<p>a,b. as a function of for the SP model and Parameters are chosen to optimize capacity under the binomial approximation. Shown are the result of the gaussian approximation without covariance (cyan) and with covariance (magenta) for these parameters. c. Optimized as a function of for the SP model at . The blue curve is for patterns with fluctuations in the number of selective neurons. The red curve is for the same number of selective neurons in all patterns. The black curve is the number of patterns that would be stored if the network were storing the same amount of information as in the case . d. Same for the MP model, where parameters have been optimized, but the depression-potentiation ratio is fixed at .</p
(a) Dots show trace strengths of individual trained (blue) and untrained (red) patterns. The time at...
We study the number p of unbiased random patterns which can be stored in a neural network of N neuro...
We have previously shown using biophysically detailed compartmental models that nonlinear interactio...
<p>Blue is for a fixed threshold and fluctuations in the number of selective neurons per pattern. Gr...
<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) Dependence of optimal potential width C* on network size N and potential depth r1. There is a cr...
(A) Storage capacity of the network as a function of r1 with the learning rule defined in Eq (6) (B)...
(a) Capacity curves are plotted for pattern densities ranging from 0.8% to 18%. Dendrite size is plo...
(A) Dependence of p on C for . In the small r1 limit, the optimal potential width C* is zero (i.e., ...
<p>a. as a function of , b. as a function of , the ratio between the number of depressing events a...
<p><b>A,</b> Contour plot of pattern capacity (number of stored memories) as a function of assembly...
<p>A. The Information capacity showing the theory (black) as well as simulations for 10, 100, 1000, ...
(a) Illustration of the setup used to assess computational capacity. An input signal, u, is used to ...
AblncL In lhis paper we consider two main aspsln of the binary perccptron problem: the maximal capac...
(a) Dots show trace strengths of individual trained (blue) and untrained (red) patterns. The time at...
We study the number p of unbiased random patterns which can be stored in a neural network of N neuro...
We have previously shown using biophysically detailed compartmental models that nonlinear interactio...
<p>Blue is for a fixed threshold and fluctuations in the number of selective neurons per pattern. Gr...
<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) Dependence of optimal potential width C* on network size N and potential depth r1. There is a cr...
(A) Storage capacity of the network as a function of r1 with the learning rule defined in Eq (6) (B)...
(a) Capacity curves are plotted for pattern densities ranging from 0.8% to 18%. Dendrite size is plo...
(A) Dependence of p on C for . In the small r1 limit, the optimal potential width C* is zero (i.e., ...
<p>a. as a function of , b. as a function of , the ratio between the number of depressing events a...
<p><b>A,</b> Contour plot of pattern capacity (number of stored memories) as a function of assembly...
<p>A. The Information capacity showing the theory (black) as well as simulations for 10, 100, 1000, ...
(a) Illustration of the setup used to assess computational capacity. An input signal, u, is used to ...
AblncL In lhis paper we consider two main aspsln of the binary perccptron problem: the maximal capac...
(a) Dots show trace strengths of individual trained (blue) and untrained (red) patterns. The time at...
We study the number p of unbiased random patterns which can be stored in a neural network of N neuro...
We have previously shown using biophysically detailed compartmental models that nonlinear interactio...