Fig. 5.
Dynamics of the multisynaptic learning rule under various conditions. (A) Learning dynamics under various initial synaptic distributions. (Inset ) The unit EPSP distributions when synaptic connections are biased toward the distal dendrite (black), unbiased (blue), and biased toward the proximal (light blue). (B) Comparison with the monosynaptic learning. We set the learning rate as ηw = 0.03, 0.1, 0.3, 1.0, from light gray to black lines. To keep the E/I balance, the inhibitory weight was set to γI = 2.0 for ηw = 1.0, and γI = 1.25 for the rest. The magenta line is the same as the black line in A. (C) Classification performance after learning with different numbers of synapses per connection with or without rewiring. For the E/I balance, the inhibitory weights were chosen as γI = 2.0, 1.2, 0.75, 0.6, 0.5, 0.4, 0.3, when the number of synapses per connections were K = 2, 3, 5, 7, 9, 11, 13, respectively. (D) The performance after learning with various synaptic failure probabilities. Both in C and D, the performance was calculated after 1,000 trials. (E) Learning dynamics under the surrogate rule. Thin gray lines represent examples. All panels were calculated by taking the means over 50 simulations.