- Email: [email protected]
A computational frame to study social behaviour in animals
ht. .I. Bio-Medical Computing,
19 (1986)
201-218
201
Elsevier Scientific Publishers Ireland Ltd.
A COMPUTATIONAL FRAME
TO STUDY SOCIAL BEI-IAVIOUR
IN ANIMALS
J. MIRAa, D. CABELLOa, A. FRAILEb, E.L. ZAPATAa and A.E. DELGADOa aDepartamento bDepartamento Madrid (Spain)
de Electrhica, Facultad de Ffsica, Universidad de Santiago de Compostela and de Fisiologia Animal, Facultad de CC. BioUgicas, Universidad Complutense de
(Received November 4th, 1986) This paper presents new methods and procedures for studying collective behaviour in rats. The animals are assumed to be indistinguishable one from another and the behaviour of the group is represented analysed and interpreted in terms of the temporal evolution of a finite state probabilistic automaton. The automaton states are defined by measures on the clustering degree considered as a social response variable. The electronic system developed to carry out the cluster analysis and the automatic control of the social behaviour in the experimental environment includes a multimicroprocessor interacting with a ‘social box’ in which, together with classical sensors and effecters, a phototransistor based position sensor is included. Preliminary experiments show the discriminative power of the cluster automaton concerning sexual differences and emotivity, as well as the extensive of a basic mechanism of clustering as a collective response to stress. Pharmacologically, the new experimental medium proposed in this paper may be used to detect a new range of products affecting social but not individual behaviour. Also, well-known products, which in normal doses produce no detectable modification of individual behaviour, might have detectable effects on the collective level. Be this as it may, the experimental environment described constitutes a further experimental facility for the analysis and control of animal behaviour.
1. Implicit Models in Animal Behaviour Experiments Experiments on habituation, classical conditioning, instrumental and operant conditioning, discrimination and decision learning (mazes) generally assume an experimental model in which the meanings of stimuli and animal responses are specified or implicitly accepted (Hildgard, 1966). These implicit models of the experimental procedures may be formally stated in terms of the dialectical interaction between two finite and usually probabilistic automata (AI, AZ), as shown in Fig. 1. One of these, Ar, represents the environment, whether free or controlled, and, if controlled, then Ar also includes the human or artificial controller, the experiment protocol in terms of the process being studied, the variables selected and the meaning associated to the animals’ behaviour. The other automaton, AZ, represents the animal or group of animals under investigation, their individual and collective actions, and the models we assume the animal possesses of their environment described in terms of the experiment being studied (classical or operant conditioning, reinforcement, etc.). Each experiment is characterized by the set of variables chosen, the protocol and 0 1986 Elsevier Scientific Publishers Ireland Ltd. 0020-7101/86/$03.50 Printed and Published in Ireland
202
J. Mira et al.
EXPERIMENTER
ANIMALS
animals model
environment model
environment
experimenter model
experiment protocol
individual actions
variables selected
collective actions
semantic tables
semantic tables
A2 =
Fig. 1. Two automata model.
the environment. From the experimenter view point a selection is made of some subset of the parameters representing the animals behaviour, Yz, and an initial table of significance is then established. This leads us to the following types of experimental designs: (a) Individual behaviour in controlled environments; (b) Social behaviour with distinguishable individuals; (c) Social behaviour with indistinguishable individuals. The behaviour of individuals has been analysed by means of controlled experimental environments (Skinner boxes, open fields, labyrinths, Mowrer-Miller boxes, etc.) permitting a small repertory of distinguishable stimuli and responses. The variables selected have been salivation or the pressing of a lever in order to deliver a pellet of food (Skinner, 1938; Hull, 1943). In all these cases the activity of a single animal is considered, or that of two animals in confrontation-fight experiments. The two automata model has been previously applied to this kind of experiment (Santesmases etal.,
1981).
Collective behaviour may be studied by observing animals in the wild or in ethological laboratories using either non-invasive techniques or intracerebral stimulation and monitoring (Rodriguez-Delgado et al., 1962; Delgado, 1964, 1965). The fundamental characteristic of this methodology is that the animals are distinguishable and may be manipulated individually. In other words, the units of social behaviour are constituted by specific relationships between elements of the group (‘a attacks b’, ‘b mounts c’, etc.). This approach basically seeks a detailed description of individual actions, though it is also capable of interpretation with the two-automata model (Monteagudo, 1984). The work reported in this paper is based on the idea that at a certain behavioural level it would be convenient to consider the animals of a group as indistinguishable in the sense that the relevant data refer to what is done and by how many animals but not to involved animals (Fraile et al., 1978; Mira et al., 1984; Cabello, 1984). The group is the only entity considered and stimulated and its responses the only behaviour analysed though the clustering degree and the process of cluster generation (aggregation) and cluster disaggregation.
Social behaviour
203
2. Clustering, Crowding and Huddling as Social Behaviour Variable-s We use the concept of clustering to define the internal states of the finite automaton AZ by means of which we represent the group dynamics under different experimental situations. Nevertheless, seemingly similar concepts although semantically different have been previously used to describe social behaviour using the labels of crowding and huddling. Stokols (1972) draws a distinction between the physical condition of ‘density’, defined purely in terms of spatial parameters and the ‘experiential state of crowding, in which the restrictive aspects of limited space are perceived by the individuals exposed to them’. Density is necessary but is not sufficient to establish a crowding condition, which also involves the interaction of social dimensions with spatial factors. The influence of the crowding motivational state on normal and pathological structures in groups of animals as well as their relations with other ethological concepts such as dominance-subordination, stress and territory, has been widely considered in the bibliography (Calhoun, 1962; Christian, 1970; Barnett, 1979; Butter, 1980). In this research path, the consideration of the animals as distinguishable is always present and the social behaviour is described in terms of specific relationship between the elements of the group. The concept of cluster that we propose in this paper is slightly different from crowding. Cluster behaviour is shown by the rats from pup to adult state both in male and female groups and this clustering is not induced by a high density situation and is controlled by the animals themselves; it can be considered as a free social variable (Herntidez, 1981; Mira et al., 1984; Cabello, 1984). Among the social interactions, Grant and Mackintosh (1963) distinguish a subgroup of ‘friendly interactions’ that ends with the grouping of two or more animals. This state is labeled as a huddling state. Whittow (1971) and Alberts (1978) studied this huddling behaviour as a co-operative process intended for temperature regulation according to the global group dynamics and the dynamics of the individuals inside the group. Our concept of clustering is similar to that huddling but emphasizes the fact that the animals are indistinguishable. For our purposes the temperature regulation is not relevant. Our experimental groups are not intended to emulate natural populations, they are homogeneous (same age, sex and physiological state) without sharing any previous history and their composition depends on the nature of the experiment. In considering the animals as indistinguishable, cluster behaviour is the only social activity that we take into account, social hierarchies being unknown. Cluster states are considered as internal micro-states of the finite automaton AZ whose state transition probabilistic matrices describe the group dynamics under the habituation, classical conditioning, instrumental different experimental situations: and operant conditioning and decision learning.
J. Mira et al.
204
3. System Hardware
The environment in which the experiments are carried out is a 60 X 60 X 60 cm box. This size was chosen on the basis of an open field experiment using litter-sized
INTERFACE
l---l=
I
SOCIAL
BEHAVIOUR BOX
effecters
Isound. light and electric st~mulal
Fig. 2a. Block diagram.
Fig. 2b. General view of system.
