Robustness to noise of associative memory using non-monotonic analogue neurons

In this paper, the dependence of the memory capacity of an analogue associative memory model using non-monotonic neurons on static synaptic noise and static threshold noise is shown. This dependence was calculated analytically by means of the self-consistent signal-to-noise analysis (SCSNA). If the noise is extremely large, a higher monotonicity produces a larger memory capacity. At moderate noise levels, if the monotonicity increases, the memory capacity decreases conversely. The memory capacity is more sensitive to an increase in static threshold noise than to an increase in static synaptic noise.