Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits
Fig 4
Optimal correlation timescale changes depending on noise characteristics.
(A) Response kernels of input neurons to external events (left) and cross-correlation among input neurons responding to the same source calculated from simulated data (right) for three different correlation timescale parameters θt. (B) Raster plots of input neurons for various θt. Only 100 correlated neurons are plotted although there are 400 input neurons in total. (C) Analytically calculated correlation kernels g1X, g2X (left), and their ratio g1X/g2X. (D) Specialization index wSI for various response probabilities qB while fixing qA = 0.6. Lines represent wR at analytically estimated stable points, and dotted squares represent simulation results. (E) Raster plots of two types of noise. The upper panel shows random noise, whereas the lower panel depicts crosstalk noise. In both panels, the first 100 neurons respond primarily to the cyan source, and the next 100 neurons respond to the purple source. For random noise, the noise (black dots) is independent from the signals, whereas the crosstalk noise (purple dots in the lower half, cyan dots in the upper half) is correlated with the signal for the other population. (F, G) The effects of random noise (F) and crosstalk noise (G) at various correlation timescales.