The development and plasticity of the nervous system involve activity-dependent modification of synaptic connections. Using simple networks of hippocampal neurons in culture, the applicant recently found that long-term depression (LTD) induced by repetitive synaptic activity at one synaptic site is accompanied by an extensive but selective propagation of the depression to other synapses within the network. The P.I. now proposes to characterize further this phenomenon of propagation of synaptic modification and to examine underlying cellular and molecular mechanisms. The experiments in Aim 1 will determine the conditions by which LTD and long-term potentiation (LTP) can be reliably induced at glutamatergic and GABAergic synapses in hippocampal cultures and the mechanisms underlying the induction and expression of these modifications. The experiments in Aim 2 will further confirm the earlier findings on the back- and lateral propagation of synaptic depression following the induction of LTD and extend these studies to include the propagation of synaptic modification accompanying the induction of LTP. The applicant will determine the time course, extent and persistence of propagated changes, their dependence on the distance from the site of LTP/LTP induction, and pre-and/or postsynaptic mechanisms underlying the changes at the propagated site.
In Aim 3, the applicant will examine whether a synapse can achieve temporal integration of multiple modulatory signals propagated from another synapse associated with the same neuron which is undergoing sequential LTD/LTP and spatial integration of multiple signals propagated from different synapses undergoing separate LTD/LTP. Finally, in Aim 4 , the applicant will study the involvement of various forms of extra-and intracellular signaling mechanisms in the propagation of synaptic modification following the induction of LTD/LTP. Together, these studies address several fundamental issues concerning the distribution of activity-induced modifications within a neural network. Multiple whole-cell recording from defined neural networks pioneered in this project promises to uncover previously unknown network properties relevant to our basic understanding of the nervous system.
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