Figure 7

Simple cells and complex-like cells. (a) Cumulative distributions of prediction errors of the simple model (green) and the complex model (magenta) for neuron E. Prediction error was defined as the difference between the predicted response and actual response. (b) Relationship of similarities between the simple model and complex model (N = 997 neurons). Neurons with the Gabor fitting similarity ≤0.6, similarity of the simple model <0, or similarity of the complex model <0 were omitted from this analysis. (c) Distribution of complexness. Simple cells (green) and complex-like cells (magenta) were classified with threshold = 0 (black arrow). (d) Proportion of classified cells, simple cells, and complex-like cells among neurons with the CNN response prediction similarity >0.3. Classified cells were neurons with the Gabor fitting similarity >0.6, the response prediction similarity of the simple model >0, and the response prediction similarity of the complex model >0. Simple cells were neurons with complexness ≤0. Complex-like cells were neurons with complexness >0. (e–g) Relationships between complexness and linear (Lasso) prediction similarity (e), similarity between linear RFs and CNN RFs (f), and the nonlinearity index (g). Data of simple cells are presented as the mean ± s.d. (N = 739 neurons, green). Solid lines are the robust fit lines⁷⁴ for complex-like cells. Both linear prediction similarity and RF similarity of complex-like cells (magenta) negatively correlated with complexness (r = −0.35, p < 0.001, N = 258 neurons: e and r = −0.29, p < 0.001, N = 258 neurons: f), while the nonlinearity index of complex-like cells positively correlated with complexness (r = 0.34, p < 0.001, N = 258 neurons: g), suggesting that complexness defined here indeed reflected nonlinearity.