ABSTRACT
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by neural microcircuits (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 principles of high dimensional dynamical systems in combination with statistical learning theory, and can be implemented on generic evolved or found recurrent circuitry. 1-58488-362-6/04/$0.00+$ 1.50
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.
