Computational modeling of an early evolutionary stage of the nervous system

BioSystems 54 (1999) 77–90

Computational modeling of an early evolutionary stage ofthe nervous system

John AlbertDepartment of Comparati6e Physiology, Department of History and Philosophy of Science, Lorand Eot6os Uni6ersity,

Pazmany Peter setany 2, Budapest, H-1117, Hungary

Received 8 October 1998; received in revised form 13 July 1999; accepted 3 September 1999

Abstract

The object of this work is to create a computational model that examines the early evolution of the nervous systemin relation to adaptive behavior. The main questions are: how did the nervous system and the most primitive formsof intelligence came into being, how a system can be organized during evolution that is able to ensure the adaptivebehavior of a being, what are the basic rules of construction that are sufficient to create a workable nervous systemwithout specifying the details of the construction. The biological bases of the model are the phyla Cnidaria andPorifera as they stand at the beginning of the genesis of nervous organization. We found in our model that in anetwork of homogenous epithelial-like cells, which is considered the starting point of the genesis of the nervoussystem, the changes that have positive influence on the behavior are those that make the spreading of the electricpotential more efficient. It can cause the increase of the effectiveness of the behavior by itself without creating newspecific cell-types. There are some alternatives to increasing the effectiveness of spreading of stimuli, for exampleincreasing the value of biophysical parameters of the cells, or increasing the density of nerve cells and the number ofsynapses. If during the evolution a sort of cell comes into being that is able to conduct electrical stimuli — even ina rudimentary way — it can increase the adaptivity of behavior by itself without the need for specific informationof how to organize the construction of this system. © 1999 Elsevier Science Ireland Ltd. All rights reserved.

Keywords: Computational neuroethology; Artificial life; Animat; Adaptive behavior; Cnidaria ; Diffuse nervous system

www.elsevier.com/locate/biosystems

1. Introduction

The field of research named ‘artificial life’ offersa new approach to modeling biological systems.We can use it to better understand how the ner-vous system functions by examining a model of

the nervous system and the resulting behaviortogether. This makes it possible to observe theeffects of changes to the nervous system on theadaptivity of behavior.

2. Computational neuroethology

The neural bases of the intelligence and behav-ior are as yet mainly unknown. We can gain someE-mail address: [email protected] (J. Albert)

0303-2647/99/$ - see front matter © 1999 Elsevier Science Ireland Ltd. All rights reserved.

PII: S 0303 -2647 (99 )00065 -9

J. Albert / BioSystems 54 (1999) 77–9078

understanding by studying species that have muchsimpler nervous systems than humans, because inthem the connection between structure and func-tion are much more apparent. This makes the taskof modeling them easier. We can call this ap-proach ‘the worm’s-eye view of intelligence’. Thistrend is followed by neuroethology and its newerversion, called computational neuroethology(Beer, 1990; Cliff, 1994). The latter is distin-guished from other trends of computational mod-eling of the nervous system in that it examines theneural mechanisms that take part in creating be-havior. The nervous system and its functioning isnot modeled by itself, but as a part of the wholeliving organism, creating a consistent system —similar to real biological organisms. To achievethis we have to model the whole sensory-motorapparatus that contributes to the creation of be-havior and put it into the suitably formed modelof a body. This type of autonomous agents iscalled ‘animats’ (Wilson, 1991; Guillot andMeyer, 1994). They are put into a simulated envi-ronment, so the functioning of the model nervoussystem will be revealed by the behavior of theanimat in that particular environment. This en-ables us to model the neural control of behaviorand to study the interaction between the nervoussystem, behavior and the environment.

Behavior — a sequence of actions — is theresult of the interaction between the animal (oranimat) and its environment. Behavior is regardedadaptive if the animat responds to environmentalstimuli in ways that promote the survival of theorganism (Meyer and Guillot, 1994). Adaptivebehavior is a broad ability to cope with the com-plex, dynamic, unpredictable world in which thegiven organism lives. A trait is adaptive if itcontributes to an organism’s overall survival.Strictly speaking, ‘adaptive behavior’ means be-havior which is adjusted to environmental condi-tions (Beer, 1990).

3. The problem of fitness

We would like to stress that the concept offitness has another meaning in these models thanin population biology. In the latter case fitness is

a mathematically well-defined term, a statisticalparameter that is calculated on the basis of thegene frequencies in the descendant population. Inour models we use the term ‘fitness’ to describethe assessment of the indi6idual performances,which is the basis for selection. In this case fitnessindicates individual suitability, so it has an etho-logical rather than a population biological mean-ing. It is a less exact concept and has moreintuitive elements.

