The ability to tell time, predict the movement of other animals, and decode complex temporal patterns- such as those in speech-are among the brain's primary functions. As such, we would put forth that it will not be possible to understand the fundamental principles underlying brain function without understanding how the brain tells time and processes temporal information. We have proposed that precisely because timing is such a fundamental component to brain function that neural circuits in general are capable of processing temporal information-that is, timing does not rely on specialized or centralized circuits. This theory posits that timing emerges from the neural dynamics of recurrent neural circuits, and predicts that even neural circuits in vitro may be able to learn to tell time. We hae recently provided evidence that timing is a general computation of cortical networks. Specifically, by chronically exposing slices in the incubator to patterned stimuli (mimicking sensory experience) we have shown that the neural dynamics reproduces the temporal features of the experienced stimuli. Here we will use novel electrical and optogenetic methods to demonstrate that cortical circuits in vitro can learn temporal patterns, and elucidate the underlying neural mechanisms of timing and cortical computations. We propose that studying network-level forms of learning in reduced preparations is necessary to understand cortical computations because of the limitations of in vivo studies. Additionally, the ability to study network behavior and `learning' in vitro will provide a means to study pathological circuit level computations using tissue from animal models of cognitive disorders.

Public Health Relevance

The ability to tell time and process temporal information is of fundamental importance to sensorimotor processing and learning. Yet, relatively little is known about the neural mechanisms underlying our ability to tell time, and it is increasingly clear that this represents a bottleneck for our understanding of the neural basis of learning, behavior, and neurological disorders. Thus elucidating both normal and pathological brain function will require that we unveil the mechanisms that allow the brain to tell time. The current project focuses on this problem, not only by directly studying temporal processing, but by establishing a novel framework to explain how complex computations emerge from the dynamics of recurrent neural circuits.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH060163-15
Application #
9232206
Study Section
Neurobiology of Learning and Memory Study Section (LAM)
Program Officer
Ferrante, Michele
Project Start
2001-09-22
Project End
2020-02-29
Budget Start
2017-03-01
Budget End
2018-02-28
Support Year
15
Fiscal Year
2017
Total Cost
$339,001
Indirect Cost
$114,001
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
Hardy, N F; Buonomano, Dean V (2018) Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model. Neural Comput 30:378-396
Motanis, Helen; Seay, Michael J; Buonomano, Dean V (2018) Short-Term Synaptic Plasticity as a Mechanism for Sensory Timing. Trends Neurosci 41:701-711
Paton, Joseph J; Buonomano, Dean V (2018) The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions. Neuron 98:687-705
Hardy, Nicholas F; Goudar, Vishwa; Romero-Sosa, Juan L et al. (2018) A model of temporal scaling correctly predicts that motor timing improves with speed. Nat Commun 9:4732
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
Goel, Anubhuti; Buonomano, Dean V (2016) Temporal Interval Learning in Cortical Cultures Is Encoded in Intrinsic Network Dynamics. Neuron 91:320-7
Hardy, Nicholas F; Buonomano, Dean V (2016) Neurocomputational Models of Interval and Pattern Timing. Curr Opin Behav Sci 8:250-257
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

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