Complexity Digest 2000.32
07-Aug-2000
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Structure and Dynamics of Complex Interactive Networks, SFI Workshop Video Notes
The website
of this workshop contains important information and links to the
work of the participants. All talks were video-taped and can be
ordered from the Santa
Fe Institute. We have recorded a number of video clips from
speakers and participants who give a summary of their work and how
it relates to the theme of the workshop. (Click on the word
"Video" in the table of content above to view the video. You need
a viewer from www.real.com
to view the videos. More information can be obtained from the
workshop website or via e-mail directly from the researchers.
- 1.1 Overview and Background, Video:
Jim Crutchfield
- 1.2 Games on Graphs and the Evolution of
Cooperation, Video:
Robert Axelrod
- 1.3 Connectivity, Cohesion, and Dynamics in Social
Networks, Video:
Doug
White
- 1.4 Scale Free Networks (i), Video:
Laszlo Barabasi
- 1.5 Scale Free Networks (ii), Video:
Reka Albert
- 1.6 Networks In The Cerebral Cortex, Video:
Olaf Sporns
- 1.7 Aggregation Of Agents To A Single, Collective
Choice, Video:
Diana
Richards
- 1.8 Large Scale National Supply Networks, Video:
Massoud Amin
- 1.9 Power Transmission Networks, Video:
George Verghese
- 1.10 Highly Optimized Tolerance and the Internet,
Video:
John
Doyle
- 1.11 Workshop Summary, Video:
Duncan
Watts
Transition From Coherence To Bistability In A Model Of Financial Markets
Abstract: We present a model describing the competition
between information transmission and decision making in financial
markets. The solution of this simple model is recalled, and
possible variations discussed. It is shown numerically that
despite its simplicity, it can mimic a size effect comparable to a
crash. Two extensions of this model are presented that allow to
simulate the demand process. One of these extensions has a
coherent stable equilibrium and is self-organized, while the other
has a bistable equilibrium, with a spontaneous segregation of the
population of agents. A new model is introduced to generate a
transition between those two equilibria. We show that the coherent
state is dominant up to an equal mixing of the two extensions. We
focus our attention on the microscopic structure of the investment
rate, which is the main parameter of the original model. A
constant investment rate seems to be a very good approximation.
Prior Information in Motor and Premotor Cortex: Activity During the Delay Period and Effect on Pre-Movement, J. Neurophysiol.
In instructed-delay (ID) tasks, instructional cues
provide prior information about the nature of a movement to
execute after a delay. Neuronal responses in dorsal premotor
cortex (PMd) during the instructed-delay period (IDP) between the
CUE and subsequent GO signals are presumed to reflect early
planning stages initiated by the prior information. In contrast,
in multiple-choice reaction-time (RT) tasks, all motor planning
and execution processes must occur after the GO signal. These
assumptions predict that neuronal planning correlates recorded
during the IDP of ID trials should share common features with
early post-GO activity in RT trials, and that those response
components need not be recapitulated after the GO signal of ID
trials. These two predictions were tested by comparing activity
recorded in RT and ID tasks from 503 neurons in PMd and caudal
(MIc) and rostral (MIr) primary motor cortex. The incidence and
strength of directionally tuned IDP activity declined
progressively from PMd to MIc. The directional tuning of activity
during the IDP of ID trials was more similar to that in the
reaction-time epoch (RTE) of RT trials than after movement onset,
especially in PMd. A modulation of post-GO activity was often
observed between RT and ID trials and was confined mainly to the
RTE. This effect was also most prominent in PMd. The most common
change was a reduction in intensity of short-latency phasic
responses to the GO signal between RT and ID trials, especially in
PMd cells with a short-latency phasic response to CUE signals.
However, the largest group of cells in each area showed no large
change in peak RTE activity between RT and ID trials, whether they
were active in the IDP or not. Since early phasic CUE-related
responses are least likely to be recapitulated after the GO signal
in ID trials, they may be a neuronal correlate of an early
planning stage such as response selection. Tonic IDP responses,
which are not as strongly associated with a post-GO reduction in
activity, may be related to other aspects of motor planning and
preparation. Finally, a major component of the movement-related
activity in both MI and PMd is not susceptible to modification by
prior information and is indivisibly coupled temporally to
movement execution.
Learning Motor Synergies Makes Use of Information on Muscular Load, Learn. Mem.
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Prism adaptation, a form of procedural learning, requires
the integration of visual and motor information for its proper
acquisition. Although the role of the visual feedback has begun to
be understood, the nature of the motor information necessary for
the development of the adaptation remains unknown. In this work we
have tested the idea that modifying the arm load at different
stages of the adaptation process, and the ensuing change of motor
information perceived by the subjects, would modify the final
properties of the adaptation. We trained a set of subjects to
throw balls to a target while wearing prism glasses and varied the
weight of their arms at different time points during the task. We
observed that the acquisition of the adaptation was not affected
by the change in load. However, its persistence (i.e., the
aftereffect) was reduced when tested under a weight condition
different from the training trials. Furthermore, when the training
weight conditions were restored later during testing, a second,
late aftereffect was unmasked, suggesting that the missing
aftereffect did not disappear but had remained latent. Our results
show that the internal representation of a motor memory
incorporates information about load conditions and that the memory
stored under a specific weight condition can be fully retrieved
only when the original training condition is restored.
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