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The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis

Fig 5

Learning to recognize repetitive patterns with heterosynaptic plasticity.

(A) A “signal” pattern is presented repeatedly to the neuron interspersed with non-repeating random “noise” patterns of the same sparsity. (B) Within each input pattern (both signal or noise) inactive synapses depress (above at left) due to the heterosynaptic calcium, whereas active synapses will potentiate from the sum of heterosynaptic calcium, , and local calcium (above at right) (C1) Signal and noise patterns are presented to the neuron. (S: signal, N: noise). (C2) Spiking output of the calcitron. Black: no spike, Red: spike. An ‘x’ marker indicates incorrect output (e.g., no spike in response to a signal pattern, or a spike in response to a noise pattern), filled circles indicate correct outputs. Note the increase in correct spiking output over time. (C3–C5) Calcium, plasticity, and weights over time respectively as the input patterns in C1 are presented.

doi: https://doi.org/10.1371/journal.pcbi.1012754.g005