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\begin{document}
\title {Natural Language Parsing using Cell Assemblies: computational 
        linguistics with attractor nets}
\author {C. Huyck, R. Belavkin, and C. Connolly \\
Middlesex University} 

\nocopyright
\maketitle
%undone get a letter of support from Murray Shanahan GC-5
%undone get a letter from John Deere
%undone get a letter from DeVries
%undone recognised researchers Dan and Satinder


\mheading{Introduction} 

We will develop a software agent based solely on simulated neurons
that uses natural language.  A parser based on simulated neurons will
have several benefits over symbolic parsers and even other types of
connectionist parsers.

Computationally, neural processors have an obvious mechanism of symbol
grounding; symbols can be grounded in sensory data giving a mechanism
for at least basic sensory semantics.  So neural processing has a hope
of resolving the classic AI symbol grounding problem \cite {Fodor},
since its symbols are grounded in sensory inputs. Semantics are
critical for resolving parsing ambiguities and for robust
parsing. Using grounded symbols as the basis of semantics will allow
the parser to be more effective.

Scientifically, neural simulations can model psychological phenomena
and can be compared to neural data by, for example, fMRI and electrodes on
individual neurons.  Consequently, psycholinguistic and neural data
can be directly compared to the parsing system.

Theoretically, processing with attractor networks can be advanced.
Attractor nets settle into stable or pseudo-stable states as
processing progresses. Simulated neural nets may act as attractor
nets, and most attractor net applications are limited to
categorisation.  We will build a parser with the lexicon, grammar
rules, and rule application mechanism all encoded in neurons.  These
active symbols \cite {Kaplan} will process and thus will apply rules
and build structures.  Cell Assemblies (CAs) are a higher order
structure within the network of neurons.  They are sets of highly
connected neurons that remain active even after external stimulation
ceases \cite {Hebb}. Individual CAs are single attractor states, but
applying rules rapidly generates new attractor states, and allows the
system to move to new states. This real application will be an advance
on current attractor net applications.

A neural system that can understand natural language, that is situated
in an environment, and can learn, will be a major advance over any
existing system.  Indeed, it will be a significant step towards an
intelligent system.

\mheading{Background}

\msubheading{Parsing and Related Natural Language Technologies} 

In the past twenty years, Natural Language Processing (NLP)
technologies have moved from academia into the business world.  Three
major reasons contribute to this move.  First, technologies have
reached a stage of sufficient maturity that they can work with real
world data.  Second, NLP has made use of machine learning to develop
systems that learn key features of the domain that is being processed.
Third, the popularity of the Internet has led to a vast quantity of
textual data that needs to be processed in a range of ways so that it
can be used \cite{Rau}.

Parsing is the central NLP subtask, involving processing a sentence to
recognise its syntactic structure, and it may generate semantic
structure.  A standard mechanism for measuring the effectiveness of a
parser is to gauge its performance on a large textual database such as
the Penn Tree Bank (PTB) \cite {Marcus}.  The best parsers score
around 90\% on this task. Collins' system is a good example of a high
scoring parser \cite {Collins}, although this is an entirely syntactic
measurement.  The best work in this area has taken advantage of
machine learning techniques to learn probabilities for selecting
between ambiguous syntax trees.

While the NLP field continues to advance, the pace has slowed.  This
may be due to a limitation of current symbolic techniques.  With the
parsing problem, systems are on the 90\% plateau largely because
ambiguity arises from the semantics and pragmatics of the
sentence. The semantics of at least concrete nouns are based, to a
large degree, on the things that can be done with the referent of the
word \cite {Gibson}. So {\it pen} is intimately related to {\it
writing} because that is what one does with a {\it pen}.  Current
machine learning techniques are unable to handle this type of natural
semantics. Instead they are usually based on a ``bag of words''
approach where the semantics of a word is based on the frequency of
the words with which it appears \cite {Lewis}.

More importantly, NLP needs to use semantics because it is the
semantics that are of interest. The parsers that perform best on the
PTB do not generate a semantic analysis; they are built to solve the
syntactic task. There are no standard tests for semantic evaluation,
though some tests have been proposed \cite {Gaizauskas}.

