Fig. 6 | Nature Communications

Fig. 6

From: Feature-based learning improves adaptability without compromising precision

Fig. 6

Architectures and performances of two alternative network models for multi-dimensional, decision-making tasks. a, b Architectures of the PDML (a) and the HDML (b) models. In both models, there are two sets of value-encoding neurons that estimate reward values of individual objects (object-value-encoding neurons, OVE) and features (feature-value-encoding neurons, FVE). The two models are different in how they combine signals from the OVE and FVE neurons and how the influence of these signals on the final decision is adjusted through reward-dependent plasticity. c The time course of the overall strengths of plastic synapses between OVE and FVE neurons and the final DM circuit (C O and C F) in the PDML model, or between OVE and FVE neurons and the signal-selection circuit (C O and C F) in the HDML model. These simulations were done for the generalizable environment (Experiment 1) where the block length was 48. d The difference between the C F and C O over time in the two models. e The difference in the overall weights of the two sets of value-encoding neurons on the final decision (W FW O) for the same set of simulations shown in c, d

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