Global neural encoding of model-free and inference-based strategies in mice
Wang, S.; Gao, H.; Ishizu, K.; Funamizu, A.
Show abstract
When a simple model-free strategy does not provide sufficient outcomes, an inference-based strategy estimating a hidden task structure becomes essential for optimizing choices. However, the neural circuitry involved in inference-based strategies is still unclear. We developed a tone frequency discrimination task in head-fixed mice in which the tone category of the current trial depended on the category of the previous trial. When the tone category was repeated every trial, the mice continued to use the default model-free strategy, as well as when tone was randomly presented, to bias the choices. In contrast, the default strategy gradually shifted to an inference-based strategy when the tone category was alternated in each trial. Brain-wide electrophysiological recording during the overtrained phase suggested that the neural activity of the frontal and sensory cortices, hippocampus, and striatum was correlated with the reward expectation of both the model-free and inference-based strategies. These results suggest the global encoding of multiple strategies in the brain.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Localized and global representation of prior value, sensory evidence, and choice in male mouse cerebral cortex 99%
- Temporal Dynamics of Nucleus Accumbens Neurons in Male Mice During Reward Seeking 98%
- Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task 97%
Similar papers in this journal
- Dynamic organization of cerebellar climbing fiber response and synchrony in multiple functional modules reduces dimensions for reinforcement learning 98%
- Response outcome gates the effect of spontaneous cortical state fluctuations on perceptual decisions 97%
- Nonlinear feedback modulation contributes to the optimization of flexible decision-making 97%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.