An existing system, Plink \cite {Huyck-Plink,Huyck-Plink2}, uses
semantics and syntax in parallel during parsing. By using this
information, preferences can select the appropriate rule to apply,
parsing in linear time, to some extent like people.  It is important
for computers to process language as people do.  Unlike formal
languages, natural languages are ambiguous, are frequently
ungrammatical, and often omit important features.  By processing
language like humans, the system will be able to use meanings that are
similar to those of people.  Existing text engineering systems are
usually built to solve one task, like extracting database templates
for rocket launches \cite {MUC7}.  They are somewhat successful, but
are limited by their singular nature and have to be re-engineered for
another task.  A system that processed language like a human would be
able to function in a wide range of domains.  Since Plink parses like
humans, it is more closely related to psycholinguistic research than
most other parsers.

\msubheading{Connectionist Parsers} 

There have been some connectionist Natural Language Parsers including
those by Mikkulainen (1993) and Henderson (2000).  These parsers are
not based on anything like real neurons and they have had basic
problems such as their inability to accomplish real world tasks.
These problems are usually solved by a symbolic solution.  Knoblauch's
system \cite {Knoblauch} uses a more interesting solution.  It is
based on model neurons with a medium degree of biological
faithfulness, implements a regular grammar, grounds its semantics in
the real world, and resolves ambiguity dynamically.

While Knoblauch's is the most sophisticated CA-based
system that we are aware of, it still has shortcomings.  Firstly, its
parsing mechanism is based on Regular Grammars; some support the
notion that language is Regular \cite {Blank}, but the consensus is
that natural language is at least Context Free.  Secondly, each word
is preallocated neurons that are later connected to sensory input;
clearly words are not preallocated particular neurons in the brain.
Both of these problems can be avoided, but require greater complexity.
Thirdly, the binding is done via synchrony.  This has a limit to the
number of different bindings, but also requires a great deal of
computation because it needs a fine time grain for simulation ($ \leq
$ 1 ms) \cite{connolly04:_approac}.


\eject
\msubheading{Computation with Neurally Plausible Attractor Nets}

The scientific community has an incomplete understanding of the brain
and neurons.  However, it is widely agreed that spiking leaky
integrators are a simple, largely accurate model of neurons, and that
the brain contains CAs \cite {Hebb}.  These two hypotheses are related
and are linked by the theory of attractor nets \cite {Amit}.

One large question in neural and connectionist research is what model
to use.  There are a wide range of models from Hopfield nets to
complex conductance based models.  Biological faithfulness usually
increases simulation time, so it is hard to simulate a large number of
very sophisticated neurons.  An example of the time vs. faithfulness
tradeoff is how frequently each element is updated.  Neural models
that are updated every 1 ms of simulated time usually incoporates
spike transmission times and refractory periods.  Models with larger
time grains do not need to account for these phenomena.

Though incomplete, spiking leaky integrators are a good model for
neurons because they are a simple model that is inexpensive to
simulate. They also have key features of neurons:  1) leaky integrators
collect activation from their inputs; neurons collect activation from
other neurons;  2) when an integrator collects enough activation, it
fires; when a neuron has enough activation it sends spikes to
connected neurons;  3) if the integrator does not fire, some of its
activation leaks away; if a neuron does not fire some of its
activation leaks away.

A network of leaky integrators may act as an attractor network.  If
enough integrators fire and their mutual connections are strong
enough, they will enter an attractor state where a large number of the
integrators are firing and will continue to fire. If enough neurons
spike and are connected with sufficient synaptic strength, they
will cause a CA to ignite.  According to Hebb \cite {Hebb}, CAs are
the neural correlate of long-term human concepts, and an active CA has
been correlated with buffers, short-term memory  and other types of
working memory \cite {Hebb,Kaplan,Malsburg}.

Neural nets and connectionist systems in general are almost always
used for categorisation tasks.  Attractor nets go to a state and
stay there.  However, animals cannot have their brains in a single
state; they must move on to new states.  Certainly environmental
input has an effect on the stability of a state, but attractor nets
can be modified to move on to new states in a principled way.

To bridge the gap to human level cognition, other types of
calculations need to be implemented.  For example, it has been
proposed that rules can be implemented with attractor nets \cite
{Huyck-CAParse}.

While other systems can implement rules, calculating with attractor
nets is of particular interest because they can learn. Work in
learning with attractor nets is largely based on learning categories.
There has been little work on learning other processes and since there
is little work in rule-application, there has been no work in learning
rules in attractor nets.

Connectionist systems also make use of parallelism. So attractor nets
that implement complex calculations on a parallel machine may be very
fast.  

