Cortical computations are the result of neural dynamics: the spatial-temporal patterns created by the flow of activity through cortical networks. It is from neural dynamics that the computations that underlie cognition emerge. Additionally, it is ultimately not the effect genes or cells in isolation that underlie cognitive abnormalities, such as those observed in neurofibromatosis or autism, but how they alter function at the network level. Over the past decades significant progress has been made regarding the learning rules governing synaptic strength, as well as in the description of experience-dependent changes in cortical processing using in vivo and imaging approaches. However, less progress has been made in bridging these levels of analyses;there is an explanatory gap in the ability of synaptic and cellular properties to account for the emergent properties of neural networks. Indeed, the mechanisms by which the properties of millions of synapses and thousands of neurons are adjusted to produce, not only a controlled (as opposed to epileptic) flow of activity, but a computation as a result of neural dynamics are not understood. The goal in the current proposal is to use cortical networks in vitro as a 'reduced preparation'to study the fundamental principals underlying neural processing and dynamics within local cortical networks. Cortical neurons develop selective responses to stimuli in an experience-dependent manner. This selectivity plays a fundamental role in sensory processing and pattern recognition, and appears to develop independently of whether the stimuli are auditory, somatosensory or visual in nature. The learning rules responsible for the emergence of selectivity are presumably not coherently engaged in traditional in vitro preparations, since these normally 'develop'in the absence of any input structure, much like the visual or auditory system being deprived of patterned input. We will chronically expose cortical networks in vitro to simple input patterns to address a number of fundamental issues, including: 1) whether neurons in vitro can develop stimulus-selective responses in an 'experience-dependent'fashion, and 2) to elucidate the computational role of spontaneous network dynamics. By advancing our understanding of how sensory-experience shapes network function and dynamics, this research will contribute to the elucidation of both normal and pathological cortical function. Furthermore the development of an in vitro model of cortical function will prove valuable to the development of experimental models for diseases affecting cortical function, such as neurofibromatosis and Alzheimer's disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH060163-09
Application #
7807986
Study Section
Neurobiology of Learning and Memory Study Section (LAM)
Program Officer
Glanzman, Dennis L
Project Start
2001-09-22
Project End
2012-03-31
Budget Start
2010-04-01
Budget End
2011-03-31
Support Year
9
Fiscal Year
2010
Total Cost
$308,950
Indirect Cost
Name
University of California Los Angeles
Department
Neurosciences
Type
Schools of Medicine
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Bakhurin, Konstantin I; Goudar, Vishwa; Shobe, Justin L et al. (2017) Differential Encoding of Time by Prefrontal and Striatal Network Dynamics. J Neurosci 37:854-870
Hardy, N F; Buonomano, Dean V (2017) Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model. Neural Comput :1-19
Hardy, Nicholas F; Buonomano, Dean V (2016) Neurocomputational Models of Interval and Pattern Timing. Curr Opin Behav Sci 8:250-257
Goel, Anubhuti; Buonomano, Dean V (2016) Temporal Interval Learning in Cortical Cultures Is Encoded in Intrinsic Network Dynamics. Neuron 91:320-7
Goudar, Vishwa; Buonomano, Dean V (2015) A model of order-selectivity based on dynamic changes in the balance of excitation and inhibition produced by short-term synaptic plasticity. J Neurophysiol 113:509-23
Goel, Anubhuti; Buonomano, Dean V (2014) Timing as an intrinsic property of neural networks: evidence from in vivo and in vitro experiments. Philos Trans R Soc Lond B Biol Sci 369:20120460
Goel, Anubhuti; Buonomano, Dean V (2013) Chronic electrical stimulation homeostatically decreases spontaneous activity, but paradoxically increases evoked network activity. J Neurophysiol 109:1824-36
Chen, W X; Buonomano, D V (2012) Developmental shift of short-term synaptic plasticity in cortical organotypic slices. Neuroscience 213:38-46
Johnson, Hope A; Goel, Anubhuthi; Buonomano, Dean V (2010) Neural dynamics of in vitro cortical networks reflects experienced temporal patterns. Nat Neurosci 13:917-9
Buonomano, Dean V; Laje, Rodrigo (2010) Population clocks: motor timing with neural dynamics. Trends Cogn Sci 14:520-7

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