What Neural Network Models Can and Cannot Explain about Cognition
What Neural Network Models Can and Cannot Explain about Cognition
What are neural network models, what kind of cognitive processes can they perform, and what do they teach us about representations and consciousness? First, this chapter explains the functioning of reduced neuron models. We construct neural networks using these building blocks and explore how they accomplish memory, categorization and other tasks. Computational advantages of parallel-distributed networks are considered, and we explore their emergent properties, such as in pattern completion. Artificial neural networks appear instructive for understanding consciousness, as they illustrate how stable representations can be achieved in dynamic systems. More importantly, they show how low-level processes result in high-level phenomena such as memory retrieval. However, an essential remaining problem is that neural networks do not possess a mechanism specifying what kind of information (e.g. sensory modality) they process. Going back to the classic labeled-lines hypothesis, it is argued that this hypothesis does not offer a solution to the question how the brain differentiates the various sensory inputs it receives into distinct modalities. The brain is observed to live in a "Cuneiform room" by which it only receives and emits spike messages: these are the only source materials by which it can construct modally differentiated experiences.
Keywords: Emergent property, Labeled-lines hypothesis, McCulloch-Pitts neuron, Modality identification, Neural network, Pattern completion, Perceptual stability
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