ROR1 Was Way Too Simple In The Past, But Now It's Virtually Impossible

In this analysis, we chose the digital-to-analog converter (DAC) synapse used on various neural chip implementations (Wang and Liu, 2006; Vogelstein et al., 2007; Schemmel et al., 2010; Linares-Barranco et al., 2011; Wang and Liu, 2011; Moradi and Indiveri, 2014). The number of bits in the DAC synapse is equivalent to the f value in the Qm.f format used for the bit precision. In this case, the quantized weight levels available are Iref/2?f where Iref is the maximum current that is equivalent to the maximum synaptic weight. Mismatch measurements from 50 copies of a particular five-bit current DAC circuit in Linares-Barranco et al. (2011) show a standard deviation around 7.77%. While the Iref can be calibrated to minimize the effect of the mismatch, we assume that there is no calibration because it would be too expensive to calibrate the many weights of a DBN network. In the case where a single DAC is used for positive and negative ABT-263 in vitro weights, then one bit is used as the sign bit. We ran simulations on a network where each synapse has a five-bit DAC. The maximum current Iref = 1 nA and one bit is used as the sign bit. The circuit noise sources such as flicker noise and thermal noise are ignored in these simulations both because of the extensive time for such simulations and the dependence on the actual device sizes of the synapse. The mismatch of the transistor that supplies the maximum current for the DAC of a synapse is assumed to have a CV of 10 or 40%. The effect of applying a CV of 40% to the weights of the receptive fields of six representative MK-2206 molecular weight neurons in the first layer of ROR1 the DBN is shown in Figure ?Figure7A.7A. Despite this high CV, the receptive fields look very similar. Figure 7 Effect of Gaussian weight variance on the performance of spiking DBNs. (A) Receptive fields of 6 representative neurons in the first hidden layer after perturbation with Gaussian weight variance of different CVs. (B) Impact of Gaussian weight variance ... The robustness of a network with this DAC precision to Gaussian variance on Iref is illustrated in Figure ?Figure7B.7B. The plots show again the performance as a function of input noise, and for two different input rates. The effect of increased Gaussian weight variance is minimal as can be seen from the different curves. As the CV increases to 40%, the classification accuracy in both the cases of 100 and 1500 Hz input rates, decreases by