title: A neural network model based on the analogy with the immune system.
abstract: The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli ("questions") selectively applied to the network by a "teacher" can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep.
mesh_terms: D001185:Artificial Intelligence; D002490:Central Nervous System / Q000502:physiology; D007107:Immune System; D007858:Learning; D008433:Mathematics; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009424:Nervous System Physiological Phenomena; D009474:Neurons / Q000502:physiology; D012890:Sleep / Q000502:physiology; D013663:Teaching