Skip to main content
. 2020 Nov 30;117(50):32124–32135. doi: 10.1073/pnas.2005087117

Fig. 5.

Fig. 5.

mRNN outperforms tested alternative models in explaining neural data in the grasping circuit. (A) Average neural variance explained per recording session for the best set of regularization parameters for each architecture (averaged over five runs) for each of the three proposed metrics, Overall Fit (A), Area-Wise Fit (B), and Interarea Fit (C). Horizontal bars represent the mean, and each dot represents a single session. We tested five alternative models in addition to the Full model: 1) mRNN model with only feed-forward connections between modules; 2) mRNN model receiving a labeled line input (one hot), where each condition is represented by a separate input dimension; 3) mRNN model with output conditions shuffled (objects reassigned); 4) homogeneous, fully connected network; or 5) a single, sparsely connected network with the total number of synaptic connections matched to the Full model. *Significant difference as compared with the Full model (paired t test, P < 0.01). (D, F, and H) Procrustes analysis (Overall Fit) comparing the dynamics of two exemplar models with neural data across all brain regions (session M2). For visualization purposes, after model data were fit to neural data they were projected onto the first six PCs defined on the neural data, and percentages show variance explained (var expl) in the neural data per PC. h. and v. correspond to horizontal and vertical cylinders, respectively. Pairwise Procrustes was performed (E) between each brain region and a resampled version of its own activity or (G and I) between each module and brain region (Interarea Fit). Individual rows and columns specify from top to bottom and from left to right either the output, intermediate, and input module or M1, F5, and AIP, respectively. (F) Exemplar model with the parameters (homogeneous model, ReTanh activation function, L2 rate regularization, 1e-1; L2 weight regularization, 1e-5; intermodule sparsity, 0.1). (H) Exemplar model with the parameters (condition-shuffled output model, ReTanh activation function, rate regularization, 1e-1; weight regularization, 1e-5; intermodule sparsity, 0.1). For D, F, and H, the multiple traces for each type of object represent the different sizes within a turntable.