Download A theory: parts of the brain control other parts

Connectionism, controllers and a brain theory
Asim Roy
Department of Information Systems
Arizona State University, Tempe, AZ 85287-4606
USA
Phone: (480) 965-6324
Email: [email protected]
Fax:
(480) 727-0881
Abstract
This paper proposes a new theory for the internal mechanisms of the brain. It postulates that there are controllers in
the brain and that there are parts of the brain that control other parts. Thus the theory refutes the connectionist
theory that there are no separate controllers in the brain for higher-level functions and that all control is “local and
distributed” at the level of the cells. Connectionist algorithms themselves are used to prove this theory. Plus there is
evidence in the neuroscience literature to support this theory. Thus the paper proposes a control theoretic approach
to understanding how the brain works and learns. That means that control theoretic principles should be applicable
to developing systems similar to the brain.
Keywords
Connectionism, controllers, control theory, learning algorithms, brain theory.
Connectionism, controllers and a brain theory
1.
Introduction
Connectionist theory postulates that the brain does not have controllers in it, controllers (which could consist of a
cell or a group of cells in the brain) that control other parts of the brain (another cell or group of cells somewhere
else in the brain) [see 11, 16, 17, 18, 19, 21, 22, 23, 24, 26, 32, 33, 47, 48 and many others]. For example,
Rumelhart, Hinton and McClelland (p. 134, chapter 4, “PDP Models and General Issues in Cognitive Science” in
[47]) argue as follows about their brain-like PDP models: “There is one final aspect of our models which is vaguely
derived from our understanding of brain functioning. This is the notion that there is no central executive overseeing
the general flow of processing. In conventional programming frameworks it is easy to imagine an executive system
which calls subroutines to carry out its necessary tasks. In some information processing models this notion of an
executive has been carried over. In these models, all processing is essentially top-down or executive-driven; if there
is no executive, then no processing takes place at all”. Thus they claim that in their PDP models, “There is no
executive or other overseer” (p. 47, chapter 2, “A General Framework for Parallel Distributed Processing” in [47]).
So connectionists believe that their brain-like methods and models do not use the standard control theory concept of
a controller or executive in the system.
This paper argues that connectionist systems are indeed based on the principles of control theory where controllers
or executives are used to operate systems. To make this argument, the paper first examines the basic concept of a
controller or executive in a system and then shows that some of the simplest connectionist learning systems - like
back-propagation [47, 48], ART (adaptive resonance theory) [21, 22, 23], RCE (reduced coulomb energy) [42], and
RBF (radial basis function) [37] - actually use controllers or executives in their operation. By logical extension,
more complex connectionist systems, such as those that are either similar in structure to these simple systems or are
based on these simple systems, also use controllers and therefore are based on the principles of control theory. This
paper, therefore, refutes the claim of connectionists that their methods do not employ controllers or executives in
their systems. In addition, if connectionist methods and models are supposed to be brain-like, then the brain itself
must be employing controllers or executives in its operations. Thus there must be parts of the brain that control
other parts. Therefore, standard control theory principles should apply to understanding the internal mechanisms of
the brain and those principles should also apply to designing and developing systems similar to the brain [10, 28,
43, 44, 45, 46].
The main motivation for looking at alternative theories for the internal mechanisms of the brain comes from the
limitations of current theories of cognition as explained very well in a recent paper by Anderson and LeBiere [5].
Another strong motivation derives from the fact that training neural networks with connectionist algorithms
involves a lot of trial-and-error with various learning parameters and that kind of external intervention (or human
baby-sitting) of learning algorithms is definitely not a property of the brain, because no one ever supplies to the
brain the kind of algorithmic information (e.g. network designs, learning parameter settings) supplied to
connectionist algorithms. Hence, to be truly brain-like, new autonomous-type learning algorithms are needed that
don’t require the kind of external intervention required by connectionist algorithms. And autonomous learning
algorithms are an absolute necessity to build robots (software or hardware) that can learn on their own without
human intervention. So all these problems entail looking at new theories of the brain that are more consistent with
neuroscience understanding of how the brain works and new theories can open the door for the exploration of new
kinds of learning algorithms with better algorithmic characteristics, as discussed later in this paper. For example,
properties like polynomial time complexity of learning algorithms can never be achieved with connectionist ideas,
but can be achieved using control theoretic ideas [10, 28, 43, 44, 45, 46].
The paper is organized as follows. Section 2 characterizes the notion of control and controllers in any system. In
section 3, it is argued that some of the simplest connectionist systems actually use executive controllers in them.
Section 4 shows the neuroscience evidence for control structures in the brain. It also shows that control theoretic
notions are widely used in other forms of cognitive modeling. Section 5 addresses the question of whether
controllers of the type used in connectionism can actually exist in the brain. This is followed by a conclusion in
section 6.
2.
Standard Control Theory – What are controllers? What are their characteristics?
