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Period of Concentration Abstracts and Lecture Materials:
Author: Charles
Anderson, Washington Univ. School of Medicine
Title: Neural Engineering
Charles H. Anderson
Dept. of Anatomy and Neurobiology
Washington University School of Medicine
St. Louis, MO 63110
cha@shifter.wustl.edu
http://compneuro.uwaterloo.ca/
Abstracts: PDF
Presentation Materials: PPT1
PDF1 PPT2
PDF2 PPT3
PDF3
Author: Peter
Foldiak , University of St. Andrews
Title: Stimulus selection for the eyxperimental study of high-level
cortex 2/18/03
Streaming Video: Real
Media
The study of the selective properties of single cells in the sensory
system are essential for our understanding of the representation of
information in the brain. Conventional methods of describing neural
selectivity are severely limited, especially in higher levels of cortical
processing. New computational techniques and novel experimental approaches
may help solve these problems. I will present the rapid serial visual
presentation method as a technique for single-cell neurophysiological
experiments, and show that its results can be used to extend the range
of tested stimuli. These methods, together with closed loop selection
procedures, can give a significantly more objective description of
selectivity.
Author: Peter
Foldiak , University of St. Andrews
Title: Stimulus optimization 2/24/03
Author: John
Hertz , Nordita, Copenhagen, Denmark
Title: Response variability in balanced cortical network models
("Chalk Talk")
Presentation Materials: PPT
PDF
[work with Barry Richmond (LN-NIMH), Pauline Ruffiot (Nordita and
Univ Joseph Fourier, Grenoble) Cristina Ursta (Nordita, Niels Bohr
Institute, and West Univ, Timisoara), Gustaf Sterner (KTH Stockholm),
Mandana Ahmadi (Ahvaz Univ, Iran) and Alexander Lerchner (DTU)]
The observed spike count distributions of V1 neurons are non-Poissonian:
The variance generally exceeds the mean, and the variance-vs-mean
relation is well-fit by a power law with an exponent greater than
1. In this work we find that the spike statistics of neurons in
a model network with dynamically balanced excitation and inhibition
show the same features. Our model, intended to represent a generic
cortical column, comprises randomly connected excitatory and inhibitory
leaky integrate-and-fire neurons driven by excitatory input from
a large population of neurons external to the model. We take this
input to vary in time like typical thalamic input to cortex. The
synaptic strengths are chosen to produce asynchronous irregular
firing at rates up to 200 Hz, depending on the strength of the input.
Random variability among neurons in both firing thresholds and the
strengths of external input currents is also included. The high
degree of connectivity permits a mean-field description in which
all input currents, both external and recurrent, can be treated
as Gaussian noise, the mean and autocorrelation function of which
are calculated self-consistently from the firing statistics of single
model neurons.
I will report on two problems under current study: (1) Balanced
networks with conductance-based synapses. Here the firing statistics
are controlled by the synaptic dynamics. (2) A balanced net model
for a visual cortical hypercolumn. The firing statistics vary systematically
with orientation: The Fano factor is largest at orientations away
from the optimal one.
1. Hertz,, J.A., Richmond, B.J., & Nilsen, K. (2002). Anomalous
Response Variation in a Balanced Cortical Network. CNS, forthcoming.
(Currently available at http://www.nordita.dk/~hertz/CNS02_Hertz.pdf)
2. Hertz, J.A., Richmond, B.J., Ruffiot, P., & Ursta, C. (2002).
Neurons in model balanced networks have firing statistics like V1
neurons: Program 558.14. In Society for Neuroscience, eds., Abstract
viewer/itinerary planner. Washington, DC: Society for Neurscience,
online.
Author: Barry
Richmond, Laboratory of Neuropsychology, National Institute
of Mental Health
Title:Decoding spike trains instant-by-instant.
Streaming Video: Real
Media
Talk 1:
Expecting what you work for: behavioral, neurophysiological and
molecular studies into motivation and reward expectancy. Barry J.
Richmond, Section on Neural Coding and Computation, Nat'l Inst.
of Mental Health, Nat'l Institutes of Health, Dept. of Health and
Human Services.
Evaluating the balance between the amount of work that needs to
be done to obtain a reward or achieve a goal is critical step in
normal behavior. We study this critical step in behavior by observing
how monkeys make use of visual stimuli to predict how many trials
of a simple operant task need to be performed to obtain a reward.