F
Social behaviour
20.5
groups of rats. This environment is controlled by means of a multimicroprocessor system organized as a parallel processor to improve velocity, Figure 2a shows the blocks diagram and Fig. 2b shows a general view of the system which is endowed with an interface board including data acquisition and control of the effecters, the social behaviour box, the multiprocessor and some conventional resources (Display, Printer, Tape, Keyboard). The design of the multimicroprocessor system, LTMuP (Zapata, 1983; Mira et al., 1982a; Mira et al., 1983) is based on the Motorola CPUs MC6800 and MC6802, which are 8-bit UPS with a 64K address space, four Interrupt lines (Reset, Halt, NM1 and IRQ) and a set of various clock and control signals ($01, @!, VMA, DBE, G/H, etc.). Many of the peripheral chips used in the design are also Motorola products (e.g. MC6821, MC6850 and MC6840), and all address decoding circuits have been implemented using TTL ICs. The LTMuP system is configurated as three functional units each one including: A extended CPU (ECPU), 8K of RAM, 8K of EPROM and an I/O unit. Globally, the system communicates with the experimenter by means of a monitor. ECPUs are connected to each other by independent interuP buses allowing mutual dialogue. Each ECPU is in itself a genuine complete microcomputer, with its own clock, processing unit, local RAM and EPROM, expansion circuits, PIAs for interprocessor communications and I$$, NMI and I&%? generators for interrupting other ECPUs. The shared resources are constituted by the units RAM, EPROM, I/O and interface to monitor. All these units are multigate and programmable in such a way that simultaneous access to two or more ECPUs is forbidden. Dialogue among processors can be fulfilled through shared memory areas. The interface board includes the multiplexer of the position sensor and stimuli generators. Experimental possibilities of the system are increased by its also including a number of classical sensors and effecters: a lever, light, sound, food troughs, drinking troughs and independent electrification of different areas of the box. The position sensor is a matrix of phototransistors homogeneously illuminated at the edge of the infrared, and it is the shadows cast by the animals which generate the succession of Boolean matrices fed into the multiprocessor system which calculates clustering and controls the experiments; thus the group temporal evolution is characterized by the evolution of the shadows. The design of the environment is completely modular, enabling food dispensers, pedals and barriers to be inserted or withdrawn at will. In this way the social box could be converted into a classical Skinner or Miller box. Moreover, the box floor is also modular and hardware and software facilities are included to enable a dynamic segmentation according to the ongoing experiment. Figure 3 shows different ways of segmentation. Each type of stimulus has been associated with a set of addresses in the memory map of the processor whose contents shape their physical characteristics: Intensity, Frequency, Timing and Area of action in the box. The system operates in a master-
206
J. Mira et al.
lb1 la1 Fig. 3. Floor segmentation.
slave configuration. The master sends the stimulus-program through the stimula bus using the two parts of the peripheral interface adapter MC-6820 programmed as outputs and the slave (the stimuli unit) executes that program. Figure 4 shows a view of the board used for acoustic stimulus. For each type of stimulus the slave includes five registers (SN74LS273) and the corresponding digital to analogue converters (DAC-0800) followed by function generators (XR-2206) and programmable timercounters (ICM-7240) followed by power amplifiers (STK-020) and so on.
Fig. 4. Acoustic stimulus board.
Social behaviour
201
4. Definition of the A, and A2 Automata The Ar automaton includes the computer program for the different experiments: learning, classical conditioning and so on. A significant feature is the automaton Aa which represents the group under study: AZ = (X2, Y?, SZ, Pz). The inputs set XZ coincides with the controller outputs Y 1 and the outputs set YZ represent the group response to any stimulus or history of stimuli. Table I shows the meaning of their elements. The information carried by ~4, excepted JJ:, is included in the shadow Boolean matrix. The system works with a quantified time scale, so that the elementary stimuli have a futed duration of At. When longer stimuli are required the elementary inputs are repeated. Thus a typical hour-long control sequence for a classical experiment to study the influence of environmental changes might be: 25’ habituation, 5’ intermittent stimulus, 30’ recuperation. The definition of the group states set, {St}, must take into account parameters indicating the nature of the group and clustering parameters. The type of group is defined by the number of animals, age, sex, previous history and physiological state (hunger, thirst, drugs ingestion, etc.). To each such set of parameters a group label Gi is assigned. For a given number of animals (included in Gi) the state of the group depends on their clustering pattern, i.e. on the number of subgroups (limited by the size of the group) and their position in the box. The number of possible states is reduced by defining equivalence classes. TABLE I MEANINGS OF INPUTS {xi, ) and outputs Inputs xf 4 x: $8 x:. x: x; xi, xi, x’,, xi”, x;* xy, x,13, xy, x :‘, xi6 y’7 18
1 .x2
outputs Y: Y: Y: Y: Y:
Y ‘,
vi }
Meanings --. Normal conditions Area A electrified Area B, C or D electrified The whole box electrified Light in area A, B, C, D or in the whole box Sound in area A, B, C, D or whole box Food in area A or B Meanings No change in clustering or spatial location That the spatial location of the subgroups changes of area there is no change in the state of clustering Aggregation: the number of subgroups decreases (in whatever the area it happens) Disaggregation: the number of subgroups rises That a pedal has been depressed
208
J. Mira et al.
The elements of S2 are thus configurations
of the form:
Sf = Wj; (P, 4, ~11 where Gi is the type of group, 4 the size of a subgroup, p its order among the subgroups and z the area occupied. For example, the states corresponding to the situation in Fig. 5 is represented by: s =
{(I,U),
(2,5J3), (3,I,C), (4JP)1
considering the experimental environment segmented in four areas. This situation describes a microstate. The automaton can be simplified by partitioning the sets of microstate into equivalence classes which are then considered as macro-states of the automaton. This is done by using an integrated index of clustering (IX) and two thresholds, 0 1 and d2. The value of IIC at any time is the sum of the animals gathered in the different subgroups, provided that the number of these animals surmounts a given 0 threshold. From IIC we define three macrostates: HIGH CLUSTERING (IIC > 0 r), MEDIUM (6 1> IIC > ed and LOW CLUSTERING (IIC < 0,). During each sampling interval of the experimental environment we detect the microstate, after that the corresponding macrostate is obtained and as the experiment proceeds the computer draws the pathway of the equivalent automaton. After
Fig. 5. Microstate S = {(1,2,A), (2,539, (3,1,C), (4,l,D)}.
Social behaviour
the segmentation of the experiment of each macro-state is:
in intervals
At, the probability
209
of occupation
where ni is the number of occasions in which the ith state has been occupied and n the total number of states in the interval Af. In a similar way, the elements of the probabilistic transition matrix, Pt (t, At) are: tcAt
P; (t, At) = c
-
nib3
t
where ni(j) is the number of transitions starting in Si and ending on Sj. The denominator is the total number of transitions starting in Si. It must be fulfilled that:
These values are obtained under constant input, X,,,. As these inputs are mutually exclusive we can represent the group dynamics by a functional matrix: Mii(X,)
=
P; *x,
Both these elements and the automaton A2 are time dependent according to the on going experiment (automaton A,) and the age, sex and physiological state of the animals. 5. Software Facilities As previously stated, the synthesis of the A, automaton is software and depends on the particular experiment being carried out. From the point of view of the controller, the type of experiments which can be programmed with this apparatus are basically the result of hanslating into collective terms the frame work of the classical experiments used in the study of individual behaviour. Two groups may be distinguished: (a) The analysis of spontaneous behaviour, comprising studies of the temporal evolution of the degree of clustering for groups of various types (i.e. for which G varies as regards sex, age, history of physiological condition), of circadian rhythms, of mobility, etc. Also included here are all those studies on the effect of environmental changes in which the group responses (Yz) does not affect the control program (A,). (b) Fixed
