postdoctoral position

[John Fletcher]

                          UNIVERSITY OF OXFORD
               MRC RESEARCH CENTRE IN BRAIN AND BEHAVIOUR

                Scientist for Neural Network Applications


Applications are invited for a postdoctoral position to  work  on
the  operation  of  neuronal  networks in the brain, with special
reference to cortical computation.  The post available is  for  a
theoretician  to  perform  analytic and/or simulation work colla-
boratively  with  experimental  neuroscientists  on  biologically
realistic  models  of computation in the brain, including systems
involved in vision, memory and/or motor function.   The  appoint-
ment will be for three years in the first instance.

The salary is on the RSIA scale, GBP 12,638-20,140, and is funded
by the Medical Research Council.

Further particulars are attached.   Applications,  including  the
names of two referees, to The Administrator, Department of Exper-
imental Psychology, South Parks Road, Oxford OX1 3UD.

The University is an Equal Opportunities Employer.

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UNIVERSITY OF OXFORD MRC RESEARCH CENTRE IN BRAIN AND BEHAVIOUR

Neural Network Scientist (RSIA Scale)


The purpose of this post is to enable a formal modeller  of  neu-
ronal  networks  to work on a daily basis with neuroscientists in
order to develop models of cortical function, and to provide  the
neuroscientists in the MRC Research Centre in Brain and Behaviour
with advice and assistance in relation to the expertise  required
for analytic models of neuronal networks.

The postholder should have a PhD or  equivalent  experience,  to-
gether  with  evidence  that  he/she can initiate a research pro-
gramme.  The postholder is expected  to  have  expertise  in  the
mathematical  analyses of neuronal networks.  He/she will provide
mathematical and related computational expertise required for the
modelling  of  real  neuronal networks, the architecture of which
will be based on anatomical information  about  neuronal  connec-
tions  and  physiological  information about their modifiability.
He/she will probably have the opportunity to  supervise  graduate
students reading for the DPhil degree.

In addition, the postholder should also have expertise useful  in
enabling  him  or  her to analyse biologically relevant models of
neural networks.  This latter requirement implies either existing
expertise  in  neuroscience, or an aptitude for working with neu-
roscientists to ensure that biological constraints are  correctly
incorporated into network models.

The postholder will be sufficiently senior to be able to initiate
independent  analyses  of  neural  networks,  and  to ensure that
results are brought to publication.

Since an important part of the Research Centre is  training,  the
postholder  will  be expected to take part in the organization of
seminar series on neural networks.  It is anticipated that  these
seminars will be held at least annually and will provide training
for both doctoral students and for other more senior  members  of
the  University  interested in research on neural networks. Other
teaching may include participation in a summer school.

Finally, the postholder, who will be responsible to the Directors
of  the  Research  Centre, will be expected to serve on a Working
Group of the Research Centre interested in computational analyses
of neuronal networks.

Examples of neural network projects in the  Research  Centre  in-
clude the following:

Dr J F Stein/Dr R C Miall, University Laboratory of Physiology:

(a)To reconsider the Zipser & Anderson  network  model  of  head-
centred  encoding of target positions by visual cells in the pos-
terior parietal cortex.   Their  model  was  very  successful  at
demonstrating  how  the  hidden  units  within  a  standard back-
propagation neural network could end up with receptive field pro-
perties  similar  to  those recorded in the PPC in awake monkeys.
However, as the responses (in both monkey and model) were complex
and hard to classify, it remains unclear whether there was a true
similarity between the two or whether the solutions  were  merely
equally  hard to interpret.  We plan to re-examine this model us-
ing more realistic constraints on the model connectivity  and  on
the  encoding  of its inputs, and should be taking on a new post-
graduate student this October to pursue these questions.

(b)To develop models of learning at the cerebellar parallel-fibre
to  Purkinje cell synapse.  We have proposed a model of the cere-
bellum in which the cortex learns to form a forward model of  the
limb dynamics to assist in visually guided arm movement.  We have
proposed that there  is  a  mechanism  related  to  reinforcement
learning, but the details remain unclear.  It is important to see
whether the biophysics of long-term depression and the  ideas  of
reinforcement  learning  can be tied together in a neural simula-
tion.

References:

Barto AG, Sutton RS & Anderson CW  (1983).   Neuronlike  adaptive
elements  that  can  solve  difficult  learning control problems.
IEEE Sys Man Cyb., 13: 834-846.

Ito M (1989).  Long-term depression.  Ann. Rev. Neurosci.  12:85-
102.

Miall RC, Weir DJ, Wolpert DM & Stein JF (1993).  Is the cerebel-
lum a Smith Predictor?  J.motor Behaviour (in press).

Zipser D & Andersen RA  (1988).   A  back-propagation  programmed
network  that simulates response properties of a subset of poste-
rior parietal neurons.  Nature 331:679-684.


Dr K Plunkett, Department of Experimental Psychology:

Kim Plunkett's research group is involved  in  computational  and
experimental investigations of language acquisition and cognitive
development.  Modelling work has  covered  connectionist  simula-
tions  of  inflectional morphology, concept formation, vocabulary
development and early syntax.  Experimental work focuses  on  the
processes of lexical segmentation in early language acquisition.

