[info] [Comp-neuro] PhD thesis available: predictive sequence learning with spiking neurons using rank order coding

[Eugen Leitl]

----- Forwarded message from Joy Bose <joyboseroy at gmail.com> -----

From: Joy Bose <joyboseroy at gmail.com>
Date: Fri, 6 Jul 2007 13:42:55 +0100
To: connectionists at cs.cmu.edu, comp-neuro at neuroinf.org
Cc: 
Subject: [Comp-neuro] PhD thesis available: predictive sequence learning
	with spiking neurons using rank order coding


   Dear connectionists,
   This is to announce that my PhD dissertation on the topic of sequence
   learning is available for download.
   Title: Engineering a sequence machine out of spiking neurons employing
   rank order codes
   Download URL: [1]http://www.cs.man.ac.uk/~bosej/JoyBose_PhD.pdf (216
   pages, 5.23 MB)
   With regards,
   Joy Bose
   APT Research Group
   Computer Science
   The University of Manchester
   Email: [2]bosejATcs.man.ac.uk, joyboseroyATgmail.com
   ----------------------------------------------------------------------
   ----------------------------------------------------------------------
   ------------------------------
   Abstract:
   Sequence memories play an important role in biological systems. For
   example, the mammalian brain continuously processes, learns and
   predicts spatio-temporal sequences of sensory inputs. The work
   described in this dissertation demonstrates how a sequence memory may
   be built from biologically plausible spiking neural components. The
   memory is incorporated in a sequence machine, an automaton that can
   perform on-line learning and prediction of sequences of symbols.
   The sequence machine comprises an associative memory which is a
   variant of Pentti Kanerva's Sparse Distributed Memory, together with a
   separate memory for storing the sequence context or history. The
   associative memory has at its core a scalable correlation matrix
   memory employing a localised learning rule which can be implemented
   with spiking neurons.
   The symbols constituting a sequence are encoded as rank-ordered N-of-M
   codes, each code being implemented as a burst of spikes emitted by a
   layer of neurons. When appropriate neural structures are used the
   spike bursts maintain coherence and stability as they pass through
   successive neural layers. The system is modelled using a
   representation of order that abstracts time, and the abstracted system
   is shown to perform equivalently to a low-level spiking neural system.
   The spiking neural implementation of the  sequence memory
   model highlights issues that arise when engineering high-level systems
   with asynchronous spiking neurons as building blocks.
   Finally, the sequence learning framework is used to simulate different
   sequence machine models. The new model proposed here is tested under
   varied parameters to characterise its performance in terms of the
   accuracy of its sequence predictions.

References

   1. http://www.cs.man.ac.uk/~bosej/JoyBose_PhD.pdf
   2. http://bosejATcs.man.ac.uk/

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----- End forwarded message -----
-- 
Eugen* Leitl <a href="http://leitl.org">leitl</a> http://leitl.org
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