210
J. Mira et al.
goal experiments (as a generalization of individual behaviour experiments). As an example, the following sequence of actions might be effected in experiments on social instrumental conditioning: 1. Control of the level of motivation of all the animals in the group via their weight (80%, for example). 2. Placement of the animals in the cluster box so that they can explore it (daily sessions for various days). 3. Placement of the animals in the cluster box with food in the food dispenser to facilitate identification. 4. Supply a food pellet every time the lever is operated by any animal. In a social experiment generalizing the Mowrer-Miller box the sequence would be as follows: (1) Habituation (darkness and silence); (2) Placement of the animals in either of the two areas (A, let us suppose); (3) Presentation in A of the stimulus (light or sound) to which the animals are to be conditioned; (4) If no animal has jumped to B within t seconds of the stimulus being presented, it is maintained together with an electric stimulus; (5) In successive experiments the voltage applied is adjusted according to the number of animals jumping to B. The cluster box also allows the study of processes of learning transfer and of the effect of collective stimuli in which the reward or punishment applied to the group is made to depend on its state of clustering. To facilitate the control of these experiments the system includes, added to its basic management operative systems, the following additional software facilities: (1) Data acquisition and stimuli programming and control; (2) Interactive program that enables the fill up of the experimental scripts using the keyboard; (3) Calculus of the clustering degree from the shadow Boolean matrices; (4) Synthesis of the AZ probabilistic automaton transition matrices; (5) Display and graphic representation of the results. As an example, Fig. 6 shows the flowchart of the AI program corresponding to the evaluation of the clustering degree as well as the synthesis of the AZ automaton in a response to standard stimuli experiment. The timing corresponds to 30 min of spontaneous behaviour, then 5 min of stimulation and finally 25 min of recuperation. The digital processing of the information supplied by the experimental medium comes under the heading of cluster analysis, in the mathematical sense. The input to the microprocessor system is a succession of Boolean matrices obtained by periodic scanning of the phototransistors matrix. The points of these matrices are so classified as to define clusters so that elements belonging to a given cluster have a high degree of similarity while these belonging to different clusters have a high degree of dissimilarity. These clusters represent subgroups of animals in contact or in close proximity. A disaggregative and hierarchical clustering procedure has been developed using a measure of dissimilarity the Euclidean distance between points. A part of the procedure consists in a radial scanning algorithm generating the spatial coordinates of the neighbours around a given point in a spatially quantized space (Mira ef al., 1982b; CabelIo, 1984). The generation of the coordinates is accomplished by means of a ‘distance parameter’ K, avoiding direct calculation of Euclidean distances and reducing the computation time. In Fig. 7 we show a set of points and the classification law. We
Social behaviour
211
interval AT
I/O
G
delay
programming
i microstate calculus
control of the stlnulatlon-slave
microstate equivalence classes
I
1 calculus of P,ltl and P,] It1
I Fig. 6. Flowchart of the A, automaton in a standard stimulus response experiment.
the near neigbbours of a given point in levels (ZV)and sublevels (N,p). For each level, N, there are p = N + 1 sublevels (p = O,l, . . . ,A/) and each point belonging to a sublevel (N, p) has the same distance to the origin and this is accounted for writing the statement:
classify
WN,P)
=
Nd cos a (N, p) ’
01(N P) = fg-’ (P/N)
212
J. Mira et al.
0
0
0
p-1
0
0
0
0
P=o
I$
0
0
0 N-2
A
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Q_-_o-_-o___o___i, 0
0
0
0
0
Fig. 7. Spatial quantization.
where d is the minimum distance between points, (N, p) the indexers of sublevel and o@J p) the polar angle associated to (4 p). We then introduce a parameter, K(N, p), to arrange the sublevels in increasing order of Euclidean distance. Thus, K = 1 for D (l,O); K = 2 for D (1,l); . . . ; K = 10 for D (4,1), K = 11 for D (3,3) and son on. Then for any active point in the Boolean matrix, (x’, y’), a cluster is made up of this element and-all the nearest neighbours included in the area defied by the K parameters, in addition to the neighbours near to each new element of the cluster. The algorithm constructs the cluster from arbitrary elements of the matrix given by the position sensor and all its nearest neighbours which are both in the matrix and included in the area defined by the K parameter are detected by radial scanning. The process is then repeated for each nearest neighbour and the cluster obtaining process finishes when all elements which form it have been scanned. The remaining unclassified elements of the position matrix form the elements to consider in later divisions. The process finally finishes when all elements have been classified. Radial scanning around a given point is carried out by generating the spatial coordinates of all the points of each sublevel until K. In order to do this we use the coordinates of the given point together with a transformation matrix obtained
Social behaviour
213
from the indices of the sublevel (N, p) (Mira et al., 1982b). This matrix takes into account the symmetries existing in the Euclidean space. The number of clusters to be obtained is not an input parameter to the algorithm and the results are independent of the order of samples processing. Strong or weak clusters can be discerned by changes in the K parameter. Considering the animals’ size and that clustering means shadow continuity optimum results have been obtained forK = 2. Once the different clusters have been detected, the number of animals included in each one is calculated as a previous step to the identification of the microstate. First of all, we assign animals to the clusters of the lower order according to experimentally selected thresholds. For the last two clusters, the remaining animals are assigned according to previous thresholds or split up half and half according to a new threshold decision. This assignation strategy takes into account the ‘piling’ effect of the animals in numerous clusters. Due to this piling, the area occupied by various animals in a cluster is less than the sum of the areas which would be occupied by each one in individual clusters. Figure 8 shows the results corresponding to different real situations. With a ‘1’ we indicate the points that belong to the cluster firstly obtained; number ‘2’ represents the points classified within the second identified cluster and so on. The above configurations having been obtained further processing of their temporal sequence depends on the particular experiment under way. In any case, in a parallel manner the software facilities include the build up of the automaton, A*, that sums up and emulates the behaviour of the animals from the strings of microstates detected. For this, after quantifying the sampling intervals and once one segment of experiment span At has ended, the probabilities of the macrostates as well as the corresponding state-transitions probabilities are obtained using the definitions previously stated. In fact, the interval At operates as a ‘window’ that we shift over the time coordinate in a convolution-like frame. The kernel duration spans from At to T enabling us to study in a continuous way the temporal evolution of the probabilistic automaton that emulates the group behaviour. 6. Preliminary Experimental Results Let us illustrate some of the possibilities of the experimental medium and the cluster analysis previously described in this paper. Hernfindez (198 1) has carried out a preliminary evaluation of the cluster behaviour in comparative experiments with other media such as open fields and Skinner and Mowrer-Miller boxes. The clustering results obtained were in all cases compatible with those of the more conventional methods. In particular, it was found that the temporal evolution of clustering was associated with sexual differences, degree of maturity and emotivity. Included in practically any experimental protocol is the spontaneous behaviour data analysis and the response to stimulus data characterization. In Fig. 9 we show
214
J. Mira et al.
??
2
??
2
. ??
2
2
2
2
??
2
??
. ??
??
*
. ??
3
2
2
2
.Z ??
??
2 2
*
3
2
3. 5
3
2
??
3 3
.
3* 5
3
??
??
3.
..t**....tt*ttt*t*t***
*
.
??
??
6
??
7
??
B
?? ??
a
~ICROGT~TE8-~~1.I.~~.~Z,t.B~.~3.4.8~.~4.1.C1.~,.1.C,. (6. 1.D) >
*ICROSTPTE
6
84
??
6
7
B
* e ***t****.**....*t.*.**
.
7
??
6
77
II. l,l3>, (2.2.P,. ,C.I.D).,7.1.C,.(8,1,CI,
?? ??
(3. l,B,.
IL.
1.C).
a.
1
Fig. 8. Clusters that define the microstates.
the influence of sex and degree of maturity on the equivalent probabilistic automata in spontaneous behaviour for two experimental groups of ten norway rats. In Fig. 9a we show the data corresponding to 2-month-old females and in Fig. 9b corresponding to adult males (9-month-old) and shows the temporal evolution throughout 24 h of the state probabilities vector (Ph. Pm, PI) obtained by means of the formal expressions presented in the previous sections using a threshold 0 = 3 in the IIC calculus, and thresholds Br = 7 and t9r = 4 for the macrostates calculus. The window used in the definition of the probabilities was of 120 sampling intervals with an 11.25 s sampling period for the position sensor (AhT = 22 min of experiment). If we use a window of 1280 sampling intervals (4 h of experiment) the state transitions diagram offers us a more compact representation of the animals behavior. These state-transition diagrams are also included in Fig, 9 for the time interval elapsed
Social behaviour Ph
Ph 10
1.0
0.5
0.5
0.0
0.0
P, 1.0 +
215
T
km of day)
pm
1
lot
ItmeOf
dayI
0.0 20
21
0 i
I
T ItIme
of
20 T ltlme
of
day1
T Itlmeof day1
day1
\
HS
high State
MS
ledlum state
LS !Ph.P,.P,l
low state state PPObabllltles vector
Fig. 9. Equivalent probabilistic males.
automaton
in spontaneous
behaviour. (a) young females, (b) adult
between the hours 4 and 8. The tendency of animals to states of high degree clustering decreases with age, as does the activity measured as a frequency of transitions between different cluster states. Concerning learning experiments, the response to aversive stimula is represented on the cluster automaton as it is shown in Fig. 10 for the electric stimuli. These data correspond to a group of ten ‘norway rats’ aged 9 months. The timing sequence of the experiment is: spontaneous behavior (30 mm), aversive stimulus presentation (5 min) and decline after stimulation (25 min). The experiment was carried out between 1l:OO and 12:00 h under low brightness conditions. The physical parameters of the stimulus were 50 V, 50 Hz and a duration of 10 s alternating with repose intervals of another 10 s during the 5 min of stimulus presentation. In order to reach compact representation of the cluster automaton we considered a three-dimensional space each one of its axes representing the probabilities of the different cluster-macrostates. The path of the phasic point in this space makes clear the evolutive tendency of the animals, their stability and oscillatory behavior in circadian, ultradian and other rhythms.
