04-16-2025, 08:53 AM
Hi all. 
Until yesterday evening, there was only one type of SB-neurons in my consciousness.
This was the type in which fixed weights of the neuron's inputs created a precise x-coordinate scale for the Activation Function graph.
The training of such a neuron consists of simply writing the training data into the Activation Function about the coordinates X, which are specified by the data at the neuron's inputs.
Such neurons are good at determining the nature of the DEPENDENCE of output values on input data.
However, there is another class of problems in which it is necessary to RECOGNIZE an object, or a current situation, based on an input SET OF FEATURES.
In this case, the input data of the SB-neuron should not create the exact X-coordinate from which the Activation Function will determine the output value.
In this case, the input weights must indicate the IMPORTANCE of each feature so that the neuron can confidently determine that the current situation corresponds to the situation that the neuron was trained on.
Such a SB-neuron already closely resembles the classical neurons that are used in the ANN extension.
I'll call it "SB-neuron Type-B".
But the internal structure of such a SB-neuron will differ from the classical one.
Until yesterday evening, there was only one type of SB-neurons in my consciousness.
This was the type in which fixed weights of the neuron's inputs created a precise x-coordinate scale for the Activation Function graph.
The training of such a neuron consists of simply writing the training data into the Activation Function about the coordinates X, which are specified by the data at the neuron's inputs.
Such neurons are good at determining the nature of the DEPENDENCE of output values on input data.
However, there is another class of problems in which it is necessary to RECOGNIZE an object, or a current situation, based on an input SET OF FEATURES.
In this case, the input data of the SB-neuron should not create the exact X-coordinate from which the Activation Function will determine the output value.
In this case, the input weights must indicate the IMPORTANCE of each feature so that the neuron can confidently determine that the current situation corresponds to the situation that the neuron was trained on.
Such a SB-neuron already closely resembles the classical neurons that are used in the ANN extension.
I'll call it "SB-neuron Type-B".
But the internal structure of such a SB-neuron will differ from the classical one.