Neural learning paradigms

To summarize there are two learning paradigms in the context of a reflex action controlled by a single ganglion in an experimental animal in which electrical measurements of nerve action are relatively easy. This experimental system permitted Kandel to map the neural circuit responsible for the gill-withdrawal reflex. The neural... 

There are two learning paradigms that determine how a network relates to its environment. In supervised learning (learning with teacher), a teacher provides output targets for each input pattern, and corrects the network s errors explicitly. The teacher has knowledge of the environment (in the form of a historical set of input-output data) so that the neural network is provided with desired response when a training vector is available. The... 

Figure 2 illustrates a so-called controlled learning paradigm where the network is supervised so as to work in a one-step-ahead method in the model predicting the object The feedback can come from two sources, depending on the position of switch FFN-RecN. In the FFN position, the artificial neural network is combined into the structure of feed forward network, while in the RecN position, the network structure corresponds to the recurrent network. 

Implicit in this paradigm is the notion that the cybernetic sequence is embedded in the neural, knowledge base, and learned internal models and can be mapped by defining the differential change in mental, disciplinary structural, and expert models. 

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