In general, most complex systems can be decomposed into various subsystems according to their functionality.
Controller-based systems are characterized by the presence of one or more controllers in them, controllers that
control (or operate) one or more subsystems within the system. In other words, in a controller-based system, there
might be one or more subsystems in control of other subsystems that are subservient to them in some sense. Thus,
in this case, the controlling subsystem can be called the "master or executive" subsystem. The main function of a
controller is to supply certain operating parameters to the subservient subsystem. That obviously can be done in a
variety of ways. Perhaps some examples will clarify this notion. For example, humans operate many man-made
devices - e.g. a car, an airplane, or a TV. In these overall systems, the car, the airplane or the TV is the
"subservient" subsystem and the human is the controller, the "master or executive" subsystem. The overall system
consists of both the man-made device (the car, the airplane, or the TV) and the human. The human in these systems
supplies the operating parameters to the subservient subsystems. For example, the human uses the accelerator of a
vehicle to set its speed, its steering wheel to determine its direction of movement, and the buttons of a remote
control to control a TV. In subsequent discussions, the subservient subsystem, without its controller, will simply be
called the “system” and the subservient system plus the controller will be called the “overall system.”
2.1
The argument against the notion of a controller in any system
Before proceeding further, one has to deal with the arguments against the very notion of a controller, because the
notion of a controller in any system (natural or man-made or any combination of them) is very much disputed by
many in brain-related sciences. Dealing with this issue also brings out a very important property of controllers. The
standard argument against controllers runs as follows. The car, the airplane or the TV that is operated by a human is
actually a feedback system. In a feedback system, a subsystem receives inputs (feedback) from the other
subsystems, and these inputs (feedback) are then used to determine its output(s), its course of action. Thus these
subsystems are completely dependent on each other (co-dependent) for their outputs and, therefore, there is no
subsystem controlling another subsystem in these overall systems. Thus, the argument goes, it is not proper to
characterize the human as the controller in any of the above-mentioned systems, because the human determines the
course of action based on information (feedback) from the other subsystems such as the car, the airplane, or the TV.
Thus, it is further argued, if one is intent on claiming that the human is the controller in these overall systems, then
the system itself (the car, the airplane, or the TV) can also be claimed to be the "controller," the one that controls
the human. So, by these arguments, there are no controllers (central or distributed) in any system, natural or manmade or any combination of them. On the surface, the above arguments seem to be correct, because in a feedback
system, as in the examples above, the so-called controller’s actions depend on the state of the subsystem it is trying
to control. So the output of any subsystem, including the controller, is a function of the inputs (feedback) received
by it from the other subsystems. So one is back to ground zero in trying to characterize controllers and distinguish
them from the subsystems they control.
2.2
Back to square one – So who is a controller? What are its characteristics?
This “no controller in any system” argument has to be dealt with first before proceeding further. A fundamental
characteristic of a controller, unlike the system that it controls (operates), is that it can determine its course of action
(its output signals, that is) without considering the state of the system it is trying to control. In other words, the
controller can also operate in a non-feedback mode. That means, it can operate with other types of inputs to
generate its output signals and it is thus not necessarily dependent on feedback from the system it is trying to
control. So a controller, by its very nature, is capable of operating in a manner that ignores the feedback of
information from the system it is trying to control. Some examples will clarify this notion. For example, a person
who is operating a TV with a remote control can simply close his or her eyes and turn the TV ON or OFF at
random or in some predetermined fashion without considering what is being shown on the TV. Thus turning the TV
ON or OFF (the human controller’s actions, that is) is no longer depended on what is on the TV (the state of the
subservient system, that is); the TV no longer has any influence on the controller, the person with the remote
control. But the subservient system itself (the TV) still depends on the ON/OFF signal from the controller (the
human) for its operation, that can’t be or has not changed. In other words, the TV (the subservient system) cannot
ignore the signals from the controller, the human, but the controller can easily ignore the signals from the TV. The
same controller characteristic holds in other cases too. For example, a driver can operate a car in a non-feedback
mode by driving with the eyes closed and ears blocked, cutting-off all feedback through those channels, although
the risk of an accident would increase substantially.
Thus the basic characteristic of a controller in any system is that it can operate in many different modes, with
different kinds of inputs, and that its operation is not necessarily or solely depended on feedback (input) from the
subsystem it is trying to control. In other words, the controller has the ability to send different operating instructions
to the subsystem it is controlling, but those operating instructions need not depend on feedback of information from
that subsystem. In contrast, the subservient system itself is still dependent on operating instructions from the
controller for its operation, which has not changed. This is the main distinguishing character of a controller. And
this is the character that entitles it to be called the “master” subsystem, because it can send signals to the
subservient system and operate it in any arbitrary manner. In other words, the subservient system is “depended” on
the controller signals for its operations, but there is no such dependency the other way round. Note that in some
systems, the “master” subsystems may not have continuous contact with their “subservient” subsystems. In other
words, there may not be continuous, interactive feedback in these systems. Connectionist systems in particular, like
ART and back-propagation, are similar to these systems. For instance, consider a doctor who provides treatment to
a person who is ill. The doctor may not have contact with a patient for a while during a certain treatment (e.g. using
a medication for a while). In this situation, the doctor is not in continuous contact with the subservient system, the
patient, but has only intermittent contact (e.g. daily or weekly). The doctor, however, is still the controller in this
situation, since he or she can be arbitrary in the treatment of the patient. So this is a type of system where the
controller can be “out-of-contact” with the subservient system for some period of time.
