Learning more than one embedded pattern.

(A) Input activity in the raster plot, five hundred afferents (80% excitatory and 20% inhibitory) send inputs to one post-synaptic neuron. The excitatory neurons’ rate is 5 Hz, and the inhibitory neurons’ rates are 20 Hz. There are two random embedded patterns of length 50 ms in the red and blue areas (between black vertical lines), r0 = 4 Hz, and the length of the training epoch is 1000 ms. (B) Membrane potential versus time. The black trace shows that after learning the neuron responds to both embedded patterns. The green line is for resting potential, red is for threshold, blue is for before learning, and black is for after learning. (C) During learning, all embedded patterns are in the epoch, and we compute the percentages of neurons that can detect only one of the patterns (blue), two (green), three (red), and four of them depending on the target rate r0. This figure is based on an average of 100 simulations with 500 afferents. The training epoch is 1000 ms, Lem = 50 ms, L = 15 ms. (D) First, the neuron learns two embedded patterns, and then R as a memory criterion vs. learning cycle is computed: Learning continues without learned patterns in afferents, and every 50 cycles, learning is paused, and the learned patterns are placed in the background, then R is computed as a memory criterion. There are two 50 ms embedded patterns, shown in afferents starting from 400 ms and 800 ms, L = 15 ms, and this figure is based on an average of 500 simulations. r0 = 4 Hz.