\msubheading{Our Work to Date}

The PI's background is in NLP. He developed a symbolic human-like
parsing system, Plink \cite {Huyck-Plink,Huyck-Plink2}, that parses
linearly and robustly.  It is based on a large number of preferences
that determine the parse to pursue; a small look-ahead allows Plink to
avoid backtracking. It uses a unification-based grammar, integrating
syntactic and semantic processing. Plink performs well on the standard
syntax parsing metrics but has not exploited machine learning
techniques.  Plink has been used in a range of tasks including the
DARPA sponsored Message Understanding Competitions \cite{MUC4,MUC5,MUC7}
and text mining tasks.  Plink is also being used in a symbolic
conversational games agent \cite {Kenny}.

It would be relatively simple to use machine learning techniques to
set Plink's semantic parsing preferences.  However, we were interested
in the mechanism that people use, feeling that it would be easy to
duplicate other's CA work.  To our surprise, we found that very little
simulation work had been done with CAs and even less work with CAs for
practical AI tasks.  Consequently, we have spent the last several
years developing programs to solve basic CA problems and some
real-world tasks. This work is more fully explained in the Previous
Track Record section.

One key weakness of AI connectionist systems is binding \cite {Fodor}.
Binding enables calculations beyond categorisation, and is easily done
on vonNeuman systems.  The standard solution to the problem in
connectionist systems is to bind via synchronous firing
\cite{Malsburg}, and simulations of this exist \cite {Sougne}.  We
have implemented another type of binding using spontaneous neural
activation.  Spontaneous neural activation has several interesting
computational properties including the spread of activation beyond
neurons that are directly activated by external stimulation \cite
{Huyck-Spont}.  Our simulations \cite {Huyck-VB} using a single layer
model have shown that it can enable binding via a form of medium-term
memory that is rapidly formed (on the order of seconds), and persists
for the order of minutes.  This binding via medium-term memory has no
limit to the number of different bindings.  It is also more
computationally efficient than binding via synchrony because it can
work with a coarse time grain of 10 ms.

Almost all NLP systems use rules, and grammatical theory is largely
based on them. Connectionist systems can apply rules if they have
variable binding.  So, we now have the capability of developing a
neural-CA based NLP system.  It is now time to combine our NLP and CA
research.

\mheading{Proposed Project}

The ultimate goal of the project is to build a system that is situated
in an environment, senses, acts, and uses natural language.  It will
be based solely on simulated neurons, and will learn critical aspects
of the environment.

As this is uncharted territory, an iterative approach will be pursued
developing three versions of the system with each scheduled for one
year of the project.  This will provide steps, periods for
reflection, and a good basis for publications throughout the project.

An interleaved engineering and neuro-biological modelling approach
will be pursued.  In the interest of making an advance on a problem, an
approach that is not biologically valid may be pursued.  However, this
``hack'' will be replaced at a later stage by a more biologically
valid solution.  For example, we will initially provide CAs for words
with the neurons devoted solely to a given word. Later, we will use a
more biologically plausible approach allowing the system to learn the
words, and those CAs will share neurons.

Critically, this project will explore the dual dynamics of neural-CA
systems.  The short-term dynamics of CA ignition and cessation will be
explored via computations such as rule application.  The long-term
dynamics of CA formation via synaptic weight modification will be
explored by the formation of medium-term binding CAs, learning 
rule selection preferences, and rule learning.

NLP tests will use standard measurements such as precision and recall.
This will be on a restricted domain, so comparison to general systems
will not be readily available.  However, comparisons to the symbolic
Plink system will be made and the corpus will be made available for
others to test their parsing algorithms.  The corpora that will be
used will be domain dependent text.  Initially, these will be
sentences that we will generate.  These will include ambiguous
sentences, complex sentences, and ungrammatical sentences. Later, the
text will be used to train the system.  Finally, we hope to use the
system as an agent in an interactive videogame (see Methodology). 
This will take open ended text from a user, though it will only
properly understand text from the domain of the game.

\mheading{Methodology}

We propose a three step programme with each step encompassing the
development of a complete system, test environment, and a test of the
system in its environment.  All of the systems will be computer
simulations of neural processing.  The three steps are first to build
a parser; second to develop a system that senses its environment and
to integrate sensing with parsing; third to enable the system to
interact with other agents in the environment while making use of
language and sensing from the earlier system.

The first major task will be to engineer a parser.  A similar mechanism has
already been developed \cite {Knoblauch}, but is based on a very
simple Regular Grammar, and implements binding via synchrony.  This
first prototype will implement the Plink algorithm using a
unification-based grammar.  Additionally, it will bind via short-term
synaptic weight change. This first prototype will not learn words. 