3.
Do connectionist systems have executive controllers in them?
To prove that connectionist systems have executive controllers, one has to show that connectionist systems either
have controllers inside them or use outside controllers or a combination of the two. To prove this, all one needs to
do is examine some of the simpler connectionist learning systems and show that they use executive controllers that
operate the learning system. Once that is established, it then follows by logical extension that more complex
connectionist systems, those that in turn use these simpler systems or are similar in structure to them, also use
executive controllers. Here a few well-known connectionist learning methods are examined and it is shown that
they employ executive controllers in their operation. It is appropriate to start with the best-known connectionist
learning method: the back propagation algorithm [47]. In algorithms like back-propagation, including any
variations of it, an external agent (perhaps a human, perhaps another module in an overall connectionist system)
supplies from outside the algorithm the design of a network and the values of the various learning parameters that
are necessary inputs for the operation of the algorithm. Since the outside agent can determine the network design
and the values of the various learning parameters potentially in an arbitrary manner (this arbitrary nature of the
agent can be verified with certainty when a human is the external agent and is providing the information to the
algorithm), the outside agent therefore satisfies the necessary character of arbitrariness of a controller as discussed
in the last section. In addition, the overall behavior of the back-propagation external agent is very much like a
doctor trying a particular treatment on a patient, waiting to see the outcome of the treatment, and if the outcome is
not satisfactory, trying a different treatment and so on. In a similar vein, the back-propagation external agent tries a
particular network design and a set of learning parameters, waits to see the outcome of back-propagation learning,
and if the outcome is not satisfactory, tries a different network design and a different set of learning parameters and
so on as shown in Figure 1. So there is clearly a central executive that oversees the operation of the backpropagation algorithm, much like a doctor with a patient. This fact clearly refutes the claim of Rumelhart, Hinton
and McClelland [47] that in PDP models, “There is no executive or other overseer.” Note that an executive
controller is part of the conceptual structure of the back-propagation learning system, independent of how it is
implemented and, in particular, whether or not a neurocomputer, with parallel computation capabilities, is used for
its implementation. Also note that the foregoing analysis of the back-propagation learning system is based strictly
on its logical structure and nothing else.
Executive-level
Controller for
Connectionist
Algorithm
- Controller’s
objective is to get
good performance
from the trained neural
network
New Network Design and Learning parameter
settings
Connectionist Algorithm
- Makes connection
weight and other
adjustments to the neural
network using local
learning rules
- Current state of the network, including
network design, connection weights, etc.
- Current overall performance of
the network
Network Design
Module
Module to
determine
learning
parameters
Training
Examples
External Source or
Environment
Figure 1 – Diagram showing controller interactions in connectionist algorithms
For other connectionist learning systems like ART (adaptive resonance theory) [21, 22, 23], RCE (reduced coulomb
energy method) [42], RBF (radial basis function method) ([37] and others) and the like, the network design function
is housed in a module inside the algorithm. The usual design task in ART, RBF, RCE and the like is to add a new
prototype or exemplar to the network - that is, to add a new node to the single hidden layer of the network where a
node is specified by a center and a radius or width. The design task, therefore, is to expand the size of the network
as and when necessary. The training task is to make adjustments to all those prototypes or exemplars – that is, to
adjust their centers and widths or radii. So, in the logical structure of these learning systems, the design and training
functions are housed in separate modules. In this logical view, therefore, the design module supplies to the training
module the design of a network. Who coordinates all of this interaction between the design and training modules?
Obviously an executive controller, who else! So the logical structure of these learning systems is just a slight
variation of back-propagation-like systems – there is an executive controller that calls the design module to provide
a network design which is then passed onto the training module for training as shown in Figure 1. The executive
controller in these systems also supplies the various learning parameters to the design and training modules, just as
in back-propagation-like systems. The iterative process of calling the design module for a network design, then
supplying that design, along with other parameters, to the training module for training continues until the executive
controller is satisfied with the outcome. So again, these connectionist learning systems are found to have an
iterative process that is coordinated by an executive controller which is similar in structure to the doctor-patient
example discussed above. This again clearly refutes the claim that connectionist systems do not employ an
“executive or other overseer” in them. And the analysis of these connectionist systems again shows that
connectionists use controllers or executives to operate their systems.
