Analog-to-digital conversion of information in the retina

doi:10.4236/ns.2011.31007

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Vol.3, No.1, 53-56 (2011) Natural Science

http://dx.doi.org/10.4236/ns.2011.31007

Analog-to-digital conversion of information in the retina

Andrey N. Volobuev, Eugeny. S. Petrov

Samara State Medical University, Samara, Russia; [email protected]

Received 17 November 2010; revised 19 December 2010; accepted 22 December 2010.

ABSTRACT

We considered the physiological mechanisms

of functioning of the retina’s neural network. It

is marked that the primary function of a neural

network is an analog-to-digital conversion of

the receptor potential of photoreceptor into the

pulse-to-digital signal to ganglion cells. We

showed the role of different types of neurons in

the work of analog-to-digital converter. We gave

the equivalent circuit of this converter. We re-

searched the mechanism of the numeric coding

of the receptor potential of the photoreceptor.

Keywords: Analog-to-Digital Converter; A Ganglion

Cell; Oscillator of Clock Frequency; Pulse; Intensity;

Neuron; Action Potential; the Retina; Photoreceptor;

Digital-to-Analog Converter

1. INTRODUCTION

Conversion of information in the retina is carried out

both by the photoreceptor and by the brachiate network

of neurons. Initial perception of the light pulse occurs in

the retinal photoreceptors - in rods and cones. Rods are

responsible for the black and white, twilight vision. They

are evenly distributed on the nervous tunic (the retina) of

eyeball, their total number is about 100 million units in

each retina [1]. Cones, which are 3 million units, are

responsible for the color and daytime vision. They are

more concentrated in the central part (fovea centralis) of

the retina (140000 cones on mm2).

Feature of the photoreceptors is the hyperpolarizing

nature of the receptor potential which is generated in

response to light stimulation. Figure 1 shows the time

dependence of the receptor potential Uin of single cones

that arises in response to short flashes of the light (10 ms)

by three different light intensities [2]. The potential Uin

subsequently proceed to the input of neural network of

retina.

In the darkness on the photoreceptor membrane rest-

ing potential of a little more (modulo) value of –20 mV

(see Figure 1). Curve 1 corresponds to the weakest in-

tensity 1

, curve 2—the light intensity 21

I, curve

3—the intensity 31



. Between the intensity of the

light flash and the receptor potential analog (logarithmic)

dependence was observed.

2. THE EQUIVALENT ELECTRIC

CIRCUIT OF THE NEURAL

NETWORK RETINA

Receptor potential Uin photoreceptor through synaptic

switching proceeds at the input of a bipolar neuron. At

Figure 2 the distribution of electrical signals along the

system retinal neurons is shown. Circuit of the neural

network adopted in accordance with [3], a slight simpli-

fication of concern, in particular, the amount involved in

information processing similar amacrine neurons. These

simplifications we will discuss further.

Neural network retina represents a slightly modified

circuit of the analog-to-digital converter (ADC) consis-

tent accounts [4]. Block diagram of the ADC, the corre-

sponding neural network retina is shown in Figure 3.

Each element of the ADC converts the analog signal

from the photoreceptor PR into digital code, corresponds

to a neuron. In particular, the comparator K corresponds

Uin

t, s

-10

-20

-30

-40

0.1

0.2

0.3 0.4

0.5

0.6

Figure 1. Time dependence of receptor potential of the rod by

its stimulation with a light flash: RP—the resting potential, I -

moment of light flash, Uin—receptor potential in mV at the

input into the neural network of the retina, t—time in seconds.

A. N. Volobuev et al. / Natural Science 3 (2011) 53-56

U2 + RP

Uin

U2 + RP

U1i

1,2

Figure 2. Schematic circuit of the neural network retina.

Showing photoreceptor PR and the following neurons:

B—bipolar, A—amacrine, I—interplexiform, G—ganglionic,

H—horizontal. U2 + RP—analog output potential of the I; U1i

—pulse voltage A; U3—analog output voltage of the B; K1,

K2—places the signal is interrupted (keys); AP—the action

potential. The dotted line shows the version of the signal pas-

sage from the I through the body of horizontal neuron H.

