Are age-related cognitive effects caused by optimization?

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BMC Neuroscience
Open Access
Poster presentation
Are age-related cognitive effects caused by optimization?
Hecke Schrobsdorff*1,2, Matthias Ihrke1,2, Jörg Behrendt1,3, J
Michael Herrmann1,4, Theo Geisel1,2 and Anna Levina1,2
Address: 1Bernstein Center for Computational Neuroscience, 37073 Göttingen, Germany, 2Max Planck Institute for Dynamics and Self-
Organization, 37073 Göttingen, Germany, 3Georg-Elias-Müller Institute for Psychology, 37073 Göttingen, Germany and 4School of Informatics,
University of Edinburgh, UK
Email: Hecke Schrobsdorff* - hecke@nld.ds.mpg.de
* Corresponding author
Introduction
Cognitive aging seems to be a process of global degrada-
tion. Performance in psychological tests of fluid intelli-
gence, such as Raven's Advanced Progressive Matrices,
tends to decrease with age [1]. These results are strongly
contrasted by performance improvements in everyday sit-
uations [2]. We therefore hypothesize that the observed
aging deficits are partly caused by the optimization of cog-
nitive functions due to learning.
Model
To provide evidence for this hypothesis, we consider a
neural memory model that allows for associative recall by
pattern matching as well as for "fluid" recombination of
memorized patterns by dynamical activation. In networks
with dynamical synapses, critical behavior is a generic
phenomenon [3]. It might provide the optimum for the
speed and completeness tradeoff in the exploration of a
large set of combinations of features, such as required in
Raven's test. The model comprises also the life-long
improvement in crystallized intelligence by Hebbian
learning of the network connectivity while exposed to a
number of neural activity patterns.
Results
The synaptic adaptation is shown to cause a breakdown of
the initial critical state that can be explained by the forma-
tion of densely connected clusters within the network cor-
responding to the learned patterns. Avalanche-like activity
waves in the network will more and more tend to remain
inside a cluster, thus reducing the exploratory effects of
the network dynamics. Meanwhile retrieval of patterns
stored in the early phase of learning is still possible. Mim-
icking the Raven's test, we presented the model with new
combinations of previously learned subpatterns during
various states of learning. Networks with comparatively
lower memory load achieve more stable activations of the
new feature combinations than the "old" networks. This
corresponds well to the results of the free-association
mode in either network type where only the "young" net-
works are close to a self-organized critical state. The speed
and extent of the loss of criticality depends on properties
of the connectivity scheme the network evolves to during
learning.
Conclusion
While on the one hand learning leads to impaired per-
formance in unusual situations, it may on the other hand
compensate for the decline in fluid intelligence if experi-
enced guesses even in complex situations are possible due
to the lifelong optimization of memory patterns.
Acknowledgements
This study was supported by a grant from the Bundesministerium für Bil-
dung und Forschung in the framework of the Bernstein Center for Compu-
tational Neuroscience Göttingen, grant number 01GQ0432.
from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009
Berlin, Germany. 18–23 July 2009
Published: 13 July 2009
BMC Neuroscience 2009, 10(Suppl 1):P9doi:10.1186/1471-2202-10-S1-P9
<supplement> <title> <p>Eighteenth Annual Computational Neuroscience Meeting: CNS*2009</p> </title> <editor>Don H Johnson</editor> <note>Meeting abstracts – A single PDF containing all abstracts in this Supplement is available <a href="http://www.biomedcentral.com/content/files/pdf/1471-2202-10-S1-full.pdf">here</a>.</note> <url>http://www.biomedcentral.com/content/pdf/1471-2202-10-S1-info.pdf</url> </supplement>
This abstract is available from: http://www.biomedcentral.com/1471-2202/10/S1/P9
© 2009 Schrobsdorff et al; licensee BioMed Central Ltd.

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