| - Thought requires a stable memory to make repetitive
deductions. - The memory stored in the weight of the neural networks cannot achieve this persistence, as the synaptic weights get spontaneously modified or tend towards standard sizes (the lifetime of a modification could be as short a day). - The model uses the algorithms successful for explaining the cognitive functions of the brain, such as the AI deductive algorithms based on production rule systems. - The all-or-none mechanisms used in digital computers are the only devices on which convincing demonstrations of intelligent behavior have been shown. - The purpose of the model presented in the book is to force digital operations within an organization not currently falsified by the current knowledge on the biological brain. |
- The brain model suggested is opposed to several current
ideas, such as the implicit view that a plastic, adaptive nervous system cannot make
all-or-none decisions in a neuron or in a synapse. - The problem addressed is not to find deep unifying theories but to understand how apparently incompatible simple systems can be integrated. - In the engineering approach, the task is to integrate hybrid devices into a working system. - This design method is called large-scale architecture and is not addressed by the reductionism methods. - The cover of the book shows several functions that are all required for a comprehensive brain system (an updated colored version is shown below). - Their integrated operations are compatible with the neuroanatomical brain connectivity. |