This will be a significant development, but the initial idea was
proposed several years ago \cite {Huyck-CAParse}.  There will be
long-term CAs to represent words and grammar rules. Completed and
partially completed phrases are represented by medium-term CAs, and
these are the stack elements; this medium-term memory implements
binding.  Both rule application and selection are required.  A rule
may be applied when the appropriate phrases and words are on the
stack.  The application of a rule changes the contents of the stack.
Though the algorithm was proposed several years ago, an appropriate
variable binding mechanism, which is necessary to implement rules, has
only recently been developed.  Word representation is crucial, but in
this step will mimic earlier work \cite {Knoblauch} and use predefined
sets of neurons for each word.  For any given stack state, several
rules may be applicable.  The symbolic Plink parsing mechanism uses
preferences to resolve these ambiguities; the neural-CA parser will
use competition between CAs to implement these preferences. The
preferences will be encoded in the weights between rules, words, and
larger constituents.

The binding mechanism uses spontaneous neural activation \cite
{Huyck-Spont} and a compensatory learning rule \cite {Huyck-Overlap}
to implement medium-term CAs.  A special binding area is set up with
slightly different neural parameters and spontaneous activation.  This
area has synapses to and from the areas with long-term memories.
When CAs in these areas are active, they rapidly form a new CA that
includes the original long-term CAs and neurons in the binding area.
This can be used after the long-term CAs have stopped being active,
but is erased relatively quickly via synaptic weight change driven
by spontaneous activation.

The test of this system will be its production of correct semantic
interpretations in the form of frames \cite {Filmore}.  The frames
will initially be generated by statistical analysis of the firing
behaviour of the network after sentence processing. Later, frames will
be generated by associating slot labels with concepts via simultaneous
presentation.  The labels will activate when output is requested by
a trained signal. This is a type of actuatator.

The second step is to integrate sensing into the system.  This will
enable the system to learn words based on the environment.  Again
earlier work \cite {Knoblauch} has done this but the words were
prespecified and were associated with the sensed items.  The second
prototype will instead use a form of labelling where the system learns
the neural representation for the words.  In essence, this is a neural
implementation of the bipolar representation of words described in
Cognitive Grammars \cite {Langacker}.  This will provide CAs for words
and semantically related words will share some neurons. This simulated
learning of words is unique.

The environment will be the virtual environment of an interactive
videogame. We have some experience in this domain \cite {Kenny}, it is
an industrially important domain \cite {Laird}, and the videogame
environment is very flexible. This flexibility means it can range from
a relatively simple domain of simple objects to a very sophisticated
dynamic one with active agents.  The environment will be the Sierra
Half-Life \cite {Sierra} system; this is flexible shareware game
technology that has 3-D graphics capabilities, internet capabilities,
the ability to create new environments, and hooks for agent
development.

The system will be able to resolve syntactic parsing ambiguities like
PP-attachment ambiguities. Ambiguity resolution will be significantly
based on the words.
%undone cite PP attachement ford, bresnan and kaplan?

Testing of the second prototype will repeat the semantic test from 
the first step.  The system should also be able to name concepts in the
environment.  Finally, ambiguity resolution will be tested using 
a Parseval like measurement. %undone cite?

The third step will be to build an agent in the Half-Life environment.
This prototype will have the ability to move, take other actions
and have goals, and these will inform sensing. A spreading activation
network \cite {Maes} will be used for a simple form of planning. The
network selects actions based on the spreading of activation between
competence modules.  The spreading activation net should be directly
translatable into a neural-CA architecture.

Rule learning will be integrated into this agent.  Rule learning is
common in cognitive architectures such as ACT--R \cite {Anderson} that
are largely symbolic.  The system will be able to learn new grammar
rules to handle common ungrammatical phenomena.  It will also be able
to learn behavioural rules to improve its performance.  Ideally, it
will be able to explore the environment and learn new causal
relations.

The SOAR \cite{Newell90} and ACT--R \cite{Anderson} cognitive
architectures have implemented and tested theories of production rules
learning.  While SOAR has been using an elegant and powerful chunking
mechanism, the ACT--R theory has taken a more constrained, but more
controllable approach in which procedural knowledge is formed from
declarative representations as a result of generalisation and
abstraction (and in some cases specialisation).  This mechanism,
called {\em production compilation}, has been extensively used to
model many cognitive tasks including language acquisition and use.
This project will find a neural equivalent of such mechanism, and the
variable binding mechanism using spontaneous activation looks very
promising.