216
J. Mira et al.
,b,
pro]ection on thelyPI
plane
pil
Fig. 10. Pathway in representational space corresponding to the animals behaviour for the electric stimulus response experiment. (b) and (c) are their PrOjeCtiOns on the Ph-P, and &PI planes.
Figure 10 shows the pathway in a representation of the space corresponding to the animals behavior as well as their projections on the &r-PI and &r--Pm planes for the previously mentioned experiment. The probabilistic automaton has been obtained using a window of 40 sampling intervals and thresholds 8 = 3, (I1 = 7 and 0s = 4. As it is evident in this representation, the stimulus presentation (event marker ‘s’m figure) strongly modifies the spontaneous behavior, increasing the tendency towards the high clustering degree states, in such a way that cluster behavior may be considered as a collective response to stress (Hernandez et al., 1980). When the aversive stimulus elapses, the cluster state decay to the more relaxed area of spontaneous behavior. A more ergonomic representation is obtained observing the projections of &r-PI and Ph-P, planes. The first is scarcely used and can be rejected. The main information for this experiment is on the &r-P, plane. These preliminary experiments show the discriminative power of the cluster automaton concerning sexual differences and emotivity, as well as the existence of a basic mechanism of clustering as collective response to stress. Using the same representation space Fig. 11 shows the influence of drugs on spontaneous behavior. The data correspond to two groups of 10 norway rats aged 3 month. Hexagonal points correspond to the control group and the triangular points to the group under drug treatment. This latter group were given Benzodiazopine as a depressor included in the meal at a dosage of 20 mg for each 100 g of diet. The experi-
Social behaviour
Fig. 11. Influence of drugs on spontaneous behaviour.
ment was carried out after three days of drugged diet ad libitum between the hours 1l:OO and 12:OO under low brightness conditions. This form of representation enables us to differentiate easily between normal and depressed animals. Their pathways on the representation space do not overlap. The control group always moves in the central space in contrast to depressed animals which move in a simiIar way to stressed animals moves with a high pH value. Pharmacologically, the experimental medium proposed in this paper may be used to detect a new range of products affecting social but not individual behavior. It is also possible that well-known products which produce no detectable modification of individual behavior in normal doses, might have detectable effects at the collective level. Be this as it may, the cluster box described, together with the associated electronics and the concept data representation together with analysis by means of the probabilistic cluster automata constitutes a further experimental facility for the analysis and control of animal behavior. References Alberts, J.R., 1978, Huddling by rats pups: group behavioral mechanisms of temperature regulation and energy conservation,J, Comp. Physiol. Psychol. 92 (2) 231-245. Barnett, %A., 1979, Cooperation, conflict, crowding and stress: an essay on method, Inferdisc@. Sci. Rev., 4 (2) 106-131.
218
J. Mira et al.
Butter, R.G., 1980, Population size, social behaviour and dispersal in house mice: a quantitative investigation, Anirn. Behav., 28,78-95. Cabello, D., 1984, Conceptos ciberneticos y sistemas multiprocesador para el anrilisis de la con. ducta social en ratas, Doctoral Disertation, University of Santiago de Compostela. Calhoun, J.B., 1962, Population density and social pathology,Sci. Am., 206, 139-148. Christian, J.J., 1970, Social subordination, population density and mammalian evolution, Science, 168,84-90.
Delgado, J.M.R., 1964, Free behavior and brain stimulation, ht. Rev. Neurobiol., 6, 349-449. Delgado, J.M.R., 1965, Sequential behavior induced repeatedly by stimulation of the red nucleus in free. monkeys, Science, 148,1361-1363. Fraile, A., De&do, A.E. and Mira, J., 1978, General ideas for the study of cooperative behaviour in rats, Proceedings of the I World Congress of Ethology applied to Zootechny, Madrid, 1978 pp. 567-568. Grant, E.G. and Mackintosh, J.H., 1963, A comparison of the social postures of some common laboratory rodents, Behaviour. 2 1,246-259. Hernandez, R., Fraile, A., Mira, J. and Delgado, A.E., 1980, Crowding as a collective animal response to stress, Poster pres. to section 1216 of XXVIII International Congress of Physiological Sciences, July 13-19, Budapest. Hernandez, R., 1981, Conducta general y testosteronemia en la rata: un modelo de comportamiento basado en esta relacibn, Doctoral Disertation, Universidad Complutense, Madrid. Hildgard E.H., 1966, Methods and procedures in the study of learning, in Handbook of Experimental Psychology, Stevens S.S. (Ed.), Wiley, New York, pp. 517-567. Hull, C.L., 1943, Principles of behavior, AppletonCentury-Crofts, New York. Mira, J., Zapata, E.L., Cabello, D. and Delgado, A.E., 1982a, On the lesion tolerance problem for cooperative process, in Implementing Functions: Microprocessors and Firmware, Ritcher, L., Le Beux, P., Chroust G. and Noguez G. (Eds.), North-Holland Publishing Company,Amsterdam, pp. 71-79. Mira, J., Cabello, D., Zapata, E.L. and Delgado, A.E., 1982b, A fast clustering procedure using radial scanning, Proc. of the 6th International Conference on Pattern Recognition, Munich, October, 1059-1062. Mira, J., Zapata, E.L., Cabello, D. and Delgado, A.E., 1983, LTMuP and LTOS: a distributed cooperative multiprocessor that tolerates physical damage, in The Relation #between Major World Problems and Systems Learning, Lasker, G.E. (Ed.), Vol. II, Intersystems, pp. 691-696. Mira, J., Fraile, A., Cabello, D, Delgado, A.E. and Zapata, E.L., 1984, Conceptos y sistemas electr6nicos para el estudio de la conducta social en ratas, in Biocibernktica. Implicaciones en BioZogfa, Medicina y Tecnologfa, Moreno-Diaz, R. and Mira J. (Eds.), Siglo XXI, Madrid, 195207. Monteagudo, J.L., 1984, Las miquinas probabilisticas variables en el tiempo coma modelo en eto-psicologia in Biociberndtica. Implicaciones en Biologfa, Medicina y Technologin. MorenoDiaz, R. and Mira, J. (Eds.), Siglo XXI, Madrid, 132-148. Rodriguez-Delgado, R. and Delgado, J.M.R., 1962, An objective approach to measurement of behavior, Philos. Sci., 29, 3. Santesmases, J.G., Giron Sierra, J.M. and Mira, J., 1981, Conceptual and technological evolution in the electronic control of learning, Kybernetes, 10,241-252. Skinner, B.F., 1938, The Behavior of Organisms, AppletonCentury-Crofts, New York. Stokols, D., 1972, On the distinction between density and crowding: same implications for future research, Psychol. Rev., 79 (3) 275-277. Whittow, G.C., 1971, Comparative Physiology of Thermoregulation, Vol. 1, Academic Press, New York. Zapata, E.L., 1983, LTMuP: un sistema multiprocesador cooperative que tolera la lesidn fisica, Doctoral Disertation, University of Santiago, de Compostela.
