The observed dissociation in ERP measures is interpreted as evidence of activation spreading in the network of multiplication facts, with the short-lasting response elicited by neighbors likely reflecting activation dissipating over time because of a weaker association to the cue digits.
Galfano et al. (2009) interpreted this pattern as evidence that automatic activation in the network of multiplication facts spreading from cue numbers fades away more quickly for weakly associated items (i.e., multiple probes) than for strongly associated items (i.e., product probes).
Figure 4 demonstrates the network performing frequency multiplication on an oscillating input with an input minimum of 6 nM.
Numerical simulations showed the network performing frequency multiplication of one half on an oscillating input with a period of 90000 seconds (25 hours) (see File S3).
Of the three functions of our network, only frequency multiplication was rationally designed, while the switch and oscillator functions were discovered.
We first confirmed the discrete switching behaviour described in figure 2. Numerical simulations in figure 3 show the network performing frequency multiplication of one half on a square wave input, i.e. the period of the oscillations in R1 to R4 is twice that of the input.
More than 1,000 views on Videolectures! Modeling real-world networks using Kronecker multiplication.
V. Elser, A network that learns Strassen multiplication, JMLR 17 (2016) 1-13.
Network marketing counts on the multiplication factor.
The following algorithms will be briefly explained: brain network, correlation, classification, dense matrix multiplication, fast Fourier transform 1D, fast Fourier transform 2D, motion estimation, hybrid coin miner, high speed packet capture, breast mammogram ROI extraction, linear regression, and n-body simulation.
Because neural networks rely heavily on matrix multiplication, using GPUs slashes the time it takes to train them from months or weeks to days or even hours, in some cases.
