Algorithms for learning in the mammalian neocortex

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Despite abundant evidence that plasticity can occur in the neocortex, we lack a coherent account of how experience transforms the properties and connections between specific cell types across the cortical column. Prior studies have employed sensory deprivation or different stimulation paradigms, using animals of widely varying ages, and different time windows for analysis, and focused on neurons in one or a few different layers of the neocortex, making across-study integration of findings difficult. Furthermore, new findings reveal stereotypic, highly-specific roles for different neuronal subtypes in the transformation of sensory input. These data suggest that experience-dependent plasticity may be initiated by changes in particular neural classes to gate long-lasting changes in pyramidal neuron response properties. Defining the role of different neuronal subtypes during learning has only recently become possible, due to the development of transgenic mice that enable targeted analysis of molecularly-defined subclasses of cells. We are investigating the sequence by which specific synapses between identified cell types are changed during a sensory-association task, using an automated behavioral training set-up that couples whisker stimulation to water reward followed by targeted recordings in transgenic mice for cell-type specific analysis. Using sophisticated anatomical and electrophysiological methods to evaluate changes in connectivity, synaptic strength and neuronal firing in this sensory-association task, we are identifying canonical principles for sensory information processing and plasticity in the neocortex.