In the brain, the model is the goal

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Abstract

Both computational and empirical studies use models of neural tissue to make inferences about the intact system. Their aims and scope are complementary, however, and their methods have different strengths and weaknesses. For example, much of our knowledge of synaptic integration comes from in vitro slices. These slices, which finish out their brief lives in man-made extracellular fluid, are crude models of the intact brain, with deeper resting potentials, lower background firing rates, higher input resistances, severed inputs, and so on. Test pulses delivered to a nerve or puffs of glutamate to a dendritic branch are crude models of synaptic stimulation in vivo. Recordings of one or two voltages within a spatially extended neuron provide a highly reduced model of the cell's electrical state. Similarly, long-term potentiation is a simplified model for learning, and high-contrast bars on a gray background are simplified models for visual stimulation. Yet many things have been learned from experiments on such simplified empirical models, the results of which—often called 'data'—underlie our current primitive understanding of brain function.

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