A working agent that senses and uses natural language gives us a host
of opportunities for exploration. These include, but are not limited
to, rule learning, simple conversational output, 2-D mapping,
cognitive mapping, language learning, planning, goal mechanisms,
learning from instruction, and even problem solving.

The initial tests for this third prototype will be its ability to
understand and follow instructions.  This will be similar to our
evaluation of a symbolic agent in the same domain \cite {Kenny}.
Measurement of the agent's ability to take instructions will
include specific tests and open domain tests with users.  Rule
learning will be tested initially by the ability to generate addition
tables.  Other tests of more sophisticated tasks, such as mapping and
simple conversational output, will be developed as necessary.

\mheading{Research Impact}

The basic engineering tasks will be successful, and this will be a
solid advance in computing with attractor nets, with significant
ramifications for anyone working in this area.  This basic engineering
is mostly in the first prototype, but will also involve duplicating
sensing research in a neural-CA net, and the development of a spreading
activation net.  

There will also be an advance in understanding neural-CA dynamics.
This includes the interplay between short and long-term dynamics, and
the simultaneous use of neurons as transducers, stores, and
processors of information.  This is largely done in the second phase,
though components are in both other phases. In particular, the dual
dynamics and the interplay between storage and transduction is
essential to the development of natural semantics based on sensing.
There will be a significant impact on the learning of concepts,
labelling concepts, and the understanding of NLP.

Learning complex relations based on synaptic weight change will be
concentrated in the third phase.  The development of a system that
learns rules based on synaptic weight change will be a major
advancement in itself.

The neural-CA development will improve understanding of neural
processing and neural learning.  The UKCRC sponsored grand challenge
on the Architecture of the Mind and Brain and the challenge on
Journeys in Non-Classical Computation will also both benefit.

NLP research will benefit from an example system that uses grounded
symbols.  In a related fashion, parsing theory will be advanced by a
novel neural parser.

A games agent that is instructed by natural language and learns as it
plays will benefit videogame users.  This could also be a new market
for videogame companies.

Perhaps most importantly, a situated agent that functions and learns
from its environment is an opportunity for a revolutionary advance.
The system may be able to learn the ramifications of its actions.
This could lead to a system with sophisticated natural semantics based
on sensing and action.  Additionally, the system could learn new
processes.  It might be able to modify its spreading activation net so
that it can set new goals and have new ways of achieving them.  The
system may even be able to learn from instruction.  It is not
currently clear how this work will advance during this grant, but at a
minimum, this will be a platform to explore these issues.  It is
conceivable that this work will lead to a revolution in computer
development and cognitive science research based around neural-CA nets
that learn; this revolution may be underway by the end of the decade.

\mheading{Dissemination}

We plan on disseminating the results of this work in a range of
conferences and journals. We intend to run a workshop on programming
with attractor nets at a conference such as the European AI Conference
in 2008.  Other conferences include the Computational Neuroscience
Conference, International Joint Conference on Neural Networks, Neural
Computation and Psychology Workshop, the International Joint
Conference on AI, the European AI Conference, the International
Conference on Cognitive Modelling and the Association for
Computational Linguistics Conference.  Journals include Neural
Networks, Neural Computation, Connection Science, Neurocomputing,
Computational Linguistics, and Artificial Intelligence.

A project web site will be maintained.  All software, benchmarks, and
reports will be made available on the site.


\mheading{Resource Management}

The PI will be responsible for the overall and day to day management
of the project.  Dr Connolly will work on this project full time and
consequently will be responsible for most of the programming.  It is
envisioned that the PhD student will concentrate on sensing. All three
will meet on a weekly basis to discuss advancements and issues.  There
will be further interactions, including the co-investigator, through
writing, system development, and presentations.

This is a major development project in the area of neural computation.
It is rare to find experienced researchers with skills in the
development of systems based on simulated neurons.  The availability
of Dr Connolly, a researcher with these skills, is an excellent
opportunity for a significant advance.  Simultaneous training of a
new researcher in this area, the PhD student, should help the project,
and will add another, rare, experienced neuro-computational engineer
and researcher to the small existing pool.

Dr Belavkin will focus on rules and rule learning.  Rules will be
implemented in the first phase and rule learning in
the third phase.  This enables independent streams of development
throughout the project. Dr Belavkin's expertise in symbolic rule
learning \cite {Belavkin-05} and cognitive architectures \cite 
{Belavkin-01,Belavkin-04} will be crucial to developing
mechanisms for learning rules.

A substantial number of presentations will be given during this
project to disseminate results in this novel area.  Two laptops will
be needed for presentations along with a travel budget.

