FIELD: physics.
SUBSTANCE: neuron simulation method is based on calculation of squares of Euclidean distance from the input vector to each of 2n vertices of a unit n-dimensional cube in weighting units, and multiplication of values inverse to these distance values with components of the target vector respectively, and then summation in an adder and conversion in the activation unit through an activation function.
EFFECT: possibility of simulating a neuron of any given Boolean function from a complete set of 
from n variables.
6 dwg, 1 tbl
| Title | Year | Author | Number |
|---|---|---|---|
| METHOD FOR REALIZING LOGICAL NONEQUIVALENCE FUNCTION BY A NEURON | 2006 |
|
RU2308758C1 |
| CONTROL METHOD OF NEUROPROCESSOR RESPONSE TO INPUT SIGNALS | 2018 |
|
RU2724784C2 |
| NEURON MODEL, REALIZING LOGICAL NONEQUIVALENCE FUNCTION | 2003 |
|
RU2269155C2 |
| METHOD OF TRAINING ARTIFICIAL NEURAL NETWORK | 2014 |
|
RU2566979C1 |
| METHOD OF NOISE-IMMUNE GRADIENT DETECTION OF CONTOURS OF OBJECTS ON DIGITAL IMAGES | 2008 |
|
RU2360289C1 |
| SELF-CHECKING MODULAR COMPUTER OF BOOLEAN FUNCTION SYSTEMS | 2009 |
|
RU2417405C2 |
| MODULE FOR BOOLEAN FUNCTION LOGIC TRANSFORMATION | 0 |
|
SU1667050A1 |
| SELF-CHECKING SPECIAL-PURPOSE COMPUTER OF BOOLEAN FUNCTION SYSTEMS | 2012 |
|
RU2485575C1 |
| METHOD FOR NOISELESS GRADIENT SELECTION OF OBJECT CONTOURS ON DIGITAL IMAGES | 2015 |
|
RU2589301C1 |
| METHOD AND ARRANGEMENT FOR LOCAL RULE OF TRAINING COMPETITION, WHICH LEADS TO A SPARSE CONNECTIVITY | 2012 |
|
RU2586864C2 |
