Colloquium Series 2019-2019
Colloquia are generally on Tuesdays at 3:30 p.m., once per month. They are usually held in E5-6111 (exceptions will be noted). Abstracts are posted as available. If you'd like to be on the mailing list announcing these events, please sign up here.
Here is a list of our upcoming speakers for the 2019 and 2020 academic year:
September 17, 2019 - Mayzar Fallah
October 8, 2019 - Wilten Nicola
November 20, 2019 - Xaq Pitkow
November 26, 2019 - Morris Moscovitch
January 25, 2020 - Randy McIntosh
Tuesday, September
27,
2019
-
3:30
pm
EC5,
2004
Mayzar
Fallah
York
University
Cortical Interactions in the Oculomotor System
Abstract:
Previous
neurophysiological
studies
have
demonstrated
that
saccade
curvatures
are
the
result
of
excitatory
and
competitive
interactions
between
potential
saccade
goals
in
the
intermediate
layers
of
the
superior
colliculus
(SCi)
and
frontal
eye
field
(FEF),
whereby
the
resulting
saccade
curvature
is
proportional
to
the
level
of
unresolved
activity
encoding
a
competing
saccade
vector.
This
suggests
that
the
magnitudes
of
saccade
curvature
vary
continuously
along
a
gradient
of
oculomotor
excitation
and
inhibition.
Given
that
top-down
factors
like
task
relevance
are
encoded
by
the
visuomotor
neurons
of
the
oculomotor
system
(reviewed
by
Fecteau
&
Munoz,
2006),
this
predicts
a
functional
relationship
between
saccade
curvature
and
cortical
visual
processing.
I
will
be
presenting
a
series
of
studies
that
investigates
the
featural
and
temporal
factors
affecting
saccadic
encoding,
as
measured
by
saccade
curvatures.
These
studies
provide
increasing
evidence
that
visual
processing
can
be
read
out
of
saccade
metrics.
Tuesday,
October
8,
2019
-
3:30pm
E5
-
2004
Wilten
Nicola
University
of
Calgary
Fast, Compressible Learning in the Hippocampus using Interneuron Sequences
Abstract:
The
hippocampus
is
able
to
rapidly
learn
incoming
information,
even
if
that
information
is
only
observed
once.
Furthermore,
this
information
can
be
replayed
in
a
compressed
format
in
either
forward
or
reverse
modes
during
sharp
wave–ripples
(SPW–Rs).
We
leveraged
state-of-the-art
techniques
in
training
recurrent
spiking
networks
to
demonstrate
how
primarily
interneuron
networks
can
achieve
the
following:
(1)
generate
internal
theta
sequences
to
bind
externally
elicited
spikes
in
the
presence
of
inhibition
from
the
medial
septum;
(2)
compress
learned
spike
sequences
in
the
form
of
a
SPW–R
when
septal
inhibition
is
removed;
(3)
generate
and
refine
high-frequency
assemblies
during
SPW–R-mediated
compression;
and
(4)
regulate
the
inter-SPW
interval
timing
between
SPW–Rs
in
ripple
clusters.
From
the
fast
timescale
of
neurons
to
the
slow
timescale
of
behaviors,
interneuron
networks
serve
as
the
scaffolding
for
one-shot
learning
by
replaying,
reversing,
refining,
and
regulating
spike
sequences.
Wednesday,
November
20,
2019
-
3:30
pm
E7,
7363
Xaq
Pitkow
Rice
University
Rational Thoughts in Neural Codes
Abstract:
Complex
behaviors
are
often
driven
by
an
internal
model,
which
integrates
sensory
information
over
time
and
facilitates
long-term
planning
to
reach
subjective
goals.
We
interpret
behavioral
data
by
assuming
an
agent
behaves
rationally
—
that
is,
they
take
actions
that
optimize
their
subjective
reward
according
to
their
understanding
of
the
task
and
its
relevant
causal
variables.
We
apply
a
new
method,
Inverse
Rational
Control
(IRC),
to
learn
an
agent’s
internal
model
and
reward
function
by
maximizing
the
likelihood
of
its
measured
sensory
observations
and
actions.
This
thereby
extracts
rational
and
interpretable
thoughts
of
the
agent
from
its
behavior.
We
also
provide
a
framework
for
interpreting
encoding,
recoding
and
decoding
of
neural
data
in
light
of
this
rational
model
for
behavior.
When
applied
to
behavioral
and
neural
data
from
simulated
agents
performing
suboptimally
on
a
naturalistic
foraging
task,
this
method
successfully
recovers
their
internal
model
and
reward
function,
as
well
as
the
computational
dynamics
within
the
neural
manifold
that
represents
the
task.
This
work
lays
a
foundation
for
discovering
how
the
brain
represents
and
computes
with
dynamic
beliefs.
Tuesday, November 26, 2019 - 3:30 pm
Morris
Moscovitch
University
of
Toronto
The Cognitive Neuroscience of Recent Remote Event and Spatial Memory
Abstract:
Memories
are
dynamic
and
interactive.
Their
representations
are
influenced
by
time
and
experience.
I
will
present
evidence
on
the
nature
of
the
changing
representations
and
their
neural
correlates,
show
how
event
and
spatial
memory
interact
with
one
another,
and
propose
a
neurocognitive
model
of
hippocampal-neocortical
interactions
that
may
account
for
the
evidence.
Tuesday, January 21, 2020 - 3:30 pm
Randy
McIntosh
Baycrest
Centre
Flow and Manifolds in Cognition and Neural Networks
Abstract:
Our
experience
is
elaborate,
where
our
perceptions
are
embellished
by
memories
and
emotions,
and
driven
by
predictions.
We
have
developed
a
quantitative
framework
that
makes
the
explicit
link
between
the
elaborate
temporal
evolution
of
the
brain
networks
and
the
accompanying
evolution
of
the
mental
streams.
We
posit
that
the
coordination
underlying
experience
can
be
understood
by
considering
neural
processes
as
flows
depicting
system
interactions.
The
flows
occur
on
relatively
low-dimensional
manifolds,
which
constrain
the
landscape
of
possible
functional
configurations
–
Structured
Flows
on
Manifolds
(SFM).
The
attraction
of
the
SFM
framework
is
that
the
same
mathematical
formulation
can
be
used
to
quantify
the
flows
and
manifolds
for
the
cognitive
architecture
as
for
the
neural
dynamics.
The
potential
for
new
configurations
reflects
the
adaptive
nature
of
the
brain
and
higher
cognitive
function.
This
“hidden
repertoire”
is
at
the
heart
of
what
makes
our
experiences
special,
where
the
richness
comes
precisely
because
of
what
is
happening
and
also
of
what
possibly
could
happen.