2 papers: Hippocampus & navigation, generalisation of constructive alg.

[Dr Neil Burgess]

I have just put two pre-prints in neuroprose (see below for abstracts and
ftp instructions).
Cheers 
Neil (n.burgess at ucl.ac.uk)

_________________________________________________________________________

	USING HIPPOCAMPAL `PLACE CELLS' FOR NAVIGATION,
		EXPLOITING PHASE CODING

	Neil Burgess, John O'Keefe and Michael Recce
		Department of Anatomy,
		University College London
		London WC1E 6BT, England.


			ABSTRACT
A model of the hippocampus as a central element in rat navigation 
is presented. Simulations show both the behaviour of single cells 
and the resultant navigation of the rat. These are compared with 
single unit recordings and behavioural data. The firing of CA1 place 
cells is simulated as the (artificial) rat moves in an environment.
This is the input for a neuronal network whose output, at each theta 
$(\theta)$ cycle, is the next direction of travel for the rat. Cells 
are characterised by the number of spikes fired and the time of 
firing with respect to hippocampal $\theta$ rhythm. `Learning' occurs
in `on-off' synapses that are switched on by simultaneous pre- and 
post-synaptic activity. The simulated rat navigates successfully to 
goals encountered one or more times during exploration in open fields.
One minute of random exploration of a $1m^2$ environment allows
navigation to a newly-presented goal from novel starting positions.
A limited number of obstacles can be successfully avoided.
_________________________________________________________________________

This paper will be published in NIPS 5. To get the postscript file do:

unix> ftp cheops.cis.ohio-state.edu
Name (cheops.cis.ohio-state.edu:userid): anonymous
Password: (use your email address)
ftp> cd pub/neuroprose
ftp> binary
ftp> get burgess.hipnav.ps.Z
200 PORT command successful.
150 Opening BINARY mode data connection for burgess.hipnav.ps.Z
.
ftp> quit
221 Goodbye.
unix> uncompress  burgess.hipnav.ps.Z
unix> lpr burgess.hipnav.ps	(or whatever you do to print)

The uncompressed file is 1.7 Mbytes and may take sometime to print. 

_________________________________________________________________________

	THE GENERALIZATION OF A CONSTRUCTIVE ALGORITHM
	      IN PATTERN CLASSIFICATION PROBLEMS

Neil Burgess,			Silvano Di Zenzo, Paolo Ferragina and
					 Mario Notturno Granieri	
Department of Anatomy		IBM Rome Scientific Center
University College London	Viale Oceano Pacifico 171
London WC1E 6BT, ENGLAND.	00144 Rome, Italy


			ABSTRACT
The use of a constructive algorithm for pattern classification is
examined. The algorithm, a `Perceptron Cascade', has been shown to
converge to zero errors whilst learning any consistent classification
of {\it real-valued} pattern vectors (Burgess, 1992). Limiting network
size and producing bounded decision regions are noted to be important for
the generalization ability of a network. A scheme is suggested by which
a result on generalization (Vapnik, 1992) may enable calculation of the
optimal network size. A fast algorithm for principal component analysis 
(Sirat, 1991) is used to construct `hyper-boxes' around each class of 
patterns to ensure bounded decision regions. Performance is compared with 
the Gaussian Maximum Likelihood procedure in three artificial problems 
simulating real pattern classification applications.

N. Burgess, submitted to International Journal of Neural Systems (1992).
J. A. Sirat, International Journal of Neural Systems, 2, 147-155 (1991).
V. Vapnik, NIPS 4, 838-838, Morgan Kaufmann (1992).
_________________________________________________________________________


This paper will be published in:
International Journal of Neural Systems 3 (Supp. 1992); 
Proceedings of the Neural Networks: from Biology to High 
Energy Physics Workshop.

The postscript file is in  burgess.gencon.ps.Z, follow the 
above instructions to retrieve it (again, page 5 may take sometime
to print as it contains a 1 Mbyte bitmap).