Neuronal Group Selection Theory: A Grounding in Robotics
Authors: Tim Smithers Jim Donnett
Published in: Advances in Neural Information Processing Systems 2  (NIPS 1989) (Main Conference Track), 1989

Abstract: In this paper, we  discuss a current attempt at applying the organi(cid:173) zational principle  Edelman  calls  Neuronal  Group  Selection  to  the  control of a real,  two-link robotic  manipulator.  We begin by moti(cid:173) vating the need for  an alternative to the position-control paradigm  of classical robotics,  and suggest  that a  possible avenue  is  to look  at the primitive animal limb 'neurologically ballistic' control mode.  We  have  been  considering  a  selectionist  approach to coordinating  a  simple perception-action task. 
1  MOTIVATION  The majority of industrial robots in the world are mechanical manipUlators - often  arm-like  devices  consisting of some  number  of rigid  links  with  actuators  mounted  where  the  links  join  that  move  adjacent  links  relative  to  each  other,  rotationally  or  translation ally.  At  the  joints  there  are  typically  also  sensors  measuring  the  relative  position of adjacent links,  and it is  in  terms of position that manipulators  are generally controlled (a desired motion is specified as a desired position of the end  effector, from  which can be derived  the necessary  positions of the links comprising  the  manipulator).  Position control dominates largely for historical reasons,  rooted  in  bang-bang  control:  manipulators bumped between mechanical stops placed so as  to enforce a  desired trajectory for  the end effector. 
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1.1  SERVOMECHANISMS 
Mechanical stops  have  been  superceded  by  position-controlling  servomechanisms,  negative feedback systems in which, for a typical manipulator with revolute joints, a  desired joint angle is compared with a feedback signal from the joint sensor signalling  actual measured angle;  the difference controls the motive power output of the joint  actuator proportionally. 
Where  a  manipulator  is  constructed  of  a  number  of  links,  there  might  be  a  ser(cid:173) vomechanism for  each joint.  In  combination,  it  is  well  known  that  joint  motions  can  affect  each other adversely, requiring careful design  and analysis to reduce  the  possibility of unpleasant dynamical instabilities.  This is  especially important when  the  manipulator will  be required  to execute fast  movements involving many or  all  of the joints.  We  are  interested in  such dynamic tasks,  and  acknowledge some suc(cid:173) cessful servomechanistic solutions  (see  [Andersson  19881, who describes a ping pong  playing robot),  but seek an  alternative that is  not as  computationally expensive. 
1.2  ESCAPING POSITION CONTROL 
In  Nature,  fast  reaching and  striking is  a  primitive and fundamental mode of con(cid:173) trol.  In  fast,  time-optimal,  neurologically  ballistic  movements  (such  as  horizontal  rotations  of the  head  where  subjects  are  instructed  to  turn  it  as  fast  as  possible,  [Hannaford  and Stark  1985]),  muscle activity patterns seem  to  show  three phases:  a launching phase  (a burst of agonist), a  braking phase (an antagonist burst), and a  locking phase  (a second agonist  burst).  Experiments have shown  (see  [Wadman  et  al.  1979])  that at least the first  100 mS of activity is  the same even if a movement is  blocked mechanically  (without forewarning the subject), suggesting that the launch  is specified from  predetermined initial conditions  (and is not  immediately modified  from  proprioceptive information).  With  the  braking  and  locking  phases  acting  as  a  damping  device  at  the  end  of  the  motion,  the  complete  motion  of  the  arm  is  essentially specified by the initial conditions - ditional robot positional control.  The overall coordination of movements might even  seem naive and simple when compared with the intricacies of servomechanisms (see  [Braitenberg 1989, N ahvi and Hashemi 19841 who discuss the crane driver's strategy  for  shifting loads quickly  and time-optimally). 
a mode radically differing from tra(cid:173)
The concept of letting insights  (such  as  these)  that can  be gained from  the biolog(cid:173) ical sciences  shape  the  engineering  principles  used  to  create  artificial  autonomous  systems is finding favour with a  growing number of researchers in  robotics.  As  it is  not generally trivial to see how life's devices can be mapped onto machines, there is  a  need for  some fundamental experimental work to develop  and test the basic  the(cid:173) oretical and empirical components of this  approach, and we  have been  considering  various robotics problems from  this perspective.  Here, we discuss  an experimental two-link manipulator that performs a simple ma(cid:173) nipulation  task - hitting  a  simple  object  perceived  to  be  within  its  reach.  The  perception  of the object  specifies  the  initial conditions that determine an  arm  mo-
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tion that reaches it.  In relating initial conditions with motor currents, we  have been  considering  a  scheme  based  on  Neuronal  Group Selection  Theory  [Edelman  1987,  Reeke  and  Edelman  1988],  a  theory  of brain  organization.  We  believe  this  to  be  the  first  attempt  to  apply  selectionist  ideas  in  a  real  machine,  rather than just  in  simulation. 