Note that the functions of a controller in these connectionist systems vary. In ART, RBF, RCE and the like, the
design module, unlike back propagation, is outside the controller because network design is a part of these
algorithms. The analysis of the logical structure of these algorithms is based on what is normally part of these
algorithms and so the resulting controller can have different functions and roles compared to some other algorithm.
4.
Control structures and signals – some neuroscience and cognitive modeling evidence
This section adds to the analysis of connectionist systems in the last section; it provides some additional insights
and evidence from neuroscience for the claim that there are parts of the brain that control other parts. At the outset,
it should be noted that the words "control," "regulation," “influence,” and "coordination" are used quite extensively
in the neuroscience literature to describe the functions of different parts of the brain. So the notion of "control" is
indeed used in neuroscience to analyze and understand the brain. A few quotes are provided here from the
neuroscience literature to show that it is quite well understood in neuroscience that different parts of the brain
control other parts. First, here are some observations from [30] on the overall controlling function of the brain (page
282): "The nervous system may be divided into the central nervous system, composed of the brain and the spinal
cord, and the peripheral nervous system, composed of ganglia and peripheral nerves. The peripheral nervous
system, which has somatic and autonomic components, relays information to the central nervous system and
executes motor commands generated in the brain and spinal cord." Here are some more characterizations of the
various control structures in the brain [30] (page 279): "Voluntary movement is controlled by complex neural
circuits in the brain interconnecting the sensory and motor systems. Although all voluntary movement is controlled
directly by the motor system, the decision to initiate a voluntary movement is regulated by the motivational system.
We reach for a glass of water if we are thirsty or a piece of fruit if we are hungry. The motivational system
influences voluntary movement by acting on the somatic motor system in the brain. In addition, it influences
behavior through its action on the autonomic nervous system, which innervates the exocrine glands, the viscera, and
smooth muscles in all organs of the body. As we have seen, the autonomic nervous system has three major
divisions: sympathetic, parasympathetic, and enteric. The sympathetic and parasympathetic divisions, which
regulate the body's basic physiology, also mediate motivational and emotional states. The main control center for
the autonomic motor system is the hypothalamus, which is critically involved in the regulation of endocrine
hormone release. The hypothalamus sends out descending fibers that regulate sympathetic and parasympathetic
nuclei in the spinal cord and brain stem…. Thus, in its role as central governor of the autonomic motor system the
hypothalamus directly regulates autonomic output and endocrine function and is responsive to a broad spectrum of
behaviorally important stimuli."
Here are some other characterizations of the various control and regulatory structures within the brain from [34]
(page 65): "Among the major subsystems of the brain there are a number of candidates for the role of coordinator
and integrator of its overall activities. The prefrontal portion of the cerebral cortex has long been recognized as the
part of the brain that involves the planning and initiation of activities at the highest level. In addition, since Papez's
pioneering work on the limbic system, this system and its hippocampal and other components have been understood
to play a major role in the overall storage of memory, integration of intersensory experience, and coordination.
Eccles and his associations … have suggested that now the cerebellum needs to be added to the list of candidates
for playing an integration or coordinating role."
Neurotransmitters are involved in many functions in both the central and peripheral nervous systems. Dopamine, a
neurotransmitter, is understood to be a global signaling mechanism in the brain and supposed to provide a teaching
signal to parts of the brain responsible for acquiring new behavior. In Scholarpedia
(http://www.scholarpedia.org/article/Reward), Wolfram Schultz writes: “Information about rewards is processed in
a number of brain structures. The dopamine neurons, named after the neurotransmitter they release with nerve
impulses in their projection territories, are located in the midbrain structures substantia nigra (pars compacta) and
the medially adjoining ventral tegmental area (VTA) ... The axons of dopamine neurons project to the striatum
(caudate nucleus, putamen and ventral striatum including nucleus accumbens), the dorsal and ventral prefrontal
cortex, and a number of other structures. Further reward signals are found in the neurons of the dopamine
projection structures themselves, including the striatum, orbitofrontal cortex and amygdala …. Rewards influence
the action-related activity of neurons in additional structures including striatum and prefrontal and parietal cortex
…. Additional brain structures influenced by reward include the supplementary motor area in the frontal lobe,
the rhinal cortex in the temporal lobe, the pallidum and subthalamic nucleus in the basal ganglia, and a few others.”
In [49] he notes: “Dopamine innervation of all neurons in striatum and many neurons in frontal cortex would allow
the dopamine reinforcement signal to exert a rather global effect.”
