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Are age-related cognitive effects caused by optimization?

Hecke Schrobsdorff1, 2*, Matthias Ihrke1, 2, Jörg Behrendt1, 3, Michael J. Herrmann1, 4, Theo Geisel1, 2 and Anna Levina1, 2
  • 1 Bernstein Center for Computational Neuroscience, Germany
  • 2 Max Planck Institute for Dynamics and Self-organization, Germany
  • 3 Georg-August University, Georg-Elias-Müller Institute for Psychology, Germany
  • 4 University of Edinburgh, Institute of Perception, Action and Behaviour, United Kingdom

Introduction Cognitive aging seems to be a story of global degradation. Performance in psychological tests e.g. of fluid intelligence, such as Raven's Advanced Progressive Matrices, tends to decrease with age [1]. These results are strongly contrasted by performance improvements in everyday situations [2]. We therefore hypothesize that the observed aging deficits are partly caused by the optimization of cognitive functions due to learning.

Model In order 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 behaviour 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 like it is 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 which can be explained by the formation of densely connected clusters within the network corresponding 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. Mimicking 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' networks 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 performance in unusual situations it may on the other hand compensate for the decline in fluid intelligence if experienced guesses even in complex situations are possible due to the live long optimization of memory patterns.

Acknowledgements This study was supported by a grant from the Bundesministerium für Bildung und Forschung in the framework of the Bernstein Center for Computational Neuroscience Göttingen, grant number 01GQ0432.

References

1. Babcock RL: Analysis of age differences in types of errors on the Raven's Advanced Progressive Matrices. Intelligence 2002, 30:485 - 503.

2. Salthouse TA: Cognitive competence and expertise in aging. Handbook of the psychology of aging 1999, 3:310 - 319.

3. A Levina, J M Herrmann and T Geisel: Dynamical synapses causing self-organized criticality in neural networks. Nature Physics 2007, 3(12):857 - 860.

Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009.

Presentation Type: Poster Presentation

Topic: Learning and plasticity

Citation: Schrobsdorff H, Ihrke M, Behrendt J, Herrmann MJ, Geisel T and Levina A (2009). Are age-related cognitive effects caused by optimization?. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.142

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Received: 28 Aug 2009; Published Online: 28 Aug 2009.

* Correspondence: Hecke Schrobsdorff, Bernstein Center for Computational Neuroscience, Göttingen, Germany, hecke@nld.ds.mpg.de