Identifying properties of pattern completion neurons in a computational model of the visual cortex
Fig 6
Modern GCaMP sensors may be able to identify efficient pattern completion neurons in vivo.
(A) Raster plots of four example ensemble recall events (from ensemble 1) evoked by stimulation of different pairs of neurons. Neurons in the ensemble activated in a sequential manner rather than simultaneously. The y-axis spans all neurons in the ensemble. (B) Scatter plot of average latency vs. PCC in ensemble 1 (top) and ensemble 2 (bottom) for all 800 trials of each neuron pair. (C) Distribution of 0–80% rise time for GCaMP8f and GCaMP7f. Data were taken from previous research that performed simultaneous calcium imaging and cell attached electrophysiology in mouse visual cortex [81]. Inset: The standard deviation of GCaMP8f’s rise time was less than that of GCaMP7f. (D) Effect of calcium indicators on Pearson r between latency and PCC when calculated with 100 trials. Error bars represent mean ± standard deviation. ** indicates p < 0.01 and *** indicates p < 0.001. (E) Accuracy of latency measurement as a function of the number of ensemble recall events for ensemble 1 (left) and ensemble 2 (right). The correlation between latency and PCC increased as the number of ensemble recall events increased and as the temporal dynamics of calcium indicators became less variable. The shaded region is the mean ± s.e.m of the correlation coefficient.