Every neuron in the neocortex receives thousands of synapses from thousands of other neurons. Activation of only a portion of them, dozens to hundreds, may evoke cell firing and under certain conditions induce plasticity. Input-specific associative plasticity is believed to be the synaptic mechanism of learning and memory. However, just as new learning always takes place on a background of existing memories, so synaptic plasticity is always induced on a background of existing distribution of synaptic weights. To understand how neurons achieve new learning while preserving existing information, we need to know, how the induction of plasticity at a specific group of synapses interacts with the existing pattern of synaptic weights. It is crucial, therefore, to understand the rules that govern heterosynaptic plasticity i.e. changes at synapses which were not active during plasticity induction. Our proposal is aimed at this question. Using in vitro slices of rodent neocortex, we will record excitatory postsynaptic potentials in major types of neocortical neurons: pyramids from layer 2/3 and 5, spiny cells from layer 4 and inhibitory interneurons. We will study plastic changes, induced in these cells by intracellular tetanization - bursts of short depolarizing pulses that evoke in vivo-like firing patterns in the postsynaptic neuron without presynaptic activity. We will ask, how heterosynaptic plasticity is induced at different types of synapses (Aim 1), how it interacts with plasticity induced by temporal coincidence (pairing) or afferent tetanization, and why it leads to mixed effects: potentiation, depression or no change (Aim 2). We will implement the rules derived in the above experiments in detailed models of major types of neocortical neurons. With these models we will examine changes of synaptic weights and their distribution during patterns of input activity typical for neurons in vivo and during multiple applications of plasticity induction protocols (Aim 3). This combined experimental and theoretical approach will allow us to achieve the long-term goal of the proposal: to understand how single neurons combine the ability for learning new while retaining existing memory traces, and how heterosynaptic plasticity helps to resolve this dilemma. This new knowledge will stimulate research and understanding of mechanisms of disorders that affect learning new and remembering previously learned information by humans. The National Center for Learning Disabilities estimates that five percent of the United States population or fifteen million people are affected by learning disorders. Three million school students receive special help because of learning disabilities. Research of learning disorders and development of new therapies will improve the quality of life of the affected people and bring economic benefits from healthcare.
The National Center for Learning Disabilities estimates that five percent of the United States population, or fifteen million people are affected by learning disorders. The goal of this project is to understand how neurons achieve new learning while preserving the old memories. This new knowledge will stimulate research and understanding of mechanisms of disorders of learning and remembering in humans and development of new diagnostic tools and therapies.
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|Volgushev, Maxim (2016) Cortical Specializations Underlying Fast Computations. Neuroscientist 22:145-64|
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|Volgushev, Maxim; Ilin, Vladimir; Stevenson, Ian H (2015) Identifying and tracking simulated synaptic inputs from neuronal firing: insights from in vitro experiments. PLoS Comput Biol 11:e1004167|
|Chistiakova, Marina; Bannon, Nicholas M; Chen, Jen-Yung et al. (2015) Homeostatic role of heterosynaptic plasticity: models and experiments. Front Comput Neurosci 9:89|
|Ilin, Vladimir; Stevenson, Ian H; Volgushev, Maxim (2014) Injection of fully-defined signal mixtures: a novel high-throughput tool to study neuronal encoding and computations. PLoS One 9:e109928|
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