Middlesex University will provide accommodation and use of laboratory
facilities for the named researcher and the PhD student.  A
considerable amount of simulation activity will be done with the
University providing computers.  It will also supply technical and
administrative support for the project.

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\end{thebibliography}
\eject


\mheading{Previous Track Record}

\msubheading {Principal Investigator: \\Dr Christian R. Huyck} has
been working in AI for over 20 years.  He has over 40 publications in
AI journals and conferences.  His two main research tracks are NLP,
and neural-CA nets.  He has been a lecturer at Middlesex University
since 1998 and is currently a principal lecturer.  He heads
Middlesex's AI research group consisting of a dozen academic staff and
a dozen PhD students.

Dr Huyck received his PhD from the University of Michigan in 1994.
While there he worked on a range of AI projects, but concentrated on
NLP.  He participated in the fourth and fifth Message Understanding
competitions \cite {MUC4,MUC5} while at Michigan, and subsequently on
the seventh \cite {MUC7}.  He also began collaboration with the
interdisciplinary SESAME group while at Michigan. This is a broad
group of researchers including researchers from AI, developmental
psychology, cognitive psychology, and even architecture.  The group is
interested in how people think and is informed by a Hebbian neural
hypothesis. It will provide an excellent forum for discussion of
issues related to this research project.

Dr Huyck's PhD thesis was based on a symbolic parser that was
effective as an engineering tool, but also parsed like humans \cite
{Huyck-Plink}.  Work on the Plink symbolic parser culminated with an
evaluation on the standard parsing metric \cite{Huyck-Plink2}.  This
evaluation showed that Plink performed well on the standard syntactic
measurement, 71/74 Precision/Recall, but below the best existing
systems, 88/89 \cite {Collins}.  To improve the system, some sort of
automated technique was needed to learn the parsing relationships.
Consequently, Huyck started research into CAs to resolve these
problems. Huyck has continued his NLP work \cite {Lethan} and is
collaborating with John Deere Inc. on Text Mining. Huyck is currently
working on a conversational games agent \cite{Kenny} that has also
given us experience in the games environment.

Huyck has concentrated on CA research for several years proposing it
as a good basis for a cognitive model \cite {Huyck-CogMod}.  This work
has been based on simple spiking fatiguing leaky integrators that are
simulated in discrete cycles that are approximately 10 ms of
biological time.  These largely adhere to known neural properties with
the obvious exception of discrete versus continuous time.  The main
advantage of these discrete cycles is that we can ignore refractory
times, synaptic delay, and transmission times across the synaptic
cleft as they are much quicker than 10 ms.  Consequently, a large
number of neurons can be simulated on a PC in real time.

This platform has been used to simulate a range of behaviours.  Like
other connectionist systems, it has done real categorisation tasks
including information retrieval \cite {Huyck-App}, categorisation of
ambiguous data \cite {Huyck-Overlap}, and hierarchical categories
\cite {Huyck-Hierarchy}.  Crucially, this involves neurons that are in
multiple CAs. This platform has been used for a novel form of variable
binding via medium-term memory \cite {Huyck-VB}.


\msubheading {Co-investigator: Dr Roman Belavkin} has been working in
Cognitive Science for seven years and is currently a Senior Lecturer at
Middlesex.  He has focused on Cognitive Architectures, cognitive models
of learning and decision--making. Recent work \cite {Belavkin-05} has
studied the effects of entropy in subsymbolic information processing
on learning in hybrid architectures and how it affects behaviour
\cite{Belavkin-04}.  Belavkin has also shown how rules contribute to
the U-effect in learning \cite {Belavkin-01}.

Roman Belavkin has worked as a senior lecturer at Middlesex since
December 2002. He was awarded his PhD in 2003 from the University of
Nottingham. His thesis was a multidisciplinary project on cognitive
modelling, and it was a candidate for the Distinguished Dissertation
2003.  Previously, Belavkin held the position of a research assistant
at the Department of Physics in Moscow State University, from which he
obtained his MSc degree in atmospheric physics.

Since 1998, his interests have been in AI and
cognitive modelling using both symbolic and subsymbolic approaches.
One of the main contributions of his work recently has been modelling
the effects of emotion on problem solving and decision making.  One of
the outcomes of this work was the creation of a new subsymbolic
learning algorithm for the ACT--R cognitive architecture
\cite{Belavkin-04}, which is currently used in studies of the effects
of caffeine, stress and other factors on cognitive processing in
humans.

