The ability to maintain relevant information on a daily basis is negatively impacted by aging. However, the neuronal mechanism manifesting memory persistence in young animals and memory decline in early aging is not fully understood. A novel event, when introduced around encoding of an everyday memory task, can facilitate memory persistence in young age but not in early aging. Here, we investigated in male rats how sub-regions of the hippocampus are involved in memory representation in behavioral tagging and how early aging affects such representation by combining behavioral training in appetitive delayed-matching-to-place tasks with the “cellular compartment analysis of temporal activity by fluorescence in situ hybridization” technique. We show that neuronal assemblies activated by memory encoding were also partially activated by novelty, particularly in the distal CA1 and proximal CA3 subregions in young male rats. In early aging, both encoding- and novelty-triggered neuronal populations were significantly reduced with a more profound effect in encoding neurons. Thus, memory persistence through novelty facilitation engages overlapping hippocampal assemblies as a key cellular signature, and cognitive aging is associated with underlying reduction in neuronal activation.

The integration of deep learning and theories of reinforcement learning (RL) is a promising avenue to explore novel hypotheses on reward-based learning and decision-making in humans and other animals. Here, we trained deep RL agents and mice in the same sensorimotor task with high-dimensional state and action space and studied representation learning in their respective neural networks. Evaluation of thousands of neural network models with extensive hyperparameter search revealed that learning-dependent enrichment of state-value and policy representations of the task-performance-optimized deep RL agent closely resembled neural activity of the posterior parietal cortex (PPC). These representations were critical for the task performance in both systems. PPC neurons also exhibited representations of the internally defined subgoal, a feature of deep RL algorithms postulated to improve sample efficiency. Such striking resemblance between the artificial and biological networks and their functional convergence in sensorimotor integration offers new opportunities to better understand respective intelligent systems.