title: Simulated apoptosis/neurogenesis regulates learning and memory capabilities of adaptive neural networks.
abstract: Characterization of neuronal death and neurogenesis in the adult brain of birds, humans, and other mammals raises the possibility that neuronal turnover represents a special form of neuroplasticity associated with stress responses, cognition, and the pathophysiology and treatment of psychiatric disorders. Multilayer neural network models capable of learning alphabetic character representations via incremental synaptic connection strength changes were used to assess additional learning and memory effects incurred by simulation of coordinated apoptotic and neurogenic events in the middle layer. Using a consistent incremental learning capability across all neurons and experimental conditions, increasing the number of middle layer neurons undergoing turnover increased network learning capacity for new information, and increased forgetting of old information. Simulations also showed that specific patterns of neural turnover based on individual neuronal connection characteristics, or the temporal-spatial pattern of neurons chosen for turnover during new learning impacts new learning performance. These simulations predict that apoptotic and neurogenic events could act together to produce specific learning and memory effects beyond those provided by ongoing mechanisms of connection plasticity in neuronal populations. Regulation of rates as well as patterns of neuronal turnover may serve an important function in tuning the informatic properties of plastic networks according to novel informational demands. Analogous regulation in the hippocampus may provide for adaptive cognitive and emotional responses to novel and stressful contexts, or operate suboptimally as a basis for psychiatric disorders. The implications of these elementary simulations for future biological and neural modeling research on apoptosis and neurogenesis are discussed.
authors.affiliation: Division of Substance Abuse, Connecticut Mental Health Center, Yale University School of Medicine, USA. robchamb@iupui.edu
authors.forename: R Andrew
authors.initials: RA
authors.lastname: Chambers
authors.forename: Marc N
authors.initials: MN
authors.lastname: Potenza
authors.forename: Ralph E
authors.initials: RE
authors.lastname: Hoffman
authors.forename: Willard
authors.initials: W
authors.lastname: Miranker
mesh_terms: D000818:Animals; D017209:Apoptosis / Q000502:physiology*; D001369:Axons / Q000502:physiology; D001921:Brain / Q000166:cytology / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000166:cytology / Q000502:physiology*