Belavkin is also interested in neural and Bayesian learning
algorithms.  He is a member of the EPSRC funded research network on
Independent Component Analysis as well as the Society for the Study of
Artificial Intelligence and the Simulation of Behaviour (AISB).  At
Middlesex, Belavkin is a member of the AI research group and the
Interaction Design Centre.

\msubheading {Named Resarcher: \\ Dr Colm Connolly } 
has recently received his PhD from University College
Dublin. His thesis was based on a model of the visual system. This
model was divided into the ``what'' and ``where'' pathways
characteristic of mammalian visual systems. It was constructed from
laterally connected excitatory and inhibitory spiking neurons which
were trained with the SOM learning algorithm. With this model he
proposed a possible solution for a psychophysical phenomenon, known as
repetition blindness, based on the timing of waves of excitatory and
inhibitory spikes \cite{connolly05}.

He has also written a spiking neuron simulator, called Milligan, which
facilitates exploring binding in CAs by means of synchronous spiking.
The simulator can quickly process large numbers of spikes with
millisecond accuracy \cite{connolly04:_approac}.

\msubheading {Middlesex University School of Computing Science} The
School of Computing Science at Middlesex University was established in
1994.  Middlesex has over 50 research active academic staff, 50 PhD
students, and fourteen visiting research fellows including seven
international visiting professors.  The School also has staff
dedicated to website design, development, and maintenance who will
assist with the project website.

Huyck is head of the AI research group.  A large
number of faculty have expertise in neural nets (e.g. Dr Siri Bavan,
Dr Ian Mitchell and Dr Usama Hasan), sensing (Dr Carl Evans, Dr Peter
Passmore, Dr Xiaohong Gao and Dr Shaehedur Rahman), Visual
Psycho-Physics (Dr Dan Diaper), Agent Technologies (Dr Satinder Gill),
NLP (Dr Elenor Maclaren), and Robotics (Prof Anthony White).  All 
can contribute expertise to the project.



\begin{thebibliography}{}
\bibitem[\protect\citeauthoryear{Belavkin \& Ritter}{2004}]{Belavkin-04}
Belavkin, R., and Ritter, F.
\newblock 2004.
\newblock Optimist: A new conflict resolution algorithm for act-r.
\newblock In {\em Proceedings of the Sixth International Conference on
  Cognitive Modelling}.

\bibitem[\protect\citeauthoryear{Belavkin}{2001}]{Belavkin-01}
Belavkin, R.
\newblock 2001.
\newblock Modelling the inverted-U effect in ACT--R.
\newblock In Altmann, E.; Cleeremans, A.; Schunn, C.; and Gray, W., eds., {\em
  Proceedings of the 2001 Fourth International Conference on Cognitive
  Modelling}.

\bibitem[\protect\citeauthoryear{Belavkin}{2005}]{Belavkin-05}
Belavkin, R.
\newblock 2005.
\newblock Entropy and information in models of learning behaviour.
\newblock {\em AISB Quarterly} 119:5.

\bibitem[\protect\citeauthoryear{Connolly \& Reilly}{2005}]{connolly05}
Connolly, C., and Reilly, R.
\newblock 2005.
\newblock A proposed model of repetition blindness.
\newblock In {\em Modelling language, cognition and action}, volume~16. World
  Scientific.
\newblock  279--88.

\bibitem[\protect\citeauthoryear{Connolly, Marian, \& Reilly}
  {2004}]{connolly04:_approac}
Connolly, C.; Marian, I.; and Reilly, R.
\newblock 2004.
\newblock Approaches to efficient simulation with spiking neural networks.
\newblock In Bowman, H., and Labiouse, C., eds., {\em Connectionist models of
  cognition and perception {II}}. World Scientific.
\newblock  231--240.

\bibitem[\protect\citeauthoryear{Gaizauskas, Hepple, \&
  Huyck}{1998}]{Gaizauskas}
Gaizauskas, R.; Hepple, M.; and Huyck, C.
\newblock 1998.
\newblock Modifying existing annotated corpora for general comparative
  evaluation of parsing.
\newblock In {\em Workshop on Parsing Evaluation: The First International
  Conference on Language Resources and Evaluation}.

\bibitem[\protect\citeauthoryear{Huyck}{1994}]{Huyck-Plink}
Huyck, C.
\newblock 1994.
\newblock {\em Plink: An Intelligent Natural Language Parser}.
\newblock Ph.D. Dissertation, The University of Michigan.

