Semantization of memories in a hippocampal–cortical spiking neural network

The human brain consolidates episodic memories into more semantic representations during sleep, enabling the continuous integration of new knowledge. This transformation is supported by hippocampal replays, which triggers a reactivation and reshaping of synaptic connections in the neocortex. In this study, we developed a plastic spiking neural network of Leaky Integrate-and-Fire (LIF) neurons to simulate the interaction between the hippocampus, perceptual neocortical areas, and neocortical regions responsible for processing semantic and contextual information. The model operates in two learning phases: first, the network encodes new experiences as episodic memories; then, during a sleep-like phase, hippocampal reactivation propagates to the neocortex. Crucially, the model leverages the apical mechanisms recently proposed by the experimentally grounded Dendritic Integration Theory (DIT) to modulate the activity of neural assemblies during both learning and replay. We evaluated the model on continual learning tasks, including split and rotational MNIST, and further demonstrated its practical applicability by training and testing it on sensory data from a soft pneumatic gripper in a dynamic robotic environment.