A matrix calculus for neural nets: II
Bulletin of Mathematical BiologyPublished 1 June 1947
H. D. Landahl
Citations15
SJR quartileQ1
SJR score0.70
SNIP0.98
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Abstract
In a previous paper a method was given by which the efferent activity of an idealized neural net could be calculated from a given afferent pattern. Those results are extended in the present paper. Conditions are given under which nets may be considered equivalent. Rules are given for the reduction or extension of a net to an equivalent net. A procedure is given for constructing a net which has the property of converting each of a given set of afferent activity patterns into its corresponding prescribed efferent activity pattern.
Keywords
Computer Science
Bulletin of Mathematical BiologyA logical calculus of the ideas immanent in nervous activity
17,984 Citations1943Warren S. McCulloch, Walter Pitts
Bulletin of Mathematical BiologyOutline of a matrix calculus for neural nets
26 Citations1946H. D. Landahl, Richard J. Runge
The activity of a neural net is represented in terms of a matrix vector equation with a normalizing operator in which the matrix represents only the complete structure of the net, and the normalized vector-matrix product represents the activity of all the non-afferent neurons.