\bibitem[\protect\citeauthoryear{Huyck}{1998}]{MUC7}
Huyck, C.
\newblock 1998.
\newblock The MUC7-plink system.
\newblock In {\em Proc. of the Seventh Message Understanding Conference
  (MUC-7)}.
\newblock Morgan Kaufmann.

\bibitem[\protect\citeauthoryear{Huyck}{2000}]{Huyck-Plink2}
Huyck, C.
\newblock 2000.
\newblock A practical system for human-like parsing.
\newblock In Horn, W., ed., {\em Proceedings of the 14th European Conference on
  Artificial Intelligence}.

\bibitem[\protect\citeauthoryear{Huyck}{2001a}]{Huyck-CogMod}
Huyck, C.
\newblock 2001a.
\newblock Cell assemblies as an intermediate level model of cognition.
\newblock In Wermter, S.; Austin, J.; and Willshaw, D., eds., {\em Emerging
  Neural Architectures based on Neuroscience}.
\newblock  383--397.

\bibitem[\protect\citeauthoryear{Huyck}{2001b}]{Huyck-CAParse}
Huyck, C.
\newblock 2001b.
\newblock Natural language parsing with cell assemblies: A model of
  nonconscious human language processing.
\newblock In {\em Proc. of the AISB'01 Symposium on Nonconscious
  Intelligence: From Natural to Artificial},  31--42.

\bibitem[\protect\citeauthoryear{Huyck}{2004}]{Huyck-Overlap}
Huyck, C.
\newblock 2004.
\newblock Overlapping cell assemblies from correlators.
\newblock {\em Neurocomputing} 56:435--9.

\bibitem[\protect\citeauthoryear{Huyck}{2005}]{Huyck-VB}
Huyck, C.
\newblock 2005.
\newblock Variable binding of cell assemblies with binding areas and
  spontaneous neural activation.
\newblock In {\em Proceedings of the 22nd Workshop of the European Society for
  the Study of Cognitive Systems}.

\bibitem[\protect\citeauthoryear{Huyck}{submitted}]{Huyck-Hierarchy}
Huyck, C.
\newblock submitted.
\newblock Creating hierarchical categories using cell assemblies.
\newblock {\em Connection Science}.

\bibitem[\protect\citeauthoryear{Huyck \& Bowles}{2004}]{Huyck-Spont}
Huyck, C., and Bowles, R.
\newblock 2004.
\newblock Spontaneous neural firing in biological and artificial neural
  systems.
\newblock {\em Journal of Cognitive Systems} 6:1:31--40.

\bibitem[\protect\citeauthoryear{Huyck \& Orengo}{2005}]{Huyck-App}
Huyck, C., and Orengo, V.
\newblock 2005.
\newblock Information retrieval and categorisation using a cell assembly
  network.
\newblock {\em Neural Computing and Applications}.

\bibitem[\protect\citeauthoryear{Kenny \& Huyck}{2005}]{Kenny}
Kenny, I., and Huyck, C.
\newblock 2005.
\newblock An embodied conversational agent for interactive videogame
  environments.
\newblock In {\em Proceedings of the AISB'05 Symposium on Conversational
  Informatics for Supporting Social Intelligence and Interaction},  58--63.

\bibitem[\protect\citeauthoryear{LeThan, Abeysinghe, \& Huyck}{2004}]{Lethan}
LeThan, H.; Abeysinghe, G.; and Huyck, C.
\newblock 2004.
\newblock Generating discourse structures for written texts.
\newblock In {\em Proceedings of 42nd Annual Meeting of the Association for
  Computational Linguistics}.

\bibitem[\protect\citeauthoryear{Lytinen \bgroup \em et al.\egroup
  }{1992}]{MUC4}
Lytinen, S.; Bhatacharyya, S.; Burridge, R.; Hastings, P.; Huyck, C.; Lipinsky,
  K.; McDaniel, E.; and Terell, K.
\newblock 1992.
\newblock The link system: {MUC-4} test results and analysis.
\newblock In {\em Proceedings of the Fourth Message Understanding Conference
  (MUC-4)},  159--63.
\newblock Morgan Kaufmann Publishers.

\bibitem[\protect\citeauthoryear{Lytinen \bgroup \em et al.\egroup
  }{1993}]{MUC5}
Lytinen, S.; Burridge, R.; Hastings, P.; and Huyck, C.
\newblock 1993.
\newblock Description of the link system used for MUC-5.
\newblock In {\em Proceedings of MUC-5},  293--304.
\newblock Morgan Kaufmann Publishers.

\end{thebibliography}
\end{document}



