We study an Attractor Neural Network that stores natural concepts, organized in semantic classes. The concepts are represented by distributed patterns over a space of attributes, and are related by both semantic and episodic associations. While semantic relations are expressed through an hierarchical coding over the attribute space, episodic links are realized via specific synaptic projections. Due to dynamic thresholds expressing neuronal fatigue, the network's behavior is characterized by convergence toward the concept patterns on a short time scale, and by transitions between the various patterns on a longer time scale. In its baseline, undamaged state, the network manifests semantic, episodic, and random transitions, and demonstrates the phenomen of priming. Modeling possible pathological changes, we have found that increasing the `noise' level or the rate of neuronal fatigue decreases the frequency of semantic transitions. When neurons characterized by large synaptic connectivity are deleted, semantic transitions decay before the episodic ones, in accordance with the findings in patients with Alzheimer's disease.