Computational Models for Generic Cortical Microcircuits
W. Maass, T. Natschlaeger, and H. Markram
The human nervous system processes a continuous stream of multi-modal input
from a rapidly changing environment. A key challenge for neural modeling is
to explain how the neural microcircuits (columns, minicolumns, etc.) in the
cerebral cortex whose anatomical and physiological structure is quite similar
in many brain areas and species achieve this enormous computational task. We
propose a computational model that could explain the potentially universal
computational capabilities and does not require a task-dependent construction
of neural circuits. Instead it is based on principles of high dimensional
dynamical systems in combination with statistical learning theory, and can be
implemented on generic evolved or found recurrent circuitry. This new
approach towards understanding neural computation on the micro-level also
suggests new ways of modeling cognitive processing in larger neural systems.
In particular it questions traditional ways of thinking about neural coding.
Reference: W. Maass, T. Natschlaeger, and H. Markram.
Computational models for generic cortical microcircuits.
In J. Feng, editor, Computational Neuroscience: A Comprehensive
Approach, chapter 18, pages 575-605. Chapman & Hall/CRC, Boca Raton, 2004.