The simplest but most frequently used and life-like method that serves for the assessment of theperformance of the modeled phenotype is a ‘bi-nary fitness function’, which tells whether thegiven individual survives or dies at the end of asimulation step (Michel and Biondi, 1994). It isapplicable when the task of the model organism isto try to find food. If their behavior control is notgood enough, the individual ‘starves to death’.Otherwise it ‘survives’ the trial (Nolfi and Parisi1991; Nolfi et al., 1994a).

4. Beings of the synthetic world

The computer-simulated animals can beclassified by the features of their nervous systemsthat enable them to behave in an adaptive way.One group of animats had a carefully plannedand precisely wired neural net. In this groupeffective functioning is the result of profoundknowledge of the neuroanatomy and physiologyof the modeled species. Such a model mimics aconcrete species and is able to generate the char-acteristic behavior of that species (Beer, 1990;Cliff, 1994).

Another type of model tries to minimize thepreprogrammed design of the nervous system, sothe behavior of the animat becomes more efficientby its own ‘experiences’. If the environmentchanges, the animat is able to adapt to the newcircumstances by modifying its behavior (Cecconiet al., 1995; Kodjabachian and Meyer, 1996). Toachieve this we have to apply mechanisms that areboth able to ensure the plasticity of the nervoussystem and have a biological basis. One of thesemethods is Hebbian learning, when the synapticweights are modified according to the Hebbian

J. Albert / BioSystems 54 (1999) 77–90 79

rule, hereby the probability of adaptive reactionsincreases in a given situation.

The plasticity of the neural net is more manifestin models where the number, the position and theconnections of the neurons are the result of anontogenetic process, i.e. one that is influenced byboth the information encoded in the genome andthe effects of the environment. Changes in thegenome of the successive generations (because ofmutation and crossing-over) also increase the pos-sible variations of the neural net and the probabil-ity of development of the most adaptive behaviortoo (Nolfi and Parisi, 1995).

Neural networks that are created to study theregulatory mechanisms underlying adaptive be-havior are called ecological neural networks oreconets (Parisi et al., 1990). The object of thesemodels are not to reproduce the structure of thenervous system and behavior of a certain species,but to study ways in which simple rules andinteraction with the environment can produceadaptive behavior.

5. The animat and its environment

5.1. Anatomy

One of the conclusions we can draw from thestudy of ecological neural networks is that over-simplified body structure, suitable for modelingrobots with an adaptive behavior, is not appropri-ate to model the behavior of biological organisms.An animal body cannot be regarded as a roboteven if we have to simplify it during the modelingprocess. In the case of a robot we can separate thebody from the neural net. The neural net can betreated as an interchangeable module; we onlyhave to connect the motors to the appropriatemotoneurons. In the case of biological beingseven the simplest movements require the coordi-nated work of many muscles and their effectdepends on their positions in the body. To modelthe movement of an animal we have to take intoaccount the body structure, because it defines thepotential modes of behavior. It follows from thisthat the construction and evolution of the nervoussystem are not separable from the anatomy andmorphogenesis of the modeled animal.

We have created a model-animal (animat) withthe characteristics of the most primitive Cnidari-ans. The animat is a tube-like organism corre-sponding to the gastrula-state, which is similar tothe body structure of a Hydra. These animalshave in fact the most primitive nervous system, soit is not unreasonable to imagine our animat as aHydra-like being without tentacles (Fig. 1.). Thisis suitable for our purpose, because these animalshave no special locomotor organs that wouldrequire an advanced neural apparatus for theiroperation (Bullock and Horridge 1965; Mackie1990a; Spencer 1991). The locomotor apparatusof the animat consists only of the muscles of thebody wall and their motoneurons. It allows thepossibility of simple movement of the body, forexample curving and crumpling, like the move-ment of the body column of Hydra and somespecies of Porifera. In the current model ouranimat is sessile, as are many species of Cnidariaand all of Porifera.

The movement of the animat is produced byfour longitudinal muscle-strips along the bodyconsisting of many muscle cells. The activation ofa muscle cell causes the contraction of one sectionof the body. This causes a very limited movementby itself. Large-scale movement is possible only ifmany muscular elements work in harmony witheach other. This simple model simulates motionas dependent on anatomical relations. It avoidsthe oversimplification of assigning the firing ofsingle motoneurons to complicated movementsthat are the result of well-coordinated functions.

