Selective inhibition in CA3: A mechanism for stable pattern completion through heterosynaptic plasticity
Fig 5
Encoding of inputs and learned synapses.
(A) Weight matrices after STDP learning. Weights represent the connection weights multiplied by the learned peak conductances. Only CA3 neurons encoding the 10 example inputs are visualized; E, excitatory; I, inhibitory. (A1) Learned direct PP weight matrix from superficial EC to CA3 excitatory. (A2) Learned excitatory Rc weight matrix from CA3 excitatory to CA3 excitatory. (A3) Learned Rc weight matrix from CA3 excitatory to CA3 inhibitory. (A4) Learned Rc weight matrix from CA3 inhibitory to CA3 excitatory. (A5) Learned Sc weight matrix from CA3 excitatory to CA1. (B) Raster plot during the encoding of 10 different inputs (A to J). Shown are spikes in the superficial EC, excitatory neurons in the GCL, CA3 excitatory and inhibitory neurons, CA1, and deep EC. Other types of neurons in the DG are omitted. As in (A), only the CA3 excitatory neurons active in these examples are selected for visualization. (C) CA3 memory capacity: number of engrams formed versus number of input patterns. (D) Relationship between input pattern size and resulting CA3 engram size (points) with a linear fit.