+ RP U

CP DAC

GC F

Figure 3. Equivalent circuit of a neural network retina: K1, K2

—keys.

diaxon B, digital-to-analog converter (DAC)—inter-

plexiform neuron I, pulse counter (PC)—ganglionic

neuron G, generator clock frequency GCF—amacrine

neuron A. Action potentials AP originally generated by

amacrine neurons and in the sequel, according to the

used encoding, through the excitation of ganglionic

neurons are transmitted to fibers of the optic nerve.

Horizontal neurons apparently feebly involved in the

analog-to-digital conversion. Their primary function is

providing contrast boundaries in the field of view visual

image [1]. Analysis of this neural network of the retina

problem in this article is not performed.

The purpose of this study is to examine the transition

of hyperpolarizing receptor potential of photoreceptors

in the pulse sequence of action potentials of ganglion

neurons.

Let us consider in more detail the equivalent circuit of

the ADC retina, shown in Figure 3. Showing items are

required for any variety of ADC. One of the most im-

portant elements is the generator of clock frequency

(GCF), which generates a continuous sequence of pulses,

in the simplest case—with a constant frequency. This

sequence of electrical pulses U1i is shown in Figure 4a.

It is known that amacrine neurons generate action po-

tentials [3], but until now their role is not clear [5].

There are identified by morphological and histochemical

methods about 30 species amacrine neurons [1]. There’s

also stated that amacrine cells “react to the inclusion and

turning off lights, which simply signaling about change

of lighting, regardless of its direction.” This correlates

well with their role GCF in the circuit ADC. A large

variety of amacrine neurons indicates about variability

parameters of ADC retina.

An important element of the ADC is the comparator K.

It does not work with pulsed signals, and compares the

two analog signals—the input to the neural network of

photoreceptor Uin and the output signal from digital-to-

analog converter (DAC), gradually and step increases to

ΔU

Figure 4. Graphs of the analog-to-digital conversion of recep-

tor potential in the retina: a—a sequence of AP amacrine neu-

rons, b—the accumulation with a step U of potential com-

parison U2+RP interplexiform neuron, RP—the resting poten-

tial of photoreceptors, c—a countable sequence (spike) AP of

ganglion neurons; d—the output signal of bipolar neurons.

A. N. Volobuev et al. / Natural Science 3 (2011) 53-56

5555

U2 + RP (see Figure 3). In case of equality Uin = U2 +

RP comparator generates a constant voltage to the U3,

see Figure 4d. This is consistent with the fact that bipo-

lar neurons do not generate action potentials. Signal for

bipolar neurons spreads like electrotonic [2].

DAC is a required element of ADC. It is discrete, with

a step U (Figure 4b), accumulates potential U2 + RP at

its output for comparison with the receptor potential Uin

photoreceptor (see Figure 3).

An interplexiform neuron feedback plays the role of

DAC. On the input of this neuron by its impulse

branches served U1i impulse voltage and on the output

occurs a step analog potential comparison U2 + RP. So

far the role and functioning of these neurons were not

described. Some authors [1] consider that the role of

these neurons is not very important and call them in-

ter-retinal.

Pulse counter in the ADC retina—is undoubtedly the

ganglionic neuron G (see Figures 2,3). Ganglionic neu-

rons direct the signal in the form of action potentials to

the central nervous system.

3. INFORMATION CODING BY A

NEURAL NETWORK RETINA

Work ADC retina begins from supply the receptor

potential Uin produced by the photoreceptors to the input

of the comparator K. By the closed keys K1 and K2 (see

Figure 3) the impulses from the GCF served on the

pulse counter, where they come out in the form of a se-

quence of action potentials, and on the DAC, where the

potential comparison accumulate U2 + RP (Figure 4b).

If the conditions arise Uin = U2 + RP comparator gener-

ates a voltage of U3 (Figure 4d), which breaks the out-

puts from the GCF by keys K1 and K2. It is also possible

to supply the signal U3, produced by the comparator K

(bipolar neurons), directly on ganglionic neurons G

(pulse counter PC) to stop the generation of pulses of

that neuron. Synaptic switching from the B on the G is

another analog of the key K1 in Figure 2. Thus, each

signal from the photoreceptor, ganglionic neuron (or

pulse counter) generates a strictly defined number of

pulses n-pulsed digital signal.

Consequently, the neural network retina carries nu-

meric coding of the analog signal at the output from a

photoreceptor with the help of a certain quantity of

pulses. In this case, the quantity of pulses generated by

the ganglion neurons is proportional to the amplitude of

the analog receptor potential, n ~ Uin.

If we consider that the duration of the signal rise Uin

(modulo) of approximately 0.1 s (see Figure 1) and the

duration of the action potential of ganglionic neuron is 1

ms (the frequency of pulsation in the optic nerve comes

up to 1000 pulses per second [6]), then to one receptor

potential of photoreceptor a ganglionic neuron generates

a spike, consisting of approximately 100 action poten-

tials. Figure 4c shows the conditional six pulses.

It is noteworthy that the signal passage U2 + RP along

the horizontal branches of a neuron H (see Figure 2).

Some bipolar neurons B (comparators) receive a signal

comparison U2 + RP from DAC (interplexiform neurons)

not directly but through the body of horizontal neurons

[3] (see Figure 2). This fact indicates the involvement of

horizontal neurons in work of the ADC of the retina, or

vice versa, the involvement of the ADC in the work of

horizontal neurons, there is a mutual influence of the

analog-to-digital conversion in the retina and the forma-

tion process of the contrast of the visual image. The in-

volvement of interplexiform neurons is in work of hori-

zontal neurons also noted in [1]. It is possible that the

signal, which passage from the I through the branches of

body H, need (or energetically favorable) to add the

stepwise changing of the voltage U2 to resting potential

H. However, the cause of the signal passage from the I

through the branches or body H requires further detailed

investigation.

The task of providing information about contrast

boundaries in the total amount of information sent to the

central nervous system is crucial. Early H. Helmholtz

pointed out the imperfections of the optical system of the

eye, and as a consequence, the poor quality of the image

on the retina [7]. Formation of a contrast boundary in the

field of view carry out by submitting a brake signal from

the horizontal neurons to bipolar neurons, in case, if the

photoreceptors, that are connected with these bipolar

neurons, provide a weak receptor potential. In this case,

approximate and strongly illuminated photoreceptors can

generate a large receptor potential and the brake signal

from the horizontal neurons doesn’t send on the bipolar

neurons, connected with them. The necessity for infor-

mation that weak receptor signals should not be involved

in the generation of potential comparison by interplexi-

form neurons may also be a cause of the signal passage

from the I through the branches or body H.

It should be noted that the circuit of the neural net-

work retina [3] is somewhat more complicated than that

is shown in Figure 2. In particular, the generator of

clock frequency GCF as an amacrine cells A is duplicate,

apparently, to give to ADC a greater reliability. There is

a feedback of pulse branch of interplxsiform (DAC) and

amacrine neurons (GCF) which is not shown in Figures

2,3. Perhaps it is necessary to stop the pulsation of GCF

by achieving equality Uin = U2 + RP, since otherwise, by

continuous work of GCF happen depletion amacrine

neurons. Perhaps this branch of interplexiform neuron

provides a more rigid circular connection of two dupli-

cated amacrine neurons in the retinal neural network.

A. N. Volobuev et al. / Natural Science 3 (2011) 53-56

4. CONCLUSIONS

Encoding an analog signal at the output from a photo-

receptor to a digital code is apparently one of the main

functions of the neural network retina. Retinal neurons

perform well-defined functions: a comparison of analog

signals, the generation of clock frequency, digital-to-

analog conversion, counting pulses. At the same time,

horizontal neurons in the retina carry out other important

tasks, such as staining of the visual image. Horizontal

neurons are involved in the work of an analog-to-digital

converter retina.

Fulfilled analysis indicates the importance of stimu-

lating the development of electropulse physiotherapy

procedures for diseases of the neural network retina.

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