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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH087631-04
Application #
8429512
Study Section
Neurobiology of Learning and Memory Study Section (LAM)
Program Officer
Asanuma, Chiiko
Project Start
2010-07-01
Project End
2015-01-31
Budget Start
2013-04-01
Budget End
2014-03-31
Support Year
4
Fiscal Year
2013
Total Cost
$359,736
Indirect Cost
$91,827
Name
University of Connecticut
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
614209054
City
Storrs-Mansfield
State
CT
Country
United States
Zip Code
06269
Bannon, Nicholas M; Chistiakova, Marina; Chen, Jen-Yung et al. (2017) Adenosine Shifts Plasticity Regimes between Associative and Homeostatic by Modulating Heterosynaptic Changes. J Neurosci 37:1439-1452
Ghanbari, Abed; Malyshev, Aleksey; Volgushev, Maxim et al. (2017) Estimating short-term synaptic plasticity from pre- and postsynaptic spiking. PLoS Comput Biol 13:e1005738
Luong, Lucas; Bannon, Nicholas M; Redenti, Andrew et al. (2017) Very low concentrations of ethanol suppress excitatory synaptic transmission in rat visual cortex. Eur J Neurosci 45:1333-1342
Volgushev, Maxim; Chen, Jen-Yung; Ilin, Vladimir et al. (2016) Partial Breakdown of Input Specificity of STDP at Individual Synapses Promotes New Learning. J Neurosci 36:8842-55
Volgushev, Maxim (2016) Cortical Specializations Underlying Fast Computations. Neuroscientist 22:145-64
Malyshev, Aleksey; Goz, Roman; LoTurco, Joseph J et al. (2015) Advantages and limitations of the use of optogenetic approach in studying fast-scale spike encoding. PLoS One 10:e0122286
Zhang, Pei; Bannon, Nicholas M; Ilin, Vladimir et al. (2015) Adenosine effects on inhibitory synaptic transmission and excitation-inhibition balance in the rat neocortex. J Physiol 593:825-41
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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