19 (1986)
201-218
201
Elsevier Scientific Publishers Ireland Ltd.
A COMPUTATIONAL FRAME
TO STUDY SOCIAL BEI-IAVIOUR
IN ANIMALS
J. MIRAa, D. CABELLOa, A. FRAILEb, E.L. ZAPATAa and A.E. DELGADOa aDepartamento bDepartamento Madrid (Spain)
de Electrhica, Facultad de Ffsica, Universidad de Santiago de Compostela and de Fisiologia Animal, Facultad de CC. BioUgicas, Universidad Complutense de
(Received November 4th, 1986) This paper presents new methods and procedures for studying collective behaviour in rats. The animals are assumed to be indistinguishable one from another and the behaviour of the group is represented analysed and interpreted in terms of the temporal evolution of a finite state probabilistic automaton. The automaton states are defined by measures on the clustering degree considered as a social response variable. The electronic system developed to carry out the cluster analysis and the automatic control of the social behaviour in the experimental environment includes a multimicroprocessor interacting with a ‘social box’ in which, together with classical sensors and effecters, a phototransistor based position sensor is included. Preliminary experiments show the discriminative power of the cluster automaton concerning sexual differences and emotivity, as well as the extensive of a basic mechanism of clustering as a collective response to stress. Pharmacologically, the new experimental medium proposed in this paper may be used to detect a new range of products affecting social but not individual behaviour. Also, well-known products, which in normal doses produce no detectable modification of individual behaviour, might have detectable effects on the collective level. Be this as it may, the experimental environment described constitutes a further experimental facility for the analysis and control of animal behaviour.
1. Implicit Models in Animal Behaviour Experiments Experiments on habituation, classical conditioning, instrumental and operant conditioning, discrimination and decision learning (mazes) generally assume an experimental model in which the meanings of stimuli and animal responses are specified or implicitly accepted (Hildgard, 1966). These implicit models of the experimental procedures may be formally stated in terms of the dialectical interaction between two finite and usually probabilistic automata (AI, AZ), as shown in Fig. 1. One of these, Ar, represents the environment, whether free or controlled, and, if controlled, then Ar also includes the human or artificial controller, the experiment protocol in terms of the process being studied, the variables selected and the meaning associated to the animals’ behaviour. The other automaton, AZ, represents the animal or group of animals under investigation, their individual and collective actions, and the models we assume the animal possesses of their environment described in terms of the experiment being studied (classical or operant conditioning, reinforcement, etc.). Each experiment is characterized by the set of variables chosen, the protocol and 0 1986 Elsevier Scientific Publishers Ireland Ltd. 0020-7101/86/$03.50 Printed and Published in Ireland
202
J. Mira et al.
EXPERIMENTER
ANIMALS
animals model
environment model
environment
experimenter model
experiment protocol
individual actions
variables selected
collective actions
semantic tables
semantic tables
A2 =
Fig. 1. Two automata model.
the environment. From the experimenter view point a selection is made of some subset of the parameters representing the animals behaviour, Yz, and an initial table of significance is then established. This leads us to the following types of experimental designs: (a) Individual behaviour in controlled environments; (b) Social behaviour with distinguishable individuals; (c) Social behaviour with indistinguishable individuals. The behaviour of individuals has been analysed by means of controlled experimental environments (Skinner boxes, open fields, labyrinths, Mowrer-Miller boxes, etc.) permitting a small repertory of distinguishable stimuli and responses. The variables selected have been salivation or the pressing of a lever in order to deliver a pellet of food (Skinner, 1938; Hull, 1943). In all these cases the activity of a single animal is considered, or that of two animals in confrontation-fight experiments. The two automata model has been previously applied to this kind of experiment (Santesmases etal.,
1981).
Collective behaviour may be studied by observing animals in the wild or in ethological laboratories using either non-invasive techniques or intracerebral stimulation and monitoring (Rodriguez-Delgado et al., 1962; Delgado, 1964, 1965). The fundamental characteristic of this methodology is that the animals are distinguishable and may be manipulated individually. In other words, the units of social behaviour are constituted by specific relationships between elements of the group (‘a attacks b’, ‘b mounts c’, etc.). This approach basically seeks a detailed description of individual actions, though it is also capable of interpretation with the two-automata model (Monteagudo, 1984). The work reported in this paper is based on the idea that at a certain behavioural level it would be convenient to consider the animals of a group as indistinguishable in the sense that the relevant data refer to what is done and by how many animals but not to involved animals (Fraile et al., 1978; Mira et al., 1984; Cabello, 1984). The group is the only entity considered and stimulated and its responses the only behaviour analysed though the clustering degree and the process of cluster generation (aggregation) and cluster disaggregation.
Social behaviour
203
2. Clustering, Crowding and Huddling as Social Behaviour Variable-s We use the concept of clustering to define the internal states of the finite automaton AZ by means of which we represent the group dynamics under different experimental situations. Nevertheless, seemingly similar concepts although semantically different have been previously used to describe social behaviour using the labels of crowding and huddling. Stokols (1972) draws a distinction between the physical condition of ‘density’, defined purely in terms of spatial parameters and the ‘experiential state of crowding, in which the restrictive aspects of limited space are perceived by the individuals exposed to them’. Density is necessary but is not sufficient to establish a crowding condition, which also involves the interaction of social dimensions with spatial factors. The influence of the crowding motivational state on normal and pathological structures in groups of animals as well as their relations with other ethological concepts such as dominance-subordination, stress and territory, has been widely considered in the bibliography (Calhoun, 1962; Christian, 1970; Barnett, 1979; Butter, 1980). In this research path, the consideration of the animals as distinguishable is always present and the social behaviour is described in terms of specific relationship between the elements of the group. The concept of cluster that we propose in this paper is slightly different from crowding. Cluster behaviour is shown by the rats from pup to adult state both in male and female groups and this clustering is not induced by a high density situation and is controlled by the animals themselves; it can be considered as a free social variable (Herntidez, 1981; Mira et al., 1984; Cabello, 1984). Among the social interactions, Grant and Mackintosh (1963) distinguish a subgroup of ‘friendly interactions’ that ends with the grouping of two or more animals. This state is labeled as a huddling state. Whittow (1971) and Alberts (1978) studied this huddling behaviour as a co-operative process intended for temperature regulation according to the global group dynamics and the dynamics of the individuals inside the group. Our concept of clustering is similar to that huddling but emphasizes the fact that the animals are indistinguishable. For our purposes the temperature regulation is not relevant. Our experimental groups are not intended to emulate natural populations, they are homogeneous (same age, sex and physiological state) without sharing any previous history and their composition depends on the nature of the experiment. In considering the animals as indistinguishable, cluster behaviour is the only social activity that we take into account, social hierarchies being unknown. Cluster states are considered as internal micro-states of the finite automaton AZ whose state transition probabilistic matrices describe the group dynamics under the habituation, classical conditioning, instrumental different experimental situations: and operant conditioning and decision learning.
J. Mira et al.
204
3. System Hardware
The environment in which the experiments are carried out is a 60 X 60 X 60 cm box. This size was chosen on the basis of an open field experiment using litter-sized
INTERFACE
l---l=
I
SOCIAL
BEHAVIOUR BOX
effecters
Isound. light and electric st~mulal
Fig. 2a. Block diagram.
Fig. 2b. General view of system.