5.2. Histology

The body of the animat consists of three cell-types, epithelial cells, muscle cells and nerve cells.All of them take part of the formation ofbehavior.

5.2.1. Epithelial cellsThe representation of this type of cell in our

model is motivated by the fact that in animalswith primitive nervous systems, mainly Cnidari-ans, the role of epithelial cells is similar to that ofnerve cells. These cells are able to receive stimuli


and to conduct electric potential in a passiveway and even to operate muscle cells (Anderson,1980; Spencer, 1991; Ranganathan, 1994). InPorifera (which have no nervous system) thesecells also play an important role in the conduc-tion of stimuli (Lawn, 1982). Many researchersderive nerve cells from them (Mackie, 1970;Robson, 1975). These cells are found mainly inthe layer of cells that covers the outside of thebody. In our model these cells give the ‘frame-work’ of the body, defining its shape andboundary. The layer of epithelial cells serves asanchor points for the muscle cells as so themovements of the animat will result in thechange of position of the affected epithelialcells. These cells therefore have a double role inthe model, as in nature: they serve as a kind of

skin, and they take part in receiving and con-ducting stimuli.

5.2.2. Muscle cellsThe organization of muscle cells into functional

units is discussed above. These cells form a ho-mogenous population similarly to the epithelialcells, which means that all of them have the sameproperties. One of these is the stimulus threshold,another is the number of nerve cells that caninnervate the same muscle cell and a third is theability of receiving stimuli from epithelial cells. (Itis well known that in Cnidarians more than onemotoneuron can innervate the same muscle celland a motoneuron can take part in the innerva-tion of more than one muscle cell.) These featuresare encoded in the model genome.

Fig. 1. The body structure of the animat. The body of the modelled animal (animal) is quite simple, and it has the characteristicsof the most primitive Cnidarians. It is a tube-like being corresponding to the gastrula-state, which is similar to the body structureof a Hydra without tentacles. It is suitable for our purpose, because these animals have the most primitive development of thenervous system. The body of the animat consists of three celltypes, all of them take part of forming the behavior. They are epithelialcells, muscle cells and nerve cells.


5.2.3. Ner6e cellsNerve cells can be receptors, interneurons or

motoneurons by their functioning. But at lowerlevels of the evolution these functions have notdifferentiated from each other yet, so in Cnidarians-mainly in Hydra, which have the most primitivenervous system — the same neuron can carry outmore than one function (Westfall and Kinnamon,1978). Evolution leads from less differentiatedprimitive nerve cells with multiple functions tohighly specialized neurons. The less differentiatedstate is characteristic not only of the function butalso of the anatomy of primitive nerve cells. Theprocesses of the cells cannot be divided into den-drites and axons (Mackie, 1990b). Accordinglydifferent functions do not preclude each other inthe same nerve cell and the processes are consideredequivalent in our model.

Connections between cells are not determined inadvance, therefore synapses of a particular cell arenot encoded in the genome (indirect encoding).From a biological point of view this solution iscloser to reality than encoding of the completeconnection-matrix in the genome (direct encoding)(Balakrishnan and Honavar, 1995). The formationof the synapses of a particular nerve cell is deter-mined by the number and the length of the pro-cesses of that cell and the number of potentiallysynaptic partner cells that are in reach.

Unlike epithelial and muscle cells, nerve cells donot necessarily form a homogenous cell population,they can develop several cell populations havingdifferent properties. Characteristics of cells belong-ing to the same group are uniform, therefore onlythe properties of the cell populations are encodedin the model genome, instead of the particular dataof individual cells.

Based on the theory that nerve cells are derivedfrom epithelial cells, we select the characteristics ofnerve cells so that they are similar to epithelial cells(e.g. the conductivity of the nerve cells can bepassive conduction with decrement).

Characteristics of passive conductivity of cells —time-constant (t) and space-constant (l) — arealso built into the model. The time-constant is thelength of time during which the value of membranepotential decreases to 1/e. The space-constant ofthe membrane is the distance where the membrane

potential decreases to 1/e (Ganong, 1987). Both ofthem are derivated from the cable-equation thatdescribes the passive electric properties of biologi-cal membranes.

The object of this work is modeling the earliestevolutionary state of the nervous system. We in-clude no inhibitory interneurons, because theirexistence is not proved in Cnidarians.