In line with many long know observations, the number of errors made
increase as the visual stimulus indicates that there are more trials
remaining, e.g. there are more errors when 3 trials remain than
when 2 trials remain. This show that the monkeys have an internal
working model of this simple reward schedule task. Recording single
neurons in areas of the brain related to dopamine, a transmitter
known to play an important role in reward-seeking behavior, we find
signals in several brain regions related to the path through the
task and the expectancy of the reward. Finally, borrowing techniques
from molecular biology we are able to inactivate dopaming 2 (D2)
receptor in one region of cortex and completely block learning of
the association of visual stimuli with reward expectancy. There
are many aspects of this task and our findings that would benefit
from theoretical work.
Talk 2:
The coordination of activity across neurons: are the messages redundant?
Barry J. Richmond, Section on Neural Coding and Computation, Nat'l
Inst. of Mental Health, Nat'l Institutes of Health, Dept. of Health
and Human Services.
It is clear from informational measurements of single neurons in
the visual system that single neurons carrying relatively small
amounts of information about the outside world. The responses are
variable, meaning that they appear noisy. Thus, for the brain to
work as reliably as it does, the brain must pool the responses of
many neurons. Is it likely that this is done through simple averaging,
or is there evidence that the brain might choose to combine the
responses in some other way? I will present showing that neurons
recorded simultaneously only share a relatively small amount of
information, suggesting that combining signals would vastly underestimate
the capabilities of neuronal populations for processing information.
Author: Mandyam
V. Srinivasan, Centre for Visual Sciences, Research School of
Biological Sciences, Australian National University
Web: http://biology.anu.edu.au/rsbsweb/profiles/srini.shtml
Title: From Flying Insects to Autonomous Robots
Anyone who has watched a fly make a flawless landing on the rim
of a teacup, or marvelled at a honeybee speeding home after collecting
nectar from a flower patch several kilometres away, would know that
insects possess visual systems that are fast, reliable and accurate.
Insects cope remarkably well with their world, despite possessing
a brain that carries fewer than 0.01% as many neurons as ours does.
What are the secrets of their success, and can some of these navigational
principles be usefully implemented in robots?
Although most insects lack stereo vision, they use a number of ingenious
strategies for perceiving their world in three dimensions and navigating
successfully in it. For example, distances to objects are gauged
in terms of the apparent speeds of motion of the objects' images,
rather than by using complex stereo mechanisms [1]. Objects are
distinguished from backgrounds by sensing the apparent relative
motion at the boundary [2]. Narrow gaps are negotiated by balancing
the apparent speeds of the images in the two eyes [3]. Flight speed
is regulated by holding constant the average image velocity as seen
by both eyes [4]. Roll is stabilised by balancing the output signals
from the two lateral ocelli, which function as horizon sensors [5,6].
Bees landing on a horizontal surface hold constant the image velocity
of the surface as they approach it, thus automatically ensuring
that flight speed is close to zero at touchdown [7]. Foraging bees
gauge distance flown by integrating optic flow: they possess a visually-driven
"odometer" that is robust to variations in wind, body
weight and energy expenditure [8-10]. This presentation will review
some of this work, and outline applications of some of these strategies
to the design of autonomous robots and flying vehicles [11-15].
1. Lehrer, M., Srinivasan, M.V., Zhang, S.W., & Horridge, G.A.
(1988). Nature, Lond., 332, 356-357.
2. Srinivasan, M.V., Lehrer, M., & Horridge, G.A. (1990). Proc.
R. Soc. Lond. B., 238, 331-350.
3. Srinivasan, M.V., Lehrer, M., Kirchner, W.H., & Zhang, S.W.
(1991). Vis. Neurosci., 6, 519-535.
4. Srinivasan, M.V., Zhang, S.W., Lehrer M., & Collett, T.S.
(1996) J. Exp. Biol,. 199, 237-244.
5. Stange, G., & Howard, J. (1979). J. Exp. Biol., 83,
351-355.
6. Stange, G.F., Stowe, S., Chahl, J.S., & Massaro, A., J. (2002).
Comp. Physiol A, 188, 455-467.
7. Srinivasan, M.V., Zhang, S.W., Chahl, J.S., Barth, E., &
Venkatesh, S. (2000). Biol. Cybernetics, 83, 171-183.
8. Srinivasan, M.V., Zhang, S.W., & Bidwell, N. (1997). J.
Exp. Biol, 200, 2513-2522.
9. Srinivasan, M.V., Zhang, S.W., Altwein, M., & Tautz, J. (2000).
Science, 287, 851 - 853.
