive filtering mechanism in the fovea that enables the retina to simultane- 
ously perform both high-pass and low-pass filtering. Therefore, dopamine action 
on gap-junction between horizontal cells and the retro-inhibition on D-amacrine 
cells was the only dopamine effect implemented (fig. 1). Our model included the 
three neuron types of the direct pathway - photoreceptors, bipolar and ganglion 
units - as well as two types of the indirect pathway - the horizontal and dopamin- 
ergic amacrine cells. Only the On pathway of a mammal fovea was studied here. 
Each neuron type has been modelled with its own anatomical and electrophysiolog- 
Dopaminergic Neuromodulation Brings a Dynamical Plasticity to the Retina 561 
release 
Figure 1: The dopaminergic amacrine units in the modelled retina. 
The connections of an On center pathway in the simulated retina. Photo: Pho- 
toreceptors. Horiz: Horizontal units. Bip: Bipolar units. Gang: Ganglion Units. 
DA: Dopaminergic Amacrine unit. DA units are stimulated by many bipolar units. 
With an enough ezcitation, they can release dopamine in the extracellular space. 
This released dopamine goes to modulates the conductance value of horizontal gap- 
junctions. 
562 Boussard and Vibert 
ical properties (W'issle & Boycott, 1991)(Lewick & Dvorak, 1986). The temporal 
evolution of the membrane potential of each unit can be recorded. 
3 RESULTS 
A 64x64 photoreceptors retina was constructed as a noisy hexagonal frame where 
photoreceptors, bipolar and ganglion units were connected to their nearest neigh- 
bours. Horizontal units were connected to their 18 nearest photoreceptors and bipo- 
lar units, with a number of synaptic boutons decreasing as a function of distance. 
They did not retroact on the nearest photoreceptor. This horizontal layer architec- 
ture produces lateral inhibition. Each modelled D-amacrine unit was connected to 
about fifty bipolar units. The diffusion of released dopamine in the extra-cellular 
space was simulated. The modelled retina consisted of 16,448 units and 862,720 
synapses. 
At each simulation, the photoreceptors layer was stimulated by an input image. 
Stimulations were given as a 256 x 256 pixel image presented to the simulated 64 x 64 
photoreceptor retina. Since the localization of photoreceptors was not regular, each 
receptor received the input from 16 pixels on the average. The output image was 
reconstructed using the ganglion units response. For each of the 4096 ganglion units 
the spike frequency was measured during a given time (according to the experiment) 
and coded in a grey level for the given unit retinotopic position. Thus, each simu- 
lation produced an image of the retina output. This output image was compared 
to the input image. 
The input image (stimulus) consisted here of one white disk on a dark background. 
The results presented, in fig. 2, were obtained after 750 ms of stationary stimula- 
tions. The stimuli were here a white disk on a black background. The inputs were 
stationary to avoid temporal effects owing to evolving inputs. Output images of 
stationary inputs, however, vanished after 1000 ms. The time was limited to 750 
ms to optimize the quality of the output image. 
Biological datas available on the conductance value suggest that in the mammalian 
retina the conductance does not remain constant and undergoes a dynamical tuning 
depending on the local brightness [?]. This provides a range of possible values for the 
conductance. The behavior of the model was tested for values within this range. 
Different values lead to different network behaviors. Three types of results were 
obtained from the simulations: 
1.Without dopamine action, the conductance values were fixed for all gap-junctions 
to 10-% (fig. 2-A). The output image rendered well the contrast in the input image, 
but did not display the Mach effect (low-pass filtering). 
2.Without dopamine action, the conductance values were fixed for all gap-junctions 
to 10-S (fig. 2-B). The low conductance value allowed a pronounced Mach effect, 
but the contrast in the output image was strongly diminished (high-pass filtering). 
This contrast appears like an average of the two brightness. Only the contour 
delimited by Mach effect allows the disk to be distinguished. 
3.With dopamine, the conductance values were initially set to 10-7S (fig. 2-C). The 
output image displayed both the contrast rendering and the Mach effect (locally 
Dopaminergic Neuromodulation Brings a Dynamical Plasticity to the Retina 563 
15 
:: 38 
40 
22 
34 
40- 
32- 
24- 
16- 
8- 
0- 
0 
40- 
32- 
24- 
16- 
8- 
0 
A 
11 22 34 45 56 
B 
12 2 36 48 60 
25 
35 
65 
52 
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o i 
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12 24 37 49 61 
Figure 2: ontour extraction (Mach effect) according to gap-junctions conduc- 
tances. 
On the left, results obtained after 750 ms of stimulation for an mage of a white 
disk on a black background. On the right, sections through the corresponding image. 
Abscissa: spike count; Ordinates: geographic position of the unit, from the left side 
to the middle of the left panel. A: without dopamine (fixed Ggap -- 10-6S). B: 
without dopamine (fixed Ggap -- 10-1S). C: with dopamine release (starting Ggap 
---- 10-7S). A gives a good contrast rendering, but no Mach effect. B gives a Mach 
effect, but there is an averaging between darker and lighter areas. C, with dopamin- 
ergic neuromodulation, gives both a Mach effect and a good contrast rendering. 
564 Boussard and Vibert 
adaptive filtering). 
4 DISCUSSION 
These results show that the conductance cannot be fixed at a single value for all 
the gap-junctions. If the conductance value is high (fig. 2-A), the model acts like 
a low-pass filter. A good contrast rendering was obtained, but there was no Mach 
effect. If the conductance value is low (fig. 2-B), the model becomes a high-pass 
filter. A Mach effect was obtained, but the contrast in the post-retinal image was 
dramatically deteriorated: an undesirable averaging of the brightness between the 
darker and the more illuminated areas appeared. Therefore in this model the Mach 
effect was only obtained at the expense of the contrast. A mammalian retina is able 
to perform both contrast rendering and contour extraction functions together. It 
works like an adaptive filter. To obtain a similar result, it is necessary to have a 
variable communication between horizontal units. The simulated retina needs low 
gap-junctions conductance in the high light intensity areas and high conductance 
in the low light intensity areas. The conductance of each gap-junction must be 
tuned according to the local stimulation. The model used to obtain the fig. 2-C 
takes into account the dopamine release by the D-amacrine cells. Here, the network 
performs the two antagonist functions of filtering. Dopamine provides our model 
with the capacity to have a biological behaviour. What is the action of dopamine 
on network? Dopamine is released by D-amacrine units. Then, it diffuses from its 
release point into the extra-cellular space among the neurons, reaches gap-junctions 
and decreases their conductance value. Thus the conductance modulation depends 
in time and in intensity on the distance between gap-junction and D-amacrine unit. 
In addition, this action is transient. 
5 CONCLUSION 
Thanks to dopamine neuromodulation, the network is able to subdivise itself 
into several subnetworks, each having the appropriate gap-junction conductance. 
Each subnetwork is thus adapted for a better processing of the external stimulus. 
Dopamine neuromodulation is a chemically addressed system, it acts more diffusely 
and more slowly than transmission through the axo-synaptic connection system. 
Therefore neuromodulation adds a dynamical plasticity to the network. 
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