5.3. Genetics

The basic information about the constructionand functioning of our animats are encoded in a‘genome’. The ‘life’ of an animat begins with a short‘ontogeny’, during which they develop on the basisof information derived from the genome. Thisphase is similar to that studied in the evolutionarymodeling of neural networks (Nolfi and Parisi,1994; Balakrishnan and Honavar, 1995). In ourmodel however genotypes do not go through anevolutionary process directed by genetic al-gorithms. The possibility is included in the modelfor future work, but in this phase we only use theability to create the ‘phenotypes’ (animats equippedwith a simple body and a nervous system) on thebasis of encoded data. This automates creatinganimats, and supports our objective of studyinghow a workable nervous system comes into being,based only on some general information about itsconstruction.

We have applied the principle of indirect encod-ing. This means that not all the specific data ofevery cell are encoded, but only the most importantgeneral rules and data, on the basis of which thenerve cells and their connections can be created. Inthis case different phenotypes belong to a certaingenotype (Nolfi et al., 1994b; Nolfi and Parisi,1995). This solution stands closer to reality, and italso means having to store less data.

The genome consists of four main parts (Fig. 2.).General information about the size and construc-tion of the body is encoded in the first part, whichdetermine the morphology of the animat. If thetube-like body is laid out, the position of the cellscan be described by x- and y-coordinates. Theirmaximum values give the size of the body. Thenumber of cells and the number of nerve cellpopulations are encoded in the first part of thegenome as well.


Fig. 2. ‘Gene-mapping’ of the animat. The basic information about the construction and functioning of the animats are encoded ina ‘genome’, so their ‘life’ begins with a short ‘ontogeny’, when they develop on the basis of this information. We have applied theprinciple of indirect encoding. It means that not all of the specific data of every cell are encoded, but only the most importantgeneral rules and data, on the basis of which the cells and their connections can be created. The genome consists of four main parts.General information about the size and construction of the body are encoded in the first part, which determine the morphology ofthe animal. In the second part the data of muscle cells are encoded, while in the third part the properties of epithelial cells. Thelargest part of the genome is the fourth, which stores data of nerve cells. These cells can form different populations, so this partconsists of as many sections as there are populations. All of the data are encoded in a single ‘chromosome’, so every part of thegenome begins with a ‘start’ sign, that shows the beginning of data belong to a cell type. In the recent model we do not use all ofthe data encoded in the genome, they are only possibilities for future works. Parameters in the first part of the genome:� Maximum value of the x- and y-coordinate: if we ‘lay out’ the tube-like animat (see Fig. 1) the x-coordinate gives the horizontal

size and the y-coordinate gives the vertical size of the body. All of the cells have to take place between these coordinates.� Number of neuron-populations: This value gives the maximum number of types of nerve cells in an animal. A certain population

consists of nerve cells with the same properties (see the fourth part of the genome).� Maximum number of neurons: Gives the maximum number of nerve cells (all types) of a certain animal. The number of nerve

cells of an animat can be less than this value, but cannot be more.Parameters in the second part of the genome:� Number of cells: gives the number of muscular cells. (The cells form four regular longitudinal muscle-strips, so the size of the

body determines their coordinates.)� Connection with epithelial cells: This parameter shows whether muscular cells are able to receive stimuli from epithelial cells or

not.� Threshold: the strength of the stimulus that can cause the contraction of the muscle cell.Parameters in the third part of the genome:� Number of cells: gives the number of epithelial cells. (This type of cell gives the ‘framework’ of the body and they seat in a regular

way, so the size of the body determines their coordinates.)� Receptor function: this parameter shows whether the epithelial cells are able to receive stimuli from the environment similarly to

the most primitive nervous systems, or not.� Time-constant: the length of time during which the value of membrane potential decreases to 1/e.� Space-constant: the distance where the membrane potential decreases to 1/e. In primitive nervous systems the role of epithelial

cells is similar to that of nerve cells, so they are able to conduct electric potential. These two parameters give the characteristicsof the passive conduction.


The data of muscle cells are encoded in thesecond part. In the third part of the genome theproperties of epithelial cells are encoded. Bothof them make homogenous cell populations andplay an important role in shaping the frame-work of the body. At the earliest evolutionalstate epithelial cells can take part in operatingmuscular cells, therefore data about this func-tion can be found in the genome.