The recent discovery of neurogenesis (new cell birth) in adults will dramatically alter our thinking about the brain
and how it works. The following is from Dupret et al. [13]: “It was classically assumed that once the development
of the central nervous system ended, “everything can die, nothing can regenerate and be renewed” [7]. This dogma,
restricting neurogenesis to a developmental phenomenon has, however, been challenged by the discovery that new
neurons are created in specific regions of the adult mammalian brain [3, 20]. The dentate gyrus (DG) of the
hippocampal formation is one of the few structures where adult neurogenesis occurs in mammals [1], and it has
been estimated that several thousand new cells are generated daily [8, 41]…. Indeed, during brain development,
many more neurons are produced than are actually needed, and the active and selective removal of the cells that
have not yet established appropriate synaptic connections allows for the sculpting of the relevant and functional
neural networks… These results indicate that spatial learning involves a cascade of events similar to the selective
stabilization process by which neuronal networks are sculpted by adding and removing specific population of
cells as a function of their maturity and functional relevance.”
The following observation about adult neurogenesis is from [12]: “Our understanding of how the brain functions
and undergoes adaptation and plasticity has changed dramatically over the past several years. A great deal of work
has demonstrated that altered levels of neurotransmitters, second messenger pathways, and gene expression profiles
underlie cellular and behavioral plasticity. This includes models of learning and memory as well as models of
depression, anxiety, and psychosis, and the long-term actions of psychotropic drugs. However, it is now becoming
increasingly evident that changes in cellular morphology and even more pronounced alterations in brain structure
also contribute to neural plasticity or remodeling…. Studies in recent years demonstrate that new cell birth occurs
in adult brain and that the rate of neurogenesis and the survival of new neurons is regulated by a number of
environmental, endocrine, and pharmacological treatments.”
And the following evidence is from [9] linking learning and neurogenesis: “Since the discovery of adult
neurogenesis, a major issue is the role of newborn neurons and the function-dependent regulation of adult
neurogenesis. We decided to use an animal model with a relatively simple brain to address these questions. In the
adult cricket brain as in mammals, new neurons are produced throughout life. This neurogenesis occurs in the main
integrative centers of the insect brain, the mushroom bodies (MBs), where the neuroblasts responsible for their
formation persist after the imaginal molt. The rate of production of new neurons is controlled not only by internal
cues such as morphogenetic hormones but also by external environmental cues..…In search of a functional role for
the new cells, we specifically ablated MB neuroblasts in young adults using brain-focused gamma ray irradiation.
We developed a learning paradigm adapted to the cricket, which we call the "escape paradigm." Using this operant
associative learning test, we showed that crickets lacking neurogenesis exhibited delayed learning and reduced
memory retention of the task when olfactory cues were used. Our results suggest that environmental cues are able to
influence adult neurogenesis and that, in turn, newly generated neurons participate in olfactory integration,
optimizing learning abilities of the animal, and thus its adaptation to its environment.” Obviously, adult
neurogenesis has great significance for the design process of networks in connectionist learning algorithms.
Plenty of other similar references can be provided. In summary, the notion of subsystems within the brain
controlling other subsystems is well-established in neuroscience, although the various characterizations of exactly
who does what would evolve and change over time as more research is done.
On the modeling side, Haykin [25] provides characterizations of artificial neural networks in terms of the common
constructs of control theory. In [27], Robert Hecht-Nielsen describes in detail the sort of computational control
architecture needed to implement the backpropagation algorithm in a parallel and distributed manner. And standard
symbolic cognitive architectures such as ACT-R [4], CAPS [29], Soar [38] and Epic [36] are known to use control
theoretic structures. More recent work in the field of computational cognitive neuroscience also use control signals
to communicate between different neural subsystems. One such architecture, the Leabra model of O’Reilly and
Munakata [40] uses both Hebbian and error-driven connectionist modules on top of a biologically-based pointneuron activation function with inhibitory competition dynamics. Leabra can create modular organizations similar
to the brain and use memory to learn cognitive and perceptual tasks. But the overall modular structure does use
control signals to regulate different learning processes. CAPS [29] also uses control signals to allocate processing to
various modules of the network. Control theory also forms the foundation of more neuroscience-oriented neural
engineering approaches such as the work of Eliasmith and Anderson [14]. In general, connectionist learning
algorithms that grow networks based on a global measure of performance were always in question because they
included a highly centralized and global mechanism that could not be clearly mapped onto known neural processes.
The recent works in computational cognitive neuroscience that use control structures are starting to resolve these
problems.
5.
Can there be controllers in the brain of the type used by connectionist algorithms?
A legitimate question to ask at this point is: Can the types of controllers required by connectionist algorithms
actually exist in the brain? Only neuroscience can answer this question in a final, definitive way. But there is plenty
of evidence already in the neuroscience literature about control and regulatory structures in the brain and those
structures send out a variety of complex control signals to other parts of the brain. So there is no question about the
existence of various controllers in the brain. So the remaining question is: Can there be controllers in the brain that
actually generate the kind of signals required by connectionist algorithms? The answer to this question, from a
connectionist point of view, has to be an unequivocal YES, because, as shown by the analysis in this paper,
connectionist algorithms rely on these types of controllers. Moreover, if one believes in connectionism, then there is
no choice but to believe that these types of controllers exist in the brain. If such controllers are out-of-the-realm of
possibilities, then connectionism itself becomes an invalid, fictitious model of the brain.
