Cortical computations rely on the spatiotemporal patterns of action potentials created by the flow of activity through cortical networks. It is from these patterns that the computations that underlie cognition emerge. But it is ultimately not genes or cells in isolation that underlie normal or abnormal cognition, but how these molecular and cellular processes govern the behavior of networks of neurons. Over the past decades significant progress has been made towards understanding critical synaptic and cellular mechanisms of neural plasticity, as well as in the description of experience-dependent changes in cortical processing using behavioral, in vivo, and imaging approaches. However, less progress has been made in bridging these levels of analyses;that is, 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 computations through neural dynamics are not understood. It is known, however, that a cardinal feature of cortical function is that throughout postnatal development neural circuits are sculpted by experience. Furthermore, abnormalities in experience-dependent plasticity contribute to a number of neurological disorders, ranging from learning disabilities to mental retardation. The learning rules responsible for the emergence of experience-dependent plasticity are presumably not coherently engaged in traditional studies using 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. Our goal is to use cortical networks in vitro as a 'reduced preparation'to study the fundamental principles underlying the experience-dependent sculpting of cortical circuits. Towards this goal we have recently described what we consider to be the first neural analog of learning in vitro. Specificall, 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 further demonstrate that cortical circuits in vitro can """"""""learn"""""""" temporal patterns, and elucidate the underlying neural mechanisms of timing and cortical computations. We suggest that a reduced preparation that provides an analog of learning and timing in vitro will ultimately prove to be required to study computations that truly emerge from the recurrent dynamics of neural networks. Additionally, by demonstrating that cortical networks are inherently capable of 'learning'temporal patterns, our experiments will address the long-standing question of the how the brain tells time. Furthermore, the ability to study network behavior and 'learning'in vitro should provide a means to study pathological circuit level computations using tissue from animal models of cognitive disorders.

Public Health Relