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Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface

Fig 4

(A) Experimentally obtained cross-correlograms of MC neurons in macaque monkey during a tracking task (blue) and Gaussian fit (red). Top, Middle: for neuron pairs recorded by the same electrode, respectively (using spike-sorting). Top shows the thinnest correlation peak (σ = 9.8 ms) and Middle shows the widest (σ = 89.3 ms). Bottom: For two neurons recorded by distinct electrodes. (B) Illustration of Gaussian-shaped external cross-correlations with width (thin black line) and resulting network cross-correlation C(u) with width σ (thick blue line). (C) Relative differences for all group-averaged synapses, as a function of stimulation delay d^†, for the model network fitted to two extremal values of correlation width. Top: σ = 10 ms. Bottom: σ = 90 ms. (D) Relative differences for averaged synaptic strengths from group a to group b () as a function of stimulation delay d^† and fitted correlation peak width σ. Black dotted line corresponds to best fit plotted in E. (E) Superposition of relative difference for and normalized mean torque change from spike-triggered conditioning experiments on macaque monkeys. Experimental data from Figure 4 of [10]; error bars show the standard error of the mean. Best fit between model and experimental curve is for σ ≃ 17 ms (see black dotted line in D).

doi: https://doi.org/10.1371/journal.pcbi.1005343.g004