NeuroThursday Spring 2003
NeuroThursday Spring 2003
10:30 AM - 12:00 Noon Thurdays in Warren Weaver Hall Room 1314
February 13:
Jack Schwartz (NYU)
Some psychophysical experiments on the perception of Glass patterns
visual motion, and the Cafe Wall illusion, with some observations on
the relationship between psychophysics and direct brain probing
February 27:
Ken Miller (UCSF)
TBA
March 13:
Souheil Inati (NYU)
The inverse problem formulation of MRI as a large scale optimization
with some example applications from neuroscience
April 3:
Carson Chow (Pittsburgh)
A spiking neuron model of binocular rivalry
April 10:
Valeria Del Prete (Kings College London)
A theoretical model for population coding
of mixed continuous and discrete stimuli:
from the linear rise to the asymptotic regime
April 24:
Christopher Pack (Harvard Medical School)
TBA
The Computational Neuroscience Forum & CIMS NeuroThursday Joint WebPage
Abstracts
Valeria Del Prete(Kings College London)
A theoretical model for population coding
of mixed continuous and discrete stimuli:
from the linear rise to the asymptotic regime
In real experiments animals are exposed to a discrete set of stimuli
or trained to perform a discrete set of tasks. Yet, natural stimuli are
multi-dimensional and can be parameterized by a mixture of continuous and
discrete dimensions. For example an arm movement can be categorized
according to its (discrete) "type"- e.g. bimanual or unimanual- and its
(continuous) direction. Inspired by data recorded by E. Vaadia from the
SMA and M1 areas of monkeys performing such arm movements, I have
developed a theoretical model of a feed-forward network of
threshold-linear neurons to investigate the information transmitted
between the two areas in presence of continuous+discrete input patterns. I
have studied analytically the information carried by the population in the
first layer in the network with respect to the complex stimuli, showing
that in the regime of the initial information rise as a function of
the population size, there is no difference in using a more realistic
model of the firing or a simple gaussian model with respect to the fit
of real data: the analytical expression is the same except for a
renormalization of the noise. On the other hand an analytical evaluation
of the information in the asymptotic regime reveals a more complicated
relationship between the two models. Introduction of correlations in the
neurons' preferred orientations (correlations actually observed in the
data) does improve the fit suggesting that the correlations detected in
the data are information bearing. Then I have evaluated the information
carried by the output layer in the limit of a large number of input units,
showing that, in absence of learning in the connectivities, the presence
of the output noise and of a threshold are two simple mechanisms which
cause an information loss in output.
Carson Chow (Pittsburgh)
A spiking neuron model of binocular rivalry
Binocular rivalry occurs when the two eyes are presented
with drastically different images. Only one of the images is
perceived at a given time, and every few seconds there is alternation
between the perceived images. The perceived durations of the images
are stochastic and uncorrelated with previous perceived durations.
Recordings from single neurons in the visual cortex of monkeys
experiencing binocular rivalry find that neuronal activity is
correlated with the monkey's perception. I will present a
biologically plausible network of Hodgkin-Huxley type neurons that
exhibits rivalry and can account for the experimental and
psychophysical results. A reduced population rate model can be
derived from the spiking model from which a simple explanation of the
model dynamics can be obtained.
A Recap of NeuroMonday Fall 2002
Louis Tao,
My CIMS web page,
Applied Mathematics Laboratory &
CNS,
New York, NY 10012. This page last updated March 2003.