Biomedical Engineering Reference
In-Depth Information
Chapter 18
ComputationalModelsfor
GenericCorticalMicrocircuits
Wolfgang Maass,
1
Thomas Natschlaeger,
1
and Henry Markram
2
1
Institute for Theoretical Computer Science, Technische Universitaet Graz, A-8010
Graz, Austria, maass,tnatschl@igi.tu-graz.ac.at,;
2
Brain Mind Institute, EPFL,
Lausanne, Switzerland, henry.markram@epfl.ch
CONTENTS
18.1 Introduction
18.2 Aconceptualframeworkforreal-timeneuralcomputation
18.4 Towards a non-Turing theory for real-time neural computation
18.5 Agenericneuralmicrocircuitonthecomputationalteststand
18.5.1 Speech recognition
18.5.2 Predictingmovementsandsolvingtheapertureproblem
18.6 Temporal integration and kernel function of neural microcircuit models
circuitmodel
18.8 Discussion
References
18.1
Introduction
A key challenge for neural modeling is to explain how a continuous stream of multi-
modal input from a rapidly changing environment can be processed by neural micro-
circuits (columns, minicolumns, etc.) in the cerebral cortex whose anatomical and
physiological structure is quite similar in many brain areas and species. However, a
model that could explain the potentially universal computational capabilities of such
microcircuits has been missing. We propose a computational model that does not
require a task-dependent construction of neural circuits. Instead it is based on prin-
ciples of high dimensional dynamical systems in combination with statistical learn-
ing theory, and can be implemented on generic evolved or found recurrent circuitry.
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