References:

Plunkett K & Marcham V (1991). U-shaped  learning  and  frequency
effects  in  a  multi-layered  perceptron: Implications for child
language acquisition.  Cognition, 38, 43-102.

Plunkett K & Sinha CG (1992).   Connectionism  and  developmental
theory.   British  Journal  of Developmental Psychology, 10, 209-
254.

Plunkett K (1993).  Lexical segmentation and vocabulary growth in
early language acquisition. Journal of Child Language, 20, 43-60.


Professor D Sherrington, Department of Theoretical Physics:

Our interest is in understanding and quantifying the design, per-
formance  and  training  of  neural network models, stimulated by
their potential as cartoons of parts of the brain, as expert sys-
tems  and  as  complex  cooperative systems.  Our methodology in-
volves the application of analytical and computational techniques
from  the theoretical physics of strongly interacting systems and
centres largely around issues of statistical  relevance,  as  op-
posed to worst-case or special-case analyses.

References:

Recent reviews:

*Sherrington D.  Neural Networks:  the spinglass approach.  OUTP-
92-485.

*Coolen T & Sherrington D.  Dynamics  of  attractor  neural  net-
works.  OUTP-92-495.

[The above are to be published in Mathematical Studies of  Neural
Networks (Elsevier); ed. JG Taylor.]

Watkin TLH, Rau A & Biehl M.  Statistical Mechanics of Learning a
Rule.   OUTP-92-45S published in Reviews of Modern Physics 65, pp
499-556 (1993).

*Copies of these are available for interested candidates.

Published research articles (selection)

Wong KYM, Kahn PE & Sherrington D.  A  neural  network  model  of
working  memory  exhibiting  primacy  and recency.  J. Phys. A24,
1119 (1991).

Sherrington D, Wong M & Rau A.  Good  Memories.   Phil  Mag.  65,
1303 (1992).

Sherrington D, Wong KYM & Coolen ACC.  Noise and  competition  in
neural networks.  J.Phys I France 3, 331 (1993).

O'Kane D & Sherrington D.  A feature-retrieving attractor  neural
network.  J.Phys A 26, 2333 (1993).


Dr E T Rolls, Department of Experimental Psychology:

(a)   Learning invariant responses to the natural transformations
of  objects.  The primate visual system builds representations of
objects which are invariant with respect to  transforms  such  as
translation, size, and eventually view, in a series of hierarchi-
cal cortical areas.  To clarify how such a system might learn  to
recognise  "naturally"  transformed objects, we are investigating
a model of cortical visual processing which incorporates a number
of features of the primate visual system.  The model has a series
of layers with convergence from a limited region of the preceding
layer,  and  mutual inhibition over a short range within a layer.
The feedforward connections between layers provide the inputs  to
competitive  networks, each utilising a modified Hebb-like learn-
ing rule which incorporates a temporal  trace  of  the  preceding
neuronal  activity.  The trace learning rule is aimed at enabling
the neurons to learn transform invariant responses via experience
of the real world, with its inherent spatio-temporal constraints.
We are showing that the model can learn  to  produce  translation
invariant  responses,  and  plan  to  develop this neural network
model to investigate its performance in learning other  types  of
invariant representation, and its capacity.

      Rolls E (1992).  Neurophysiological  mechanisms  underlying
face  processing  within  and beyond the temporal cortical areas.
Phil. Trans. Roy. Society London Ser B 335, 11-21.

      Wallis G, Rolls E & Foldiak P (1993).  In: Proc  Int  Joint
Conference on Neural Networks.

(b)   Neural Networks in the Hippocampus involved in  Memory  and
Recall.   We are developing a model based on hippocampal anatomy,
physiology, and psychology, of how the  neural  networks  in  the
hippocampus  could  operate  in memory.  A key hypothesis is that
the hippocampal CA3 circuitry forms an autoassociation memory. We
are  developing  a  quantitative  theory  of  how  the  CA3 could
operate, and of how it would function in relation to other  parts
of  the hippocampus.  We are also starting to develop a theory of
how the hippocampus could recall memories in the cerebral neocor-
tex using backprojections in the cerebral cortex.

      Rolls ET (1989a).  Functions of neuronal  networks  in  the
hippocampus and neocortex in memory.  In Neural models of plasti-
city: Experimental and theoretical approaches (ed. JH Byrne &  WO
Berry), 13, 240-265).  San Diego: Academic Press.

      Rolls  ET  (1990a).   Theoretical  and   neurophysiological
analysis  of  the functions of the primate hippocampus in memory.
Cold Spring Harbor Symposia in Quantitative Biology 55, 995-1006.

      Treves A & Rolls ET (1991).  What determines  the  capacity
of autoassociative memories in the brain?  Network 2: 371-397.

      Treves A & Rolls ET (1992).  Computational constraints sug-
gest  the  need for two distinct input systems to the hippocampal
CA3 network.  Hippocampus 2: 189-199.

      Rolls ET & Treves A (1993).  Neural Networks in  the  Brain
involved  in  Memory and Recall.  In: Proc. Int. Joint Conference
on Neural Networks.

We would welcome collaboration on either or both projects.