10. Esch, H., Zhang, S.W., Srinivasan, M.V., & Tautz, J. (2001).
Nature, 411, 581-583.
11. Srinivasan, M.V., & Venkatesh, S. (Eds.). (1997). From
living eyes to seeing machines. U.K.: Oxford University Press.
12. Srinivasan, M.V., Chahl, J.S., Weber, K., Venkatesh, S., Nagle,
M.G., & Zhang S.W. (1999). Robotics and Autonomous Systems,
26, 203-216.
13. Chahl, J.S., & Srinivasan, M.W. (1999). Proceedings of the
Field and Service Robotics Conference. Pittsburgh, PA, pp. 127-132.
14. Srinivasan, M.V. (2002). Visual flight control and navigation
in honeybees, and applications to robotics. In J. Ayers, J.L. Davis,
& A. Rudolph, eds., Neurotechnology for biomimetic robots.
Cambridge, MA: MIT Press, pp. 593 - 610.
15. Chahl, J., Thakoor, S., Bouffant, N. L., Stange, G., Srinivasan,
M.V., Hine, B., et al. (in press). Journal of Robotic Systems.
Author: Mandyam
V. Srinivasan, Centre for Visual Sciences, Research School of
Biological Sciences, Australian National University
Web: http://biology.anu.edu.au/rsbsweb/profiles/srini.shtml
Title: Small Brains, Smart Minds: Vision, perception and 'cognition'
in honeybees
Recent work is beginning to reveal that insects may not be the simple,
reflexive creatures that they were once assumed to be. Honeybees,
for example, can learn rather general features of flowers and landmarks,
such as colour, orientation and symmetry, and apply them to distinguish
between objects that they have never previously encountered [1-5].
Bees exhibit "top-down" processing: that is, they are capable
of using prior knowledge to detect poorly visible or camouflaged objects
[6]. Furthermore, bees can learn to navigate through labyrinths [7-10],
to form complex associations [11, 12] and to acquire abstract concepts
such as "sameness" and "difference" [13]. All
of these observations suggest that there is no hard dichotomy between
invertebrates and vertebrates in the context of perception, learning
and 'cognition'; and that brain size is not necessarily a reliable
predictor of perceptual capacity. This presentation will review some
of the perceptual capacities of honeybees and speculate, where possible,
on underlying mechanisms.
1. van Hateren, J.H., Srinivasan, M.V., & Wait, P.B. (1990).
J. Comp. Physiol., 167, 649-654.
2. Srinivasan, M.V., Zhang, S.W., & Witney, K. (1994). Phil.
Trans. R. Soc. Lond. B., 343, 199-210.
3. Srinivasan, M.V. (1994). J. Insect Physiol. 40, 183-194.
4. Horridge, G. A.. (1996). J. Insect Physiol., 42 (8), 755-764.
5. Giurfa, M., Eichmann, B., & Menzel, R. (1996). Symmetry perception
in an insect. Nature, 382, 458-461.
6. Zhang, S.W., & Srinivasan, M.V. (1994). Nature (Lond.)
368, 330-332.
7. Zhang, S.W., Bartsch, K., & Srinivasan, M.V. (1996). Neurobiology
of Learning and Memory, 66, 267-282.
8. Zhang, S.W., Lehrer, M., & Srinivasan, M.V. (1998). J.
Comp. Physiol. A., 182, 747-754.
9. Srinivasan, M.V., & Zhang, S.W. (1998). Zoology: Analysis
of Complex Systems, 101, 246-259.
10. Zhang, S.W., Mizutani, A., & Srinivasan, M.V. (2000). Learning
and Memory, 7, 363-374.
11. Srinivasan, M.V., Zhang, S.W., & Zhu, H. (1998). Nature
(Lond.), 396, 637-638.
12. Zhang, S.W., Lehrer, M., & Srinivasan, M.V. (1999). Neurobiology
of Learning and Memory, 72, 180-201.
13. Giurfa, M., Zhang, S.W., Jenett, A., Menzel, R., & Srinivasan,
M.V. (2001). Nature, 410, 930-933.
Contact:
Dr. Mandyam V. Srinivasan
Centre for Visual Sciences
Research School of Biological Sciences
Australian National University
P.O. Box 475
Canberra, A.C.T. 2601
Australia
Phone: +61-2-6125-2409
Fax: +61-2-6125-3808
email: M.Srinivasan@anu.edu.au
Web: http://biology.anu.edu.au/rsbsweb/profiles/srini.shtml
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