The largest part of the genome is the fourth,which stores data of nerve cells. These cells canform different populations, so this part consistsof as many sections as there are populations.The value of the x- and y-coordinates of a par-ticular cell is not encoded, just the range of co-ordinates where the cells of that population canbe found. The coordinates are given randomvalues between these limits when the phenotypeis instantiated. The connection-matrix of thenerve cells is not encoded either, just the maxi-mum length and the maximum number of theprocesses and the types of actual cells that canbe potential synaptic partners for a particularnerve cell population. The actual connectionsare determined by the number of cells of appro-priate types within reach. All of the data areencoded in a single ‘chromosome’.

5.4. En6ironment

The environment and the task of the animatin our model is similar to those of ecologicalneural networks (Parisi et al., 1990; Nolfi et al.,

1994b). The environment is modeled as a box(Fig. 3.). As the animats are sessile, the interac-tion between the individuals would be minimal,so we perform the tests with only one individualin the box at a time.

The stimulus is the appearance of a piece offood, which slowly sinks to the bottom of thebox. This is a chemical stimulus for the animatand its strength decreases exponentially with thedistance. If the animat is able to catch at leastone piece of food, the test is continued. Thefeeding is successful if the animat not onlytouches the piece of food, but it also ‘eats’ it, inother words, it gets food particles into its coe-lenteron through the mouth (Fig. 4.). The mostsuccessful individuals are those that can catchthe most pieces of food.

6. Experiments

Investigation of the model consisted of severalparts, during which the program was run with anumber of different settings of the parameters.The purpose was to test and to ‘calibrate’ theabilities of the animat and to establish relations— if they exist — between the behavior and thevalues of these parameters. During the tests westudied the effect of the time-constant and space-constant of the epithelial cells and nerve cells, thenumber of nerve cells and the maximum length ofneural processes on the adaptivity of the behaviorof the animat.

Fig. 2. (Continued)Parameters in the fourth part of the genome:� Number of neurons: gives the number of nerve cells belonging to a certain population.� Minimum and maximum value of x- and y-coordinates: appoint the part of the body where the cells of a certain population can

seat. (This can be the whole body, or a part of it, e.g. the hypostome or the basal disk.)� Maximum length of connections: gives the maximum length of neural processes. (In Cnidarians we cannot distinguish axons and

dendrites, so we need only one parameter for the description.)� Maximum number of connections: gives the maximum number of the neural processes. (A certain nerve cell can have a smaller

number of neurites than this value, but does not have more.)� Receptor function: this parameter shows whether nerve cells are able to receive stimuli from the environment.� Threshold; value of action potential; length of refractory period: these are the characteristics of action potential, but we did not

use them in the model in question.� Time-constant; space-constant: see above.� Cell types for connections: Gives the cell types that can be potential synaptic partners for a certain nerve cell population.


Fig. 3. The animat and its environment The environment andthe task of the animat in our model is similar to those ofecological neural networks. The environment is a box and theanimats are put into it. As the animats are sessile, the interac-tion between the individuals would be minimal, so we performthe tests with only one individual in the box at a time. Theenvironmental stimulus is the appearance of a piece of foodthat slowly sinks to the bottom of the box. It is a chemicalstimulus for the animat and its strength is decreasing with thedistance exponentially.

nervous system — can be sufficient to control thebasic reactions of a simple living being to theenvironmental stimuli, which agrees with the re-sults of physiological experiments.

This fact probably had great importance duringthe early evolution of the nervous system, becauseit means that if a cell-type arises that is able toconduct electrical stimuli in addition to its origi-nal function, even if in a primitive way, it canbenefit a simple organism by itself without havingto organize into a specially constructed system. Itcan serve as a starting point to the evolution of ahighly developed controlling system. Our modelshows that the increase of the effectiveness ofconducting stimuli alone is able to increase theadaptivity of the organism, which suggests a pos-sible direction for the evolution.

The relation between the fitness and the effec-tiveness of the conduction can be demonstratedby changing the time- and space-constant. In na-ture, however biophysical parameters can changeonly to a limited extent. Therefore evolution hadto find another solution, discussed next.

7.2. The effect of the number of ner6e cells onthe beha6ior

In further experiments we studied the changesof the behavior of the animats when epithelial-likeprotoneural cells appear in the primitive nerve-free conducting system as created in the previousstep. This corresponds to the evolutionary stepduring which the ancestors of nerve cells comeinto being from epithelial cells and begin to orga-nize to a simple network. In this phase nerve cellsare rather similar to epithelial cells, because theyare able to connect only with their close neigh-bors. The conduction is decremental, so the net-work is a ‘protoneuronal system’ that is similar tothe present Hexactinellida (Mackie et al., 1983).The time- and space-constants of the nerve cellsare similar to those of epithelial cells. They havesuch a low value that animats with nerve-freenetworks cannot feed successfully. Therefore thechanges of the behavior must be explained by theappearance of nerve cells.

