RI: Small: Theory of Robust Learning Based on the Structure and Function of the Cortical Column

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How the brain learns, and in the process modifies its synaptic connectivity, remains one of the greatest mysteries of modern science. The objective of this project is to uncover the effects of robust associative learning and long-term memory storage on synaptic connectivity, thus creating the basis for quantitative analyses of these fundamental brain functions. The investigator proposes to develop a biologically realistic model of robust associative learning by cortical circuits. The model will be derived from a single hypothesis, according to which synaptic connectivity in a given circuit of adult cortex is functioning in a steady-state. In such a state the associative memory storage capacity of the circuit is maximal, and learning new associations is accompanied with forgetting some of the old ones. The model will integrate current knowledge of excitatory and inhibitory neuron classes, with structural connectivity constraints imposed by the morphologies of axonal and dendritic arbors of cortical neurons, with homeostatic constraints on numbers and strengths of synaptic connections. It is proposed to simulate steady-state learning based on one of the best studied networks in the mammalian neocortex - the barrel-centered column of rodent somatosensory cortex. The simulations will be imbedded in the structural connectivity of the column, built from the morphologies of neurons reconstructed in three-dimensions from various cortical depths. Salient features of steady-state circuits will be validated against a large dataset of experimental studies reporting probabilities of connections between neurons, probabilities of specific higher-order connectivity motifs, distributions of unitary postsynaptic potentials, as well as relative strengths of laminar and inter-laminar projections in rodent barrel cortex. The dataset will be created as part of the project and will encompass connectivity of major excitatory and inhibitory cell classes present in all cortical layers. The proposed research is rooted in the basic principles of statistical learning and will advance the state of the art in theoretical and computational modeling of cognitive functions with basic neuroscience and computational intelligence applications.