ARE NEURAL NETS A VALID MODEL OF COGNITION?
Commentary on Green on Connectionist-Explanation

Abstract

Connectionist models purport to model cognitive neuropsychology by means of adaptive linear algebra applied to point neurons. As a theory of cognition, this approach is deficient in several aspects: noncovergence in neurobiological real-time; omission of two topological structures fundamental to the information processing psychology on which connectionist models are based; omission of the local structure of neurobiological processing; omission of actual neuron morphologies, cortical cytoarchitecture, and the cortical orientation response; the inability to perform memory retrieval from point-neuron "weights" in neurobiological real-time; and failure to implement psychological constancy. Cognitive processing by neuronal flows is offered as a viable alternative. Finally, neural nets fail Hempel's test of empirical and systematic import.

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References

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