From a neuroscience perspective, however, such controllers should not be out-of-the-realm of possibilities.
Neuroscience literature mentions much more complex control signals being generated by various control structures
in the brain. Motor control signals are a prime example of such complex control signals. So there is no evidence in
neuroscience to suggest that the kind of control signals used by connectionist algorithms cannot be feasibly
generated by any of the controllers in the brain.
6.
Conclusions
This paper has proposed a new paradigm for the internal mechanisms of the brain, one that postulates that there are
parts of the brain that control other parts. In other words, standard control theory can be used to model the brain and
create brain-like systems. Since this paradigm is in direct conflict with the connectionist paradigm, the paper
analyzed connectionist systems themselves to prove its case - it showed that connectionist systems themselves use
executive controllers that control its various subsystems. To establish the connection between control theory and
connectionism, the paper first examined the notion of control and controllers in any system and characterized
controllers as subsystems in an overall system that can operate in an arbitrary manner. Using this characterization
of arbitrariness, it then performed a logical analysis of the structure of some core connectionist learning systems like back-propagation, ART (adaptive resonance theory), RCE (reduced coulomb energy), and RBF (radial basis
function) - and showed that they have and do use executive controllers in them. Sometimes these executive
controllers are housed within the learning system (ART, RCE, RBF and the like) and sometimes they are external
to the learning system (back-propagation and the like). But the paper clearly establishes that executive controllers
do exist in these systems and thus these systems are based on control theory principles. By logical extension, more
complex connectionist systems, the ones that use these simpler learning systems inside them or rely on them in
some other manner or are similar in structure to them, also use executive controllers and control theory principles.
Having established this connection between connectionism and control theory, it is a small step to argue that if
connectionist models are brain-like, then, given that they use executive controllers, the internal mechanisms of the
brain must also use executive controllers. Hence it can be generalized and claimed that there are parts of the brain
that control other parts.
It should be pointed out that this theory does not posit that there is a single executive controller in the brain. Instead
it envisions “multiple distributed controllers” controlling various subsystems or modules of the brain. The main
argument of the paper that connectionists use “executive controllers” is only pointing out that their algorithms use a
“central controller.” But different modules in the brain using connectionist-type learning can have their separate
controllers.
What are the implications of this discovery of connectionism using control theory principles? First, a control
theoretic approach to understanding how the brain works and learns could overcome many of the limitations of
connectionism and lead to the development of more powerful models that can properly replicate the functions of the
brain, such as autonomous learning without external intervention. None of the current connectionist algorithms can
learn without human intervention that requires setting various learning parameters by a trial-and-error process. So it
is impossible to build autonomous robots (software or hardware), ones that can learn on their own, with these
connectionist algorithms. This new control theoretic paradigm, however, should allow the field to freely explore
other means of learning in neural networks.
So in what other ways can algorithms learn under this control theoretic paradigm? First, the door is now wide open
to the possibility of sending “other kinds of control signals” beyond those envisioned and used by connectionist
algorithms. For example, one can now think of controllers sending actual weight adjustment instructions to a neural
network. Thus there should no longer be a need for connectionist type algorithms that use local learning laws to
compute the network weight adjustment decisions locally. Also, for example, one can now think of the controller
having access to lots of previously stored information about the problem in different parts of the brain and using
such information to make better network design and weight adjustment decisions. So one can now make the
controller do much more work (add functionalities to it) than previously envisioned in connectionist algorithms. In
a sense, the added functions of the controller would only be limited by imagination and thus the algorithmic
possibilities become endless under this control theoretic paradigm. This theory thus opens the door to constructing
new kinds of learning algorithms that are more efficient and effective than connectionist ones. Using such
alternative means of learning, Roy et al. [43, 44, 45, 46] have developed robust and reliable learning algorithms that
have polynomial time computational complexity in both design and training of neural networks, a desirable
computational characteristic that has eluded connectionist algorithms thus far. Those algorithms don’t use local
learning laws and use memory to store training examples, which is a departure from the connectionist idea of
memoryless learning. Such departures from connectionist ideas could not have been justified as brain-like without
the theory presented in this paper. Leabra models of O’Reilly and Munakata [40] from computational cognitive
neuroscience are also examples of using much more freedom in building brain-like models. And data mining
algorithms [6, 15, 35, 50] and support vector machines and soft computing including hybrid and fuzzy systems [2,
31, 39, 51] are other sources of rich ideas to include in brain-like learning algorithms of the future.