F
Social behaviour
20.5
groups of rats. This environment is controlled by means of a multimicroprocessor system organized as a parallel processor to improve velocity, Figure 2a shows the blocks diagram and Fig. 2b shows a general view of the system which is endowed with an interface board including data acquisition and control of the effecters, the social behaviour box, the multiprocessor and some conventional resources (Display, Printer, Tape, Keyboard). The design of the multimicroprocessor system, LTMuP (Zapata, 1983; Mira et al., 1982a; Mira et al., 1983) is based on the Motorola CPUs MC6800 and MC6802, which are 8-bit UPS with a 64K address space, four Interrupt lines (Reset, Halt, NM1 and IRQ) and a set of various clock and control signals ($01, @!, VMA, DBE, G/H, etc.). Many of the peripheral chips used in the design are also Motorola products (e.g. MC6821, MC6850 and MC6840), and all address decoding circuits have been implemented using TTL ICs. The LTMuP system is configurated as three functional units each one including: A extended CPU (ECPU), 8K of RAM, 8K of EPROM and an I/O unit. Globally, the system communicates with the experimenter by means of a monitor. ECPUs are connected to each other by independent interuP buses allowing mutual dialogue. Each ECPU is in itself a genuine complete microcomputer, with its own clock, processing unit, local RAM and EPROM, expansion circuits, PIAs for interprocessor communications and I$$, NMI and I&%? generators for interrupting other ECPUs. The shared resources are constituted by the units RAM, EPROM, I/O and interface to monitor. All these units are multigate and programmable in such a way that simultaneous access to two or more ECPUs is forbidden. Dialogue among processors can be fulfilled through shared memory areas. The interface board includes the multiplexer of the position sensor and stimuli generators. Experimental possibilities of the system are increased by its also including a number of classical sensors and effecters: a lever, light, sound, food troughs, drinking troughs and independent electrification of different areas of the box. The position sensor is a matrix of phototransistors homogeneously illuminated at the edge of the infrared, and it is the shadows cast by the animals which generate the succession of Boolean matrices fed into the multiprocessor system which calculates clustering and controls the experiments; thus the group temporal evolution is characterized by the evolution of the shadows. The design of the environment is completely modular, enabling food dispensers, pedals and barriers to be inserted or withdrawn at will. In this way the social box could be converted into a classical Skinner or Miller box. Moreover, the box floor is also modular and hardware and software facilities are included to enable a dynamic segmentation according to the ongoing experiment. Figure 3 shows different ways of segmentation. Each type of stimulus has been associated with a set of addresses in the memory map of the processor whose contents shape their physical characteristics: Intensity, Frequency, Timing and Area of action in the box. The system operates in a master-
206
J. Mira et al.
lb1 la1 Fig. 3. Floor segmentation.
slave configuration. The master sends the stimulus-program through the stimula bus using the two parts of the peripheral interface adapter MC-6820 programmed as outputs and the slave (the stimuli unit) executes that program. Figure 4 shows a view of the board used for acoustic stimulus. For each type of stimulus the slave includes five registers (SN74LS273) and the corresponding digital to analogue converters (DAC-0800) followed by function generators (XR-2206) and programmable timercounters (ICM-7240) followed by power amplifiers (STK-020) and so on.
Fig. 4. Acoustic stimulus board.
Social behaviour
201
4. Definition of the A, and A2 Automata The Ar automaton includes the computer program for the different experiments: learning, classical conditioning and so on. A significant feature is the automaton Aa which represents the group under study: AZ = (X2, Y?, SZ, Pz). The inputs set XZ coincides with the controller outputs Y 1 and the outputs set YZ represent the group response to any stimulus or history of stimuli. Table I shows the meaning of their elements. The information carried by ~4, excepted JJ:, is included in the shadow Boolean matrix. The system works with a quantified time scale, so that the elementary stimuli have a futed duration of At. When longer stimuli are required the elementary inputs are repeated. Thus a typical hour-long control sequence for a classical experiment to study the influence of environmental changes might be: 25’ habituation, 5’ intermittent stimulus, 30’ recuperation. The definition of the group states set, {St}, must take into account parameters indicating the nature of the group and clustering parameters. The type of group is defined by the number of animals, age, sex, previous history and physiological state (hunger, thirst, drugs ingestion, etc.). To each such set of parameters a group label Gi is assigned. For a given number of animals (included in Gi) the state of the group depends on their clustering pattern, i.e. on the number of subgroups (limited by the size of the group) and their position in the box. The number of possible states is reduced by defining equivalence classes. TABLE I MEANINGS OF INPUTS {xi, ) and outputs Inputs xf 4 x: $8 x:. x: x; xi, xi, x’,, xi”, x;* xy, x,13, xy, x :‘, xi6 y’7 18
1 .x2
outputs Y: Y: Y: Y: Y:
Y ‘,
vi }
Meanings --. Normal conditions Area A electrified Area B, C or D electrified The whole box electrified Light in area A, B, C, D or in the whole box Sound in area A, B, C, D or whole box Food in area A or B Meanings No change in clustering or spatial location That the spatial location of the subgroups changes of area there is no change in the state of clustering Aggregation: the number of subgroups decreases (in whatever the area it happens) Disaggregation: the number of subgroups rises That a pedal has been depressed
208
J. Mira et al.
The elements of S2 are thus configurations
of the form:
Sf = Wj; (P, 4, ~11 where Gi is the type of group, 4 the size of a subgroup, p its order among the subgroups and z the area occupied. For example, the states corresponding to the situation in Fig. 5 is represented by: s =
{(I,U),
(2,5J3), (3,I,C), (4JP)1
considering the experimental environment segmented in four areas. This situation describes a microstate. The automaton can be simplified by partitioning the sets of microstate into equivalence classes which are then considered as macro-states of the automaton. This is done by using an integrated index of clustering (IX) and two thresholds, 0 1 and d2. The value of IIC at any time is the sum of the animals gathered in the different subgroups, provided that the number of these animals surmounts a given 0 threshold. From IIC we define three macrostates: HIGH CLUSTERING (IIC > 0 r), MEDIUM (6 1> IIC > ed and LOW CLUSTERING (IIC < 0,). During each sampling interval of the experimental environment we detect the microstate, after that the corresponding macrostate is obtained and as the experiment proceeds the computer draws the pathway of the equivalent automaton. After
Fig. 5. Microstate S = {(1,2,A), (2,539, (3,1,C), (4,l,D)}.
Social behaviour
the segmentation of the experiment of each macro-state is:
in intervals
At, the probability
209
of occupation
where ni is the number of occasions in which the ith state has been occupied and n the total number of states in the interval Af. In a similar way, the elements of the probabilistic transition matrix, Pt (t, At) are: tcAt
P; (t, At) = c
-
nib3
t
where ni(j) is the number of transitions starting in Si and ending on Sj. The denominator is the total number of transitions starting in Si. It must be fulfilled that:
These values are obtained under constant input, X,,,. As these inputs are mutually exclusive we can represent the group dynamics by a functional matrix: Mii(X,)
=
P; *x,
Both these elements and the automaton A2 are time dependent according to the on going experiment (automaton A,) and the age, sex and physiological state of the animals. 5. Software Facilities As previously stated, the synthesis of the A, automaton is software and depends on the particular experiment being carried out. From the point of view of the controller, the type of experiments which can be programmed with this apparatus are basically the result of hanslating into collective terms the frame work of the classical experiments used in the study of individual behaviour. Two groups may be distinguished: (a) The analysis of spontaneous behaviour, comprising studies of the temporal evolution of the degree of clustering for groups of various types (i.e. for which G varies as regards sex, age, history of physiological condition), of circadian rhythms, of mobility, etc. Also included here are all those studies on the effect of environmental changes in which the group responses (Yz) does not affect the control program (A,). (b) Fixed
