Global attractivity criteria for a discrete-time Hopfield neural network model with unbounded delays via singular <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si15.svg"> <mml:mi>M</mml:mi> </mml:math> —matrices

In this work, we establish two global attractivity criteria for a multidimensional discrete-time non-autonomous Hopfield neural network model with infinite delays and delays in the leakage terms. The first criterion, which applies when the activation functions are bounded, is based on 𝑀-matrices that are not necessarily invertible. The second criterion, relevant for unbounded activation functions, requires that a related singular 𝑀-matrix be irreducible. We contrast our findings with existing results in the literature and present numerical simulations to illustrate the novelty of the proposed criteria.