One should reiterate here that connectionist algorithms were not originally designed to exploit higher-level
controllers. They therefore are not based on standard control theory concepts, although the analysis in this paper
happens to find that, in the end, they couldn’t totally exclude higher-level controllers. Connectionists claim they use
“only local, distributed control, not any higher-level control.” And local and memoryless learning ideas are derived
directly from the idea that there are no higher-level controllers and that all control is local. Thus the concepts of
local and memoryless learning are inseparable from the concept of “only local, distributed control, not any higherlevel control.” One can’t claim, therefore, that differences in the computational characteristics between
connectionist and other classes of learning algorithms arise not from the control perspective (local vs. non-local),
but from other assumptions made by connectionists, like local and memoryless learning, that are completely
independent and unrelated to the control perspective. Local and memoryless learning are, in fact, directly tied to the
control perspective of connectionists that there are no higher-level controllers. Therefore all differences in the
computational characteristics between connectionist and other classes of learning algorithms arise from the control
perspective (local vs. non-local) and its derivative ideas and not from any other independent source.
In summary, this theory opens the door for research on new kinds of learning algorithms free from the restrictions
of connectionist ideas like local and memoryless learning. And this new paradigm can help in the development of
truly autonomous learning systems through the exploration of other means of learning.
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
Abrous D.N., Koehl M., Le Moal M. (2005) Adult neurogenesis: From precursors to network and
physiology. Physiol Rev 85: 523–569.
Al-Alaoui, M.A., Mouci, R., Mansour, M.M., Ferzli, R. (2002) A cloning approach to classifier training.
IEEE Transactions on Systems, Man and Cybernetics, Part A, 32, 6, 746- 752.
Altman J. (1962) Are new neurones formed in the brains of adult mammals? Science 135: 1127–1128.
Anderson, J.R. (1993). The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum.
Anderson, J. and LeBiere, C. (2003) The Newell Test for a theory of cognition, Brain and Behavioral
Sciences, 26 (5), pp. 618-619.
Apolloni, B., Brega, A., Malchiodi, D., Palmas, G., Zanaboni, A.M. (2006) Learning rule representations
from data. IEEE Transactions on Systems, Man and Cybernetics, Part A, 36, 5, 1010- 1028.
Cajal R. S. (1991) Cajal's degeneration and regeneration of the nervous system May RM, translator;
DeFelipe J, Jones EG New York: Oxford University Press. p. 766
Cameron H.A., McKay R.D. (2001) Adult neurogenesis produces a large pool of new granule cells in the
dentate gyrus. J Comp Neurol 435: 406–417.
Cayre M., Scotto-Lomassese S., Malaterre J., Strambi C., and Strambi A., Understanding the Regulation
and Function of Adult Neurogenesis: Contribution from an Insect Model, the House Cricket, Chemical
Senses Advance Access, April 2, 2007, DOI 10.1093/chemse/bjm010.
Christensen, W.D., and Hooker, C.A., "An interactivist-constructivist approach to intelligence: selfdirected anticipative learning", Philosophical Psycholog,. 13(1), 5-45, 2000.
Churchland, P. and Sejnowski, T., The Computational Brain. MIT Press, 1992.
Duman R. S., Nakagawa S. and Malberg J., “Regulation of Adult Neurogenesis by Antidepressant
Treatment,” Neuropsychopharmacology, 25, 836-844, 2001.
Dupret D., Fabre A., Döbrössy M.D., Panatier A., Rodríguez J.J., et al. (2007) Spatial Learning Depends on
Both the Addition and Removal of New Hippocampal Neurons . PLoS Biol 5(8): e214.
Eliasmith, C. and C. H. Anderson (2003). Neural Engineering: Computation, representation and dynamics
in neurobiological systems. MIT Press.
Elovici, Y., Braha, D. (2003) A decision-theoretic approach to data mining. IEEE Transactions on Systems,
Man and Cybernetics, Part A, 33, 1, 42- 51.
Fahlman, S. E. and Hinton, “Connectionists Architectures for Artificial Intelligence,” Computer, 20, 100109, 1987.
Feldman, J. A. and Ballard, “Connectionists Models and Their Properties,” Cognitive Science, 6, 205-254,
1982.
Fodor, J. and Pylyshyn, Z., “Connectionism and Cognitive Architecture: A Critical Analysis,” Chapter 12
in Haugeland, J. (ed), Mind Design II, MIT Press, 309-350, 1997.
Freeman, W. J., Societies of brains: A study in neuroscience of love and hate. Hillsdale, NJ:Lawrence
Erlbaum, 1995.
Gross C.G. (2000) Neurogenesis in the adult brain: Death of a dogma. Nat Rev Neurosci 1: 67–73.
Grossberg, S., Studies of Mind and Brain: Neural Principles of Learning Perception, Development,
Cognition, and Motor Control. Boston: Reidell Press, 1982.
Grossberg, S., “Competitive learning: From interactive activation to adaptive resonance,” Cognitive
Science, 11, 23-63, 1987.