210
J. Mira et al.
goal experiments (as a generalization of individual behaviour experiments). As an example, the following sequence of actions might be effected in experiments on social instrumental conditioning: 1. Control of the level of motivation of all the animals in the group via their weight (80%, for example). 2. Placement of the animals in the cluster box so that they can explore it (daily sessions for various days). 3. Placement of the animals in the cluster box with food in the food dispenser to facilitate identification. 4. Supply a food pellet every time the lever is operated by any animal. In a social experiment generalizing the Mowrer-Miller box the sequence would be as follows: (1) Habituation (darkness and silence); (2) Placement of the animals in either of the two areas (A, let us suppose); (3) Presentation in A of the stimulus (light or sound) to which the animals are to be conditioned; (4) If no animal has jumped to B within t seconds of the stimulus being presented, it is maintained together with an electric stimulus; (5) In successive experiments the voltage applied is adjusted according to the number of animals jumping to B. The cluster box also allows the study of processes of learning transfer and of the effect of collective stimuli in which the reward or punishment applied to the group is made to depend on its state of clustering. To facilitate the control of these experiments the system includes, added to its basic management operative systems, the following additional software facilities: (1) Data acquisition and stimuli programming and control; (2) Interactive program that enables the fill up of the experimental scripts using the keyboard; (3) Calculus of the clustering degree from the shadow Boolean matrices; (4) Synthesis of the AZ probabilistic automaton transition matrices; (5) Display and graphic representation of the results. As an example, Fig. 6 shows the flowchart of the AI program corresponding to the evaluation of the clustering degree as well as the synthesis of the AZ automaton in a response to standard stimuli experiment. The timing corresponds to 30 min of spontaneous behaviour, then 5 min of stimulation and finally 25 min of recuperation. The digital processing of the information supplied by the experimental medium comes under the heading of cluster analysis, in the mathematical sense. The input to the microprocessor system is a succession of Boolean matrices obtained by periodic scanning of the phototransistors matrix. The points of these matrices are so classified as to define clusters so that elements belonging to a given cluster have a high degree of similarity while these belonging to different clusters have a high degree of dissimilarity. These clusters represent subgroups of animals in contact or in close proximity. A disaggregative and hierarchical clustering procedure has been developed using a measure of dissimilarity the Euclidean distance between points. A part of the procedure consists in a radial scanning algorithm generating the spatial coordinates of the neighbours around a given point in a spatially quantized space (Mira ef al., 1982b; CabelIo, 1984). The generation of the coordinates is accomplished by means of a ‘distance parameter’ K, avoiding direct calculation of Euclidean distances and reducing the computation time. In Fig. 7 we show a set of points and the classification law. We
Social behaviour
211
interval AT
I/O
G
delay
programming
i microstate calculus
control of the stlnulatlon-slave
microstate equivalence classes
I
1 calculus of P,ltl and P,] It1
I Fig. 6. Flowchart of the A, automaton in a standard stimulus response experiment.
the near neigbbours of a given point in levels (ZV)and sublevels (N,p). For each level, N, there are p = N + 1 sublevels (p = O,l, . . . ,A/) and each point belonging to a sublevel (N, p) has the same distance to the origin and this is accounted for writing the statement:
classify
WN,P)
=
Nd cos a (N, p) ’
01(N P) = fg-’ (P/N)
212
J. Mira et al.
0
0
0
p-1
0
0
0
0
P=o
I$
0
0
0 N-2
A
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Q_-_o-_-o___o___i, 0
0
0
0
0
Fig. 7. Spatial quantization.
where d is the minimum distance between points, (N, p) the indexers of sublevel and o@J p) the polar angle associated to (4 p). We then introduce a parameter, K(N, p), to arrange the sublevels in increasing order of Euclidean distance. Thus, K = 1 for D (l,O); K = 2 for D (1,l); . . . ; K = 10 for D (4,1), K = 11 for D (3,3) and son on. Then for any active point in the Boolean matrix, (x’, y’), a cluster is made up of this element and-all the nearest neighbours included in the area defied by the K parameters, in addition to the neighbours near to each new element of the cluster. The algorithm constructs the cluster from arbitrary elements of the matrix given by the position sensor and all its nearest neighbours which are both in the matrix and included in the area defined by the K parameter are detected by radial scanning. The process is then repeated for each nearest neighbour and the cluster obtaining process finishes when all elements which form it have been scanned. The remaining unclassified elements of the position matrix form the elements to consider in later divisions. The process finally finishes when all elements have been classified. Radial scanning around a given point is carried out by generating the spatial coordinates of all the points of each sublevel until K. In order to do this we use the coordinates of the given point together with a transformation matrix obtained
Social behaviour
213
from the indices of the sublevel (N, p) (Mira et al., 1982b). This matrix takes into account the symmetries existing in the Euclidean space. The number of clusters to be obtained is not an input parameter to the algorithm and the results are independent of the order of samples processing. Strong or weak clusters can be discerned by changes in the K parameter. Considering the animals’ size and that clustering means shadow continuity optimum results have been obtained forK = 2. Once the different clusters have been detected, the number of animals included in each one is calculated as a previous step to the identification of the microstate. First of all, we assign animals to the clusters of the lower order according to experimentally selected thresholds. For the last two clusters, the remaining animals are assigned according to previous thresholds or split up half and half according to a new threshold decision. This assignation strategy takes into account the ‘piling’ effect of the animals in numerous clusters. Due to this piling, the area occupied by various animals in a cluster is less than the sum of the areas which would be occupied by each one in individual clusters. Figure 8 shows the results corresponding to different real situations. With a ‘1’ we indicate the points that belong to the cluster firstly obtained; number ‘2’ represents the points classified within the second identified cluster and so on. The above configurations having been obtained further processing of their temporal sequence depends on the particular experiment under way. In any case, in a parallel manner the software facilities include the build up of the automaton, A*, that sums up and emulates the behaviour of the animals from the strings of microstates detected. For this, after quantifying the sampling intervals and once one segment of experiment span At has ended, the probabilities of the macrostates as well as the corresponding state-transitions probabilities are obtained using the definitions previously stated. In fact, the interval At operates as a ‘window’ that we shift over the time coordinate in a convolution-like frame. The kernel duration spans from At to T enabling us to study in a continuous way the temporal evolution of the probabilistic automaton that emulates the group behaviour. 6. Preliminary Experimental Results Let us illustrate some of the possibilities of the experimental medium and the cluster analysis previously described in this paper. Hernfindez (198 1) has carried out a preliminary evaluation of the cluster behaviour in comparative experiments with other media such as open fields and Skinner and Mowrer-Miller boxes. The clustering results obtained were in all cases compatible with those of the more conventional methods. In particular, it was found that the temporal evolution of clustering was associated with sexual differences, degree of maturity and emotivity. Included in practically any experimental protocol is the spontaneous behaviour data analysis and the response to stimulus data characterization. In Fig. 9 we show
214
J. Mira et al.
??
2
??
2
. ??
2
2
2
2
??
2
??
. ??
??
*
. ??
3
2
2
2
.Z ??
??
2 2
*
3
2
3. 5
3
2
??
3 3
.
3* 5
3
??
??
3.
..t**....tt*ttt*t*t***
*
.
??
??
6
??
7
??
B
?? ??
a
~ICROGT~TE8-~~1.I.~~.~Z,t.B~.~3.4.8~.~4.1.C1.~,.1.C,. (6. 1.D) >
*ICROSTPTE
6
84
??
6
7
B
* e ***t****.**....*t.*.**
.
7
??
6
77
II. l,l3>, (2.2.P,. ,C.I.D).,7.1.C,.(8,1,CI,
?? ??
(3. l,B,.
IL.
1.C).
a.
1
Fig. 8. Clusters that define the microstates.
the influence of sex and degree of maturity on the equivalent probabilistic automata in spontaneous behaviour for two experimental groups of ten norway rats. In Fig. 9a we show the data corresponding to 2-month-old females and in Fig. 9b corresponding to adult males (9-month-old) and shows the temporal evolution throughout 24 h of the state probabilities vector (Ph. Pm, PI) obtained by means of the formal expressions presented in the previous sections using a threshold 0 = 3 in the IIC calculus, and thresholds Br = 7 and t9r = 4 for the macrostates calculus. The window used in the definition of the probabilities was of 120 sampling intervals with an 11.25 s sampling period for the position sensor (AhT = 22 min of experiment). If we use a window of 1280 sampling intervals (4 h of experiment) the state transitions diagram offers us a more compact representation of the animals behavior. These state-transition diagrams are also included in Fig, 9 for the time interval elapsed
Social behaviour Ph
Ph 10
1.0
0.5
0.5
0.0
0.0
P, 1.0 +
215
T
km of day)
pm
1
lot
ItmeOf
dayI
0.0 20
21
0 i
I
T ItIme
of
20 T ltlme
of
day1
T Itlmeof day1
day1
\
HS
high State
MS
ledlum state
LS !Ph.P,.P,l
low state state PPObabllltles vector
Fig. 9. Equivalent probabilistic males.
automaton
in spontaneous
behaviour. (a) young females, (b) adult
between the hours 4 and 8. The tendency of animals to states of high degree clustering decreases with age, as does the activity measured as a frequency of transitions between different cluster states. Concerning learning experiments, the response to aversive stimula is represented on the cluster automaton as it is shown in Fig. 10 for the electric stimuli. These data correspond to a group of ten ‘norway rats’ aged 9 months. The timing sequence of the experiment is: spontaneous behavior (30 mm), aversive stimulus presentation (5 min) and decline after stimulation (25 min). The experiment was carried out between 1l:OO and 12:00 h under low brightness conditions. The physical parameters of the stimulus were 50 V, 50 Hz and a duration of 10 s alternating with repose intervals of another 10 s during the 5 min of stimulus presentation. In order to reach compact representation of the cluster automaton we considered a three-dimensional space each one of its axes representing the probabilities of the different cluster-macrostates. The path of the phasic point in this space makes clear the evolutive tendency of the animals, their stability and oscillatory behavior in circadian, ultradian and other rhythms.
