Date:
Tues.,
Dec.
12,
2006
Time:
3:00
p.m.
Location:
Hagey
Hall
(HH),
room
178
Speaker:
Jean
Rouat,
(McMaster
University
visiting
professor,
Sherbrooke
University)
Title:
Towards
Neurocomputational
Models
of
Speech
and
Sound
Processing
Abstract:
From
physiology
we
learn
that
the
auditory
system
extracts
simultaneous
features
from
the
underlying
signal,
giving
birth
to
simultaneous
representations
of
audible
signals.
We
also
learn
that
pattern
analysis
and
recognition
are
not
separated
processes
(in
opposition
to
the
engineering
approach
of
pattern
recognition
where
analysis
and
recognition
are
usually
separated
processes).
Also,
in
the
visual
system,
it
has
been
observed
that
the
sequence
order
of
firing
is
crucial
to
perform
fast
visual
recognition
tasks
(Rank
Order
Coding).
The use of the Rank Order Coding has also been recently hypothesized in the mammalian auditory system. Dr. Rouat will present exploratory and potential solutions to applications in speech processing with networks of spiking neurones. In one application he will present a very simplistic speech recognition prototype that uses the Rank Order Coding scheme and will discuss its performance in comparison with a conventional Hidden Markov Model speech recognizer.
In another application, he will explain how to combine an auditory images representation with a network of oscillatory spiking neurones to segregate and bind auditory objects for acoustical source separation. It is shown that the spiking neural network performs unsupervised auditory images segmentation (to find 'auditory' objects) and binding of the objects belonging to the same auditory source (yielding automatic sound source separation). Dr. Rouat will also briefly comment on the use of the Oscillatory Dynamic Link Matcher for applications in image processing.
More
about
the
speaker:
Jean
Rouat
is
McMaster
Univ.
visiting
prof.
and
Sherbrooke
Univ.
prof.
He
holds
a
master
degree
in
physics
from
Univ.
de
Bretagne,
France
(1981),
an
E.
&
E.
master
degree
in
speech
coding
and
speech
recognition
from
Université
de
Sherbrooke
(1984)
and
an
E.
&
E.
Ph.D.
in
cognitive
and
statistical
speech
recognition
jointly
with
Université
de
Sherbrooke
and
McGill
University
(1988).
From
1988
to
2001
he
was
with
Université
du
Québec
à
Chicoutimi
(UQAC).
In
1995
and
1996,
he
was
on
a
sabbatical
leave
with
the
Medical
Research
Council,
Applied
Psychological
Unit,
Cambridge,
UK
and
the
Institute
of
Physiology,
Lausanne,
Switzerland.
In
1990
he
founded
the
ERMETIS,
Microelectronics
and
Signal
Processing
Research
Group
from
UQAC.
He
is
now
with
Université
de
Sherbrooke
where
he
founded
the
Computational
Neuroscience
and
Intelligent
Signal
Processing
Research
group.
He
regularly
acts
as
a
reviewer
for
speech,
neural
networks
and
signal
processing
journals.
He
is
an
active
member
of
scientific
associations
(Acoustical
Society
of
America,
Int.
Speech
Communication,
Institute
of
Electrical
and
Electronics
Engineers
(IEEE),
Int.
Neural
Networks
Society,
Association
for
Research
in
Otolaryngology,
etc.).
He
is
a
senior
member
of
the
IEEE
and
was
on
the
IEEE
technical
committee
on
Machine
Learning
for
Signal
Processing
from
2001
to
2005.
Date:
Wed.,
Mar.
28,
2007
Time:
3:30
p.m.
Location:
HH
334
Speaker:
Dr.
Mandar
Jog,
Associate
Professor,
University
of
Western
Ontario,
(Director,
Movement
Disorders
Program,
London
Health
Sciences
Centre
LHSC)
Title:
Information
Entropy
and
the
Tipping
Point
of
Neuronal
Networks
Abstract: Despite significant advances in the understanding of neurochemical and neuroanatomical connectivity of the brain in the healthy and disease states, we are still no farther ahead in the understanding of how the flow of information may be occurring within the system. Information theory has been applied to this system and again, in isolation has not taken the field very far. We have used a theoretical framework that has attempted to combine thermodynamic and information entropy and applied it to actual neurophysiological data from chronic multichannel recordings from rodent basal ganglia. We have found interesting and non-obvious results that extend the understanding of the learning process within the basal ganglia at a very subtle, temporo-spatial level.