Grossberg, S., “Nonlinear neural networks: principles, mechanisms, and architectures,” Neural Networks,
1, 17-61, 1988.
Haugeland, J., “What is Mind Design.,” Chap. 1 in Haugeland, J. (ed), Mind Design II, 1997, MIT Press, 128, 1996.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, New Jersey, 2nd ed. (1999)
Hebb, D. O., The Organization of Behavior, a Neuropsychological Theory. John Wiley, 1949.
Hecht-Nielsen, R., Neurocomputing, Addison-Wesley (July 1990)
Hooker, C. A., H. B. Penfold, R. J. Evans, "Control, Connectionism and Cognition: Towards a New
Regulatory Paradigm,” Britt. J. Phil. Sci., 43, 517-536, 1992.
Just, M.A. and Carpenter, P.A. (1992). A capacity theory of comprehension: Individual
differences in working memory. Psychological Review, 99(1):122-149
Kandel E.R., Schwartz & Jessel, Principles of Neural Science (3rd ed.). Elsevier, 1993.
Kecman, V.: Learning and Soft Computing, Support Vector machines, Neural Networks and Fuzzy Logic
Models, The MIT Press, Cambridge, MA (2001).
Kohonen, T., Self-organization and associative memory. 3rd ed., Spriger-Verlag, 1989.
Kohonen, T., “Physiological interpretation of the self-organizing map algorithm,” Neural Networks, 6, 895905, 1993.
Levin, F. M. and Vuckovich, D. M., “Brain Plasticity, Learning, and Psychoanalysis: Some Mechanisms of
Integration and Coordination Within the Central Nervous System,” Chapter 3 in Levin, F. M. (ed),
Mapping the Mind, The Analytic Press, 1991.
Li, Xiangyang, Ye, Nong (2006) A supervised clustering and classification algorithm for mining data
with mixed variables. IEEE Transactions on Systems, Man and Cybernetics, Part A, 36, 2, 396- 406.
Meyer, D. E. and Kieras, D. E. (1997). A computational theory of executive cognitive processes and
multiple-task performance: Part 1. Basic mechanisms. PsychologicalReview, 104(1):3-65
Moody, J. & Darken, C., “Fast Learning in Networks of Locally-Tuned Processing Units,” Neural
Computation, 1(2), 281-294, 1989.
Newell, A. (1990) Unified Theories of Cognition. Cambridge, MA: Harvard
Pedrycz, W., Sosnowski, Z.A. (2000) Designing decision trees with the use of fuzzy granulation, IEEE
Trans. on Systems Man and Cybernetics, 30, 2, 151-159.
O’Reilly, R. C., & Munakata, Y. (2000). Computational explorations in cognitive neuroscience:
Understanding the mind by simulating the brain. Cambridge, Massachusetts: MIT Press.
Rao M.S., Shetty A.K. (2004) Efficacy of doublecortin as a marker to analyse the absolute number and
dendritic growth of newly generated neurons in the adult dentate gyrus. Eur J Neurosci 19: 234–246.
Reilly, D.L., Cooper, L.N. and Elbaum, C., “A Neural Model for Category Learning,” Biological
Cybernetics, 45, 35-41, 1982.
Roy, A., Kim, & Mukhopadhyay, “A Polynomial Time Algorithm for the Construction and Training of a
Class of Multilayer Perceptrons,” Neural Networks, 6, 4, 535-545, 1993.
Roy, A., Govil, S. & Miranda, R. “An Algorithm to Generate Radial Basis Function (RBF)-like Nets for
Classification Problems,” Neural Networks, 8, 2, 179-202, 1995.
Roy, A., Govil, S. & Miranda, R., “A Neural Network Learning Theory and a Polynomial Time RBF
Algorithm,” IEEE Transactions on Neural Networks, 8, 6, 1301-1313, 1997.
Roy, A. & Mukhopadhyay, “Iterative Generation of Higher-Order Nets in Polynomial Time Using Linear
Programming,” IEEE Transactions on Neural Networks, 8, 2, 402-412, 1997.
Rumelhart, D.E., and McClelland, J.L.(eds.), Parallel Distributed Processing: Explorations in
Microstructure of Cognition, Vol. 1: Foundations. MIT Press, 318-362, 1986.
Rumelhart, D.E., “The Architecture of Mind: A Connectionist Approach,” Chapter 8 in Haugeland, J. (ed),
Mind Design II, 1997, MIT Press, 205-232.
Schultz, Wolfram. Predictive reward signal of dopamine neurons. J. Neurophysiol. 80: 1-27, 1998.
Wang, L.P., Fu, X.J.: Data Mining with Computational Intelligence, Springer, Berlin, 2005.
Zeng, Xiao-Jun, Keane, J.A. (2006) Approximation capabilities of hierarchical hybrid systems. IEEE
Transactions on Systems, Man and Cybernetics, Part A, 36, 5, 1029- 1039.