(A) The storage capacity decreases when the noise strength increases strongly depends on r1. Small values of r1 lead to large storage capacity, but storage is highly sensitive to noise. Large values of r1 lead to small capacity, but storage is highly robust. (B) The trade-off between p0 and R, where p0 is the storage capacity in the absence of noise (r3 = 0), and R quantifies robustness to noise, Eq (14). With deeper potentials, the storage capacity is smaller but storage is more robust. Here, C is optimized for each value of r1 and other parameters are given as: N = 10, 000, r2 = 1, θ = 0, f = 0.5, c = 0.05.</p
Cowan's review shows that a short-term memory limit of four items is consistent with a wide range of...
(A) Dependence of optimal potential width C* on network size N and potential depth r1. There is a cr...
<p>Blue is for a fixed threshold and fluctuations in the number of selective neurons per pattern. Gr...
When C is small (e.g., C = 0, 1, 2, 3), the storage capacity p monotonically decreases with the nois...
(A) Dependence of p on C for . In the small r1 limit, the optimal potential width C* is zero (i.e., ...
<p>The theoretical calculations is compared with the simulations for <i>f</i> = 0.2. Note that the c...
<p><b>A.</b> The red plot shows the critical capacity as a function of the size of the basins of att...
<p>a. Optimal information capacity as a function of , the average number of activated synapses after...
(A) Storage capacity of the network as a function of r1 with the learning rule defined in Eq (6) (B)...
Experimental investigations have revealed that synapses possess interesting and, in some cases, unex...
<p><i>N</i> = 10000, <i>K</i> = 15, <i>M</i> = 1000 and the optimal connectivity rate are used. (A) ...
<p><b>A,</b> Contour plot of pattern capacity (number of stored memories) as a function of assembly...
<p>A–B. Dependence on coding levels. A. Maximal capacity as a function of for different coding leve...
<p>All network are of size <i>N</i> = 1000. <b>A</b>: Recall as a function of the load for different...
<p>A. The synaptic information capacity versus the coding density for soft-bound (solid line) and ha...
Cowan's review shows that a short-term memory limit of four items is consistent with a wide range of...
(A) Dependence of optimal potential width C* on network size N and potential depth r1. There is a cr...
<p>Blue is for a fixed threshold and fluctuations in the number of selective neurons per pattern. Gr...
When C is small (e.g., C = 0, 1, 2, 3), the storage capacity p monotonically decreases with the nois...
(A) Dependence of p on C for . In the small r1 limit, the optimal potential width C* is zero (i.e., ...
<p>The theoretical calculations is compared with the simulations for <i>f</i> = 0.2. Note that the c...
<p><b>A.</b> The red plot shows the critical capacity as a function of the size of the basins of att...
<p>a. Optimal information capacity as a function of , the average number of activated synapses after...
(A) Storage capacity of the network as a function of r1 with the learning rule defined in Eq (6) (B)...
Experimental investigations have revealed that synapses possess interesting and, in some cases, unex...
<p><i>N</i> = 10000, <i>K</i> = 15, <i>M</i> = 1000 and the optimal connectivity rate are used. (A) ...
<p><b>A,</b> Contour plot of pattern capacity (number of stored memories) as a function of assembly...
<p>A–B. Dependence on coding levels. A. Maximal capacity as a function of for different coding leve...
<p>All network are of size <i>N</i> = 1000. <b>A</b>: Recall as a function of the load for different...
<p>A. The synaptic information capacity versus the coding density for soft-bound (solid line) and ha...
Cowan's review shows that a short-term memory limit of four items is consistent with a wide range of...
(A) Dependence of optimal potential width C* on network size N and potential depth r1. There is a cr...
<p>Blue is for a fixed threshold and fluctuations in the number of selective neurons per pattern. Gr...