Figure 7. Neural Network Modeling Identifies Significant Dimensions of Functional Connectivity.
(A) Model incorporating hindbrain circuitry. Green, F, forward premotor units. Gray circles, Pt neurons projecting to the ipsilateral early hindbrain (EHB, ovals) and late hindbrain (LHB). Red, T, turn premotor circuitry. Neurons numbered as in Figures 5B and 6B.
(B) Best-fit connection strengths (95% confidence intervals) between neuron types and premotor units. Only two types predict significant non-zero connections (darker bars).
(C) Left, model error as a function of connectivity strength between neuron type 1 and F and neuron type 2 and F; all others connected by best-fit connection strengths. Right, while Pt neurons do not behave independently, we identified independent dimensions of population connectivity (functional modes) by rotating the error surface. Each axis now corresponds to a pattern of activation and suppression across neuron types (STAR Methods).
(D) Top, independent connectivity patterns for neuronal response types (rows) within each functional mode (columns), sorted according to the model’s sensitivity to perturbations in the direction of each mode (black bars, top). Bottom, fraction of error eliminated by each mode. (*) modes with a reliable influence on behavior (STAR Methods).
(E) Illustration of how modes contribute to model output in response to stimuli (here shown only for coherent rightward motion).
(F) The behavioral output associated with individual swimming modes (ModeF) and turning modes (ModeT). Top two rows show output for the two most significant swimming (left) and turning (right) modes. Bottom row shows collective output for the six most and two most significant swimming and turning modes, respectively. Bars are colored by stimulus identity.
(G) Quantitative whole-brain model for the OMR, reflecting the best-fit connectivity in Figure 7B. Neuron types are represented by symbols illustrating activation and suppression by motion stimuli (see legend).