2  NEURONAL GROUP SELECTION THEORY  Edelman proposes Neuronal Group Selection  (NGS)  [Edelman  1978]  as  an organiz(cid:173) ing principle for higher brain function - mainly a biological basis for perception - primarily  applicable  to  the  mammalian  (and  specifically,  human)  nervous  system  [Edelman  1981].  The essential idea is  that groups  of cells,  structurally varied  as  a  result  of developmental processes,  comprise  a  population  from  which  are  selected  those  groups  whose  function  leads  to  adaptive  behaviour  of  the  system.  Similar  notions  appear  in  immunology  and,  of  course,  evolutionary  theory,  although  the  effects of neuronal group selection  are manifest  in  the lifetime of the organism. 
There  are  two  premises  on  which  the  principle  rests.  The first  is  that  the  unit  of  selection  is  a  cell  group of perhaps 50  to  10,000 neurons.  Intra-group connections  between cells  are  assumed to vary (greatly)  between groups,  but other connections  in the brain (particularly inter-group) are quite specific.  The second premise is  that  the kinds of nervous systems whose organization the principle addresses are able to  adapt  to circumstances  not  previously  encountered by  the  organism  or  its  species  [Edelman  1978]. 
2.1  THREE CENTRAL TENETS 
There are  three important ideas  in  the NGS  theory  [Edelman  1987]. 
•  A  first  selective  process  (cell  division,  migration,  differentiation,  or  death)  results  in  structural  diversity  providing  a  primary  repertoire  of  variant  cell  groups. 
•  A second selective process occurs as the organism experiences its environment;  group  activity  that  correlates  with  adaptive  behaviour  leads  to  differential  amplification  of intra- and  inter-group  synaptic  strengths  (the  connectivity  pattern remains  unchanged).  From the primary  repertoire  are  thus selected  groups whose  adaptive functioning  means  they are more  likely to find  future  use  -
these groups form  the  ,econdary repertoire. 
•  Secondary repertoires themselves form  populations,  and the NGS  theory ad(cid:173)
ditionally  requires  a  notion  of  reentry,  or  connections  between  repertoires,  usually  arranged  in  maps,  of which  the  well-known  retinotopic  mapping  of  the visual system  is  typical.  These  connections  are critical for  they correlate  motor and sensory repertoires,  and lend the world the kind of spatiotemporal  continuity we  all experience. 
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2.2  REQUffiEMENTS  OF  SELECTIVE SYSTEMS 
To be selective, a system must satisfy three requirements IReeke and Edelman 1988].  Given a configuration of input signals  (ultimately from the sensory epithelia, but for  'deeper' repertoires mainly coming from other neuronal groups), if a group responds  in a specific  way it has  matched the input  IEdelman 1978].  The first  requirement of  a selective system is that it have a sufficiently large repertoire of variant elements to  ensure  that an  adequate match can  be found for  a wide range of inputs.  Secondly,  enough of the groups in  a  repertoire must 'see' the diverse input signals effectively  and quickly so that selection can operate on  these  groups.  And finally,  there must  be  a  means  for  'amplifying'  the  contribution,  to  the  repertoire,  of groups  whose  operation when  matching input signals has led  to adaptive behaviour. 
In  determining  the  necessary  number  of groups in  a  repertoire,  one  must consider  the  relationship  between repertoire  size  and  the specificity  of member groups.  On  the  one  hand,  if groups  are  very specific,  repertoires  will  need  to  be  very large  in  order to recognize a  wide range of possible inputs.  On the other hand, if groups are  not  as  discriminating,  it  will  be possible  to have smaller numbers  of them,  but  in  the limit (a single group with virtually no specificity) different signals will no longer  be  distinguishable.  A  simple  way  to  quantify  this  might  be  to  assume  that  each  of N  groups has  a  fixed  probability,  P,  of matching an input configuration;  then  a  typical measure  IEdelman  1978]  relating  the  effectiveness  of recognition,  r,  to  the  number of groups is  r  = 1 -
(1 - p)N  (see  Fig.  1).