216
J. Mira et al.
,b,
pro]ection on thelyPI
plane
pil
Fig. 10. Pathway in representational space corresponding to the animals behaviour for the electric stimulus response experiment. (b) and (c) are their PrOjeCtiOns on the Ph-P, and &PI planes.
Figure 10 shows the pathway in a representation of the space corresponding to the animals behavior as well as their projections on the &r-PI and &r--Pm planes for the previously mentioned experiment. The probabilistic automaton has been obtained using a window of 40 sampling intervals and thresholds 8 = 3, (I1 = 7 and 0s = 4. As it is evident in this representation, the stimulus presentation (event marker ‘s’m figure) strongly modifies the spontaneous behavior, increasing the tendency towards the high clustering degree states, in such a way that cluster behavior may be considered as a collective response to stress (Hernandez et al., 1980). When the aversive stimulus elapses, the cluster state decay to the more relaxed area of spontaneous behavior. A more ergonomic representation is obtained observing the projections of &r-PI and Ph-P, planes. The first is scarcely used and can be rejected. The main information for this experiment is on the &r-P, plane. These preliminary experiments show the discriminative power of the cluster automaton concerning sexual differences and emotivity, as well as the existence of a basic mechanism of clustering as collective response to stress. Using the same representation space Fig. 11 shows the influence of drugs on spontaneous behavior. The data correspond to two groups of 10 norway rats aged 3 month. Hexagonal points correspond to the control group and the triangular points to the group under drug treatment. This latter group were given Benzodiazopine as a depressor included in the meal at a dosage of 20 mg for each 100 g of diet. The experi-
Social behaviour
Fig. 11. Influence of drugs on spontaneous behaviour.
ment was carried out after three days of drugged diet ad libitum between the hours 1l:OO and 12:OO under low brightness conditions. This form of representation enables us to differentiate easily between normal and depressed animals. Their pathways on the representation space do not overlap. The control group always moves in the central space in contrast to depressed animals which move in a simiIar way to stressed animals moves with a high pH value. Pharmacologically, the experimental medium proposed in this paper may be used to detect a new range of products affecting social but not individual behavior. It is also possible that well-known products which produce no detectable modification of individual behavior in normal doses, might have detectable effects at the collective level. Be this as it may, the cluster box described, together with the associated electronics and the concept data representation together with analysis by means of the probabilistic cluster automata constitutes a further experimental facility for the analysis and control of animal behavior. References Alberts, J.R., 1978, Huddling by rats pups: group behavioral mechanisms of temperature regulation and energy conservation,J, Comp. Physiol. Psychol. 92 (2) 231-245. Barnett, %A., 1979, Cooperation, conflict, crowding and stress: an essay on method, Inferdisc@. Sci. Rev., 4 (2) 106-131.
218
J. Mira et al.
Butter, R.G., 1980, Population size, social behaviour and dispersal in house mice: a quantitative investigation, Anirn. Behav., 28,78-95. Cabello, D., 1984, Conceptos ciberneticos y sistemas multiprocesador para el anrilisis de la con. ducta social en ratas, Doctoral Disertation, University of Santiago de Compostela. Calhoun, J.B., 1962, Population density and social pathology,Sci. Am., 206, 139-148. Christian, J.J., 1970, Social subordination, population density and mammalian evolution, Science, 168,84-90.
Delgado, J.M.R., 1964, Free behavior and brain stimulation, ht. Rev. Neurobiol., 6, 349-449. Delgado, J.M.R., 1965, Sequential behavior induced repeatedly by stimulation of the red nucleus in free. monkeys, Science, 148,1361-1363. Fraile, A., De&do, A.E. and Mira, J., 1978, General ideas for the study of cooperative behaviour in rats, Proceedings of the I World Congress of Ethology applied to Zootechny, Madrid, 1978 pp. 567-568. Grant, E.G. and Mackintosh, J.H., 1963, A comparison of the social postures of some common laboratory rodents, Behaviour. 2 1,246-259. Hernandez, R., Fraile, A., Mira, J. and Delgado, A.E., 1980, Crowding as a collective animal response to stress, Poster pres. to section 1216 of XXVIII International Congress of Physiological Sciences, July 13-19, Budapest. Hernandez, R., 1981, Conducta general y testosteronemia en la rata: un modelo de comportamiento basado en esta relacibn, Doctoral Disertation, Universidad Complutense, Madrid. Hildgard E.H., 1966, Methods and procedures in the study of learning, in Handbook of Experimental Psychology, Stevens S.S. (Ed.), Wiley, New York, pp. 517-567. Hull, C.L., 1943, Principles of behavior, AppletonCentury-Crofts, New York. Mira, J., Zapata, E.L., Cabello, D. and Delgado, A.E., 1982a, On the lesion tolerance problem for cooperative process, in Implementing Functions: Microprocessors and Firmware, Ritcher, L., Le Beux, P., Chroust G. and Noguez G. (Eds.), North-Holland Publishing Company,Amsterdam, pp. 71-79. Mira, J., Cabello, D., Zapata, E.L. and Delgado, A.E., 1982b, A fast clustering procedure using radial scanning, Proc. of the 6th International Conference on Pattern Recognition, Munich, October, 1059-1062. Mira, J., Zapata, E.L., Cabello, D. and Delgado, A.E., 1983, LTMuP and LTOS: a distributed cooperative multiprocessor that tolerates physical damage, in The Relation #between Major World Problems and Systems Learning, Lasker, G.E. (Ed.), Vol. II, Intersystems, pp. 691-696. Mira, J., Fraile, A., Cabello, D, Delgado, A.E. and Zapata, E.L., 1984, Conceptos y sistemas electr6nicos para el estudio de la conducta social en ratas, in Biocibernktica. Implicaciones en BioZogfa, Medicina y Tecnologfa, Moreno-Diaz, R. and Mira J. (Eds.), Siglo XXI, Madrid, 195207. Monteagudo, J.L., 1984, Las miquinas probabilisticas variables en el tiempo coma modelo en eto-psicologia in Biociberndtica. Implicaciones en Biologfa, Medicina y Technologin. MorenoDiaz, R. and Mira, J. (Eds.), Siglo XXI, Madrid, 132-148. Rodriguez-Delgado, R. and Delgado, J.M.R., 1962, An objective approach to measurement of behavior, Philos. Sci., 29, 3. Santesmases, J.G., Giron Sierra, J.M. and Mira, J., 1981, Conceptual and technological evolution in the electronic control of learning, Kybernetes, 10,241-252. Skinner, B.F., 1938, The Behavior of Organisms, AppletonCentury-Crofts, New York. Stokols, D., 1972, On the distinction between density and crowding: same implications for future research, Psychol. Rev., 79 (3) 275-277. Whittow, G.C., 1971, Comparative Physiology of Thermoregulation, Vol. 1, Academic Press, New York. Zapata, E.L., 1983, LTMuP: un sistema multiprocesador cooperative que tolera la lesidn fisica, Doctoral Disertation, University of Santiago, de Compostela.