We found that the success of behavior is deter-mined by the space-constant and the number of

7. Results and discussion

7.1. The effect of the time- and space-constantof the epithelial cells on the beha6ior

First we studied the behavior of animats thathave only epithelial and muscle cells, but no nervecells. This group forms our control group. Thetime-constant of the epithelial cells was studied inthe range between 1 to 50 ms (the physiologicalvalue is about 1–20 ms) and the space-constantbetween 1 to 10 mm (the physiological value isabout 1–5 mm).

Our results show that the excitability and thesuccess of feeding are determined basically by themeasure of the space-constant, and the time-con-stant modifies it only slightly (Fig. 5.). The successof feeding grows with the space-constant signifi-cantly if its measure is close to the physiologicalvalue. The epithelial cells are able to provide aminimally adaptive level of the behavior for theanimat, similar to the behavior of nerve-free Hy-dra (Campbell et al., 1976).

On the basis of these results we can say that ahomogenous, diffuse conducting network —which is probably similar to the ancestor of the


nerve cells together (Fig. 6.). The number of hits(pieces of food that are caught and eaten) in-creases with the number of nerve cells and thistendency is more pronounced if we increase thevalue of the space-constant. Comparing this withthe results of the nerve-free conducting system,we found that among animats with the sameparameter value, those with a nerve-free con-ducting system are not able to respond to envi-ronmental stimuli, but the appearance andmultiplication of nerve cells increases the successof feeding behavior. The nerve-free conductingsystem can have a similarly successful perfor-mance only if the space and time-constant havean extremely high value.

These results suggest that at the most primi-tive evolutionary state of the nervous system the

ability to respond to environmental stimuli canincrease by increasing the number of elements ofthis system alone. It may have a great evolution-ary importance because at the beginning of thenervous organization increasing the number ofcells may be the simplest way to increase theeffectiveness. At this level of the evolution thereis no need for developing complex centers in thenervous system to increase its effectiveness, sothere is no need to store organizational informa-tion in the genome. Fitness can be increasedmerely by increasing the number of nerve cells.This could play an important role during theearly evolution of the nervous system. Our tenetseems to be justified by the fact that it is notanimals with the smallest number of nerve cellsthat have the most ancient nervous system.

Fig. 4. The movement of the animat. If the animat is able to catch at least one piece of food the test is continued. The feeding issuccessful if the animat not only touches the piece of food but it also ‘eats’ it, in other words it has to get it into its coelenteronthrough the mouth.


Fig. 5. The number of hits depending on the time- and space-constant of the epithelial cells, in the case of the nerve-free network.The success of feeding is indicated by the number of hits (pieces of food that are caught and eaten). It is determined basically bythe measure of the space-constant, and the time-constant can modify it only to a slight degree. The success of feeding grows withthe space-constant significantly. If its measure is close to the physiological value (about 1–5 mm), the epithelial cells are able toensure a minimally adaptive level of the behavior for the animal, similarly to the behavior of nerve-free Hydra.

7.3. The effect of the connections of ner6e cellson the beha6ior

Next we studied how the length of the processesof nerve cells, which determines the maximumdistance of connections, influence the success ofbehavior. The length of the processes is specifiedas a proportion of the cell size. During the simula-tion this length ranged between the 10- and 50-fold size of the nerve cells. In natural animals with

the simplest nervous systems we cannot find nervecells with longer processes than this.

We found that the success of behavior increasesonly a little with the length of the processes, andthe degree of increase depends on the number ofnerve cells at hand (Fig. 7.). If the animat has lessthan 200 nerve cells, the length of processes (andwith this the maximum distance of connections)has no effect on behavior. Considerable influencecan be seen with a large number of nerve cells, but


the main factor is not really the length of pro-cesses, but the ability to develop processes at all.If the cells are able to create them, the behavior isinfluenced favorably even with short processes. Ifthe number of cells is between 200–500, thegrowth of processes has no additional effect. Ifthe number of cells is more than 500, the adaptiv-ity increases with maximum process length.Therefore successful behavior depends first of allon the number of nerve cells, and secondarily onprocess length.

The effects due to the process size (these effectsalso depend on the numbers of nerve cells) can beexplained by observing that nerve cells have toreach a critical density to organize a workablenetwork. In the cases with small numbers of nerve

cells their density is not enough to get close toeach other and they cannot form connections.

