AI Article Synopsis
- Animals learn complex tasks by linking their actions to intricate sensory and logical structures, relying on the hippocampus to create a cognitive map for navigation.
- The study proposes a model where individual cell learning and reinforcement learning work together to form task-specific representations crucial for problem-solving, while ignoring irrelevant information.
- This model aligns with existing experimental data and offers testable predictions about how these task-related representations and behaviors develop simultaneously.
Article Abstract
To successfully learn real-life behavioral tasks, animals must pair actions or decisions to the task's complex structure, which can depend on abstract combinations of sensory stimuli and internal logic. The hippocampus is known to develop representations of this complex structure, forming a so-called "cognitive map". However, the precise biophysical mechanisms driving the emergence of task-relevant maps at the population level remain unclear. We propose a model in which plateau-based learning at the single cell level, combined with reinforcement learning in an agent, leads to latent representational structures codependently evolving with behavior in a task-specific manner. In agreement with recent experimental data, we show that the model successfully develops latent structures essential for task-solving (cue-dependent "splitters") while excluding irrelevant ones. Finally, our model makes testable predictions concerning the co-dependent interactions between split representations and split behavioral policy during their evolution.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10806076 | PMC |
| http://dx.doi.org/10.1038/s41467-024-44871-6 | DOI Listing |
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