Though the number of connections increaseswith the size of the processes, they are insufficientto effect behavior. The effect of increasing thedensity of nerve cells at the first stage this growthis not due to the development of a well-function-ing neural net, but to the increasing number ofnerve cells that get close to the muscle cells. Thesewill function as motorneurons, innervating musclecells directly. It is common among Cnidarians thatnerve cells have more than one function. They canserve as receptors and innervate a muscle cell(motoneuron function) at the same time (Westfalland Kinnamon, 1978). Therefore a certain level ofadaptive behavior can be achieved even if the

Fig. 6. The number of hits depending on the number of nerve cells in the case of different values of time- and space-constant Thesuccess of feeding behavior is determined by the space-constant and the number of nerve cells together. If the value of thespace-constant is too low, the animat is not able to respond to environmental stimuli. But if the space-constant is high enough, thenumber of hits (pieces of food that are caught and eaten) increases with the number of nerve cells and this tendency is more definiteif we increase the value of the space-constant


Fig. 7. The number of hits depending on the number of nerve cells and the maximum length of connections. The success of feedingbehavior strongly depends on the number of nerve cells, but increases only a little with the length of the maximum distance ofconnections (that is determined by the length of the processes of the nerve cells).

organization of the nerve cells into a network is atan early stage.

8. Summary

Summarizing the results we can say that in anetwork of homogenous epithelial-like cells, whichis considered as the starting point of nervousorganization, the changes that influence adaptivityare those that make the conductivity more effi-cient. We found that such changes can cause theincrease of the effectiveness of the behavior byitself, without any differentiation in the networkor any development of special cell-types. There arenumerous ways to increase the conductivity in thenetwork. One of them is the increase of the space-

constant of cells in case of passive decrementalconduction. Biophysical parameters in living or-ganisms can change only to a limited degree, butwe can experiment more freely during the simula-tion. Our results show that the effectiveness of thebehavior increases significantly with conductivity.The model illustrates that evolution had foundanother solution to increase the effectiveness offorwarding electrical stimuli without increasingthe space-constant in an extreme way, and it wasaction potential. Another way to increase theeffectiveness of functioning is increasing the den-sity of nerve cells, which is perhaps the simplestway in biological systems to achieve this aim.

The simple ways of increasing the adaptivity ofthe behavior probably played an important roleduring the early evolution of the nervous system,


because at this stage the genome of the animalscould not contain any information that encodedthe construction and functioning of an effectivenervous system, since one could not exist yet, onlyas a potentiality, because a new cell type hadcome into being that had the ability to do the job.Therefore the organization of this system pro-ceeded in an ad hoc way at the first stage, and thisis the point where it has a great importance if thisstate of the system was able to increase the chanceof surviving at all.

Our model supports the hypothesis that such aprimitive system — without having detailed infor-mation about the organization — is able to influ-ence the behavior of a simple-constructed animalin an adaptive way.

The effectiveness of this simple system can in-crease significantly by changing merely quantita-tive parameters, therefore the factors thatdetermine the adaptivity of the behavior can re-duce to quantitative attributes. All of these sug-gest that the way leading to the ancestral forms ofthe nervous system could be realized by simplesteps. Perhaps this is not obvious if we only takean experimental look at the sophisticated ‘instru-ment’ that directs the behavior of even the sim-plest real-life Hydra.

References

Anderson, P.A.V., 1980. Epithelial conduction: its propertiesand functions. Prog. Neurobiol. 15, 161–203.

Balakrishnan, K., Honavar, V., 1995. Evolutionary design ofneural architectures — a preliminary taxonomy and guideto literature, Artificial Intelligence Research Group; Tech-nical Report CS TR c 95-01.

Beer, R., 1990. Intelligence as Adaptive Behavior. An Experi-ment in Computational Neuroethology. Academic Press,New York, p. 1990.

Bullock, T.H., Horridge, G.A., 1965. Structure and Functionin the Nervous System of Invertebrates. Freeman, SanFrancisco, CA, pp. 450–506.

Campbell, R.D., Josephson, R.K., Schwab, W.E., Rushforth,N.B., 1976. Excitability of nerve-free hydra. Nature 262,388–390.

Cecconi, F., Menczer, F., Belew, R.K., 1995. Maturation andthe evolution of imitative learning in artificial organisms,Technical Report CSE 506; University of California, SanDiego 1995.

Cliff, D., 1994. Computational Neuroethology, Technical Re-port CSRP 338; to appear in: Arbib M.A. (Ed.), Hand-book of Brain Theory and Neural Networks. MIT Press.

Ganong, W.F., 1987. Review of Medical Physiology. PrenticeHall, Appleton-Lange, pp. 87–111.

Guillot, A., Meyer, J.A., 1994. Computer simulation of adap-tive behavior in animats. In: Computer Animation. IEEEComputer Society Press.

Kodjabachian, J., Meyer, J.A., 1996. Evolution and develop-ment of control architectures in animats. Robot. Auton.Syst. 16, 2–4.

Lawn, I.D., 1982. Porifera. In: Shelton, G.A.B. (Ed.), Electri-cal Conduction and Behaviour in ‘Simple’ Invertebrates.Clarendon Press, Oxford, pp. 49–72.

Mackie, G.O., 1970. Neuroid conduction and the evolution ofconducting tissues. Q. Rev. Biol. 45, 319–332.

Mackie, G.O., 1990a. The elementary nervous system revis-ited. Am. Zool. 30, 907–920.

Mackie, G.O., 1990b. Giant axons and control of jetting in thesquid Loligo and the jellyfish Aglantha. Can. J. Zool. 68,799–805.

Mackie, G.O., Lawn, I.D., Pavans de Ceccatty, M., 1983.Studies on hexactinellid sponges II. Excitability, con-duction and coordination of responses in Rhabdocalyptusdawsoni (Lambe 1873). Phil. Trans. R. Soc. B. 301, 401–418.

Meyer, J.A., Guillot, A., 1994. From SAB90 to SAB94: fouryears of animat research. In: Cliff, D., Husbands, P.,Meyer, J.A., Wilson, S.W. (Eds.), From Animals to Ani-mats: Proceedings of the Third International Conferenceon Simulation of Adaptive Behavior, The MIT Press/Brad-ford Books, Cambridge, Massachusetts.

Michel, O., Biondi, J., 1994. From the Chromosome to theNeural Network. Valbonne, p. 1994.

Nolfi, S., Elman, J.L., Parisi, D., 1994. Learning and evolutionin neural networks, Technical Report 94–08; Departmen-tof Neural Systems and Artificial Life, Rome, 1994.

Nolfi, S., Miglino, O., Parisi, D., 1994. Phenotypic plasticity inevolving neural networks, Technical Report PCIA-94-05;To appear in: Proceeding of the First Conference FromPerception to Action, 5–9 Sept. Lausanne.

Nolfi, S., Parisi, D., 1991. Growing neural networks, TechnicalReport PCIA-91-15; Department of Cognitive Processesand Artificial Intelligence, Rome 1991; to appear in Artifi-cial Life III.

Nolfi, S., Parisi, D., 1994. Phylogenetic recapitulation in theontogeny of artificial neural networks, Technical Report94; Institute of Psychology N.R.C. Rome.

Nolfi, S., Parisi, D., 1995. Genotypes’ for neural networks. In:Arbib, M.A. (Ed.), Handbook of Brain Theory and NeuralNetworks. MIT Press, p. 1995.

Parisi, D., Cecconi, F., Nolfi, S., 1990. Econets: neural net-works, that learn in an environment. Network 1, 149–168.

Ranganathan, R., 1994. Evolutionary origins of ion channels.Proc. Natl. Acad. Sci. USA 91, 3484–3486.


Robson, E.A., 1975. The nervous system in coelenterates. In:Usherwood, P.N.R., Newth, D.R. (Eds.), Simple NervousSystems. Edward Arnold, pp. 169–209.

Spencer, A.N., 1991. A protoneuronal system in sponges;Cnidarian nervous systems. In: Ladd Prosser, C. (Ed.),Neural and Integrative Animal Physiology; Compara-tive Animal Physiology. Wiley-Liss Publication, pp. 554–567.

Westfall, J.A., Kinnamon, J.C., 1978. A second sensory-mo-tor-interneuron with neurosecretory granules in Hydra. J.Neurocytol. 7, 365–379.

Wilson, S.W., 1991. The animat path to AI. In: Meyer, J.A.,Wilson, S.W. (Eds.), From Animals to Animats: Proceed-ings of the First International Conference on Simulation ofAdaptive Behavior, The MIT Press/Bradford Books, Cam-bridge, Massachusetts.

..

Computational modeling of an early evolutionary stage of the nervous system

Documents

Transcript of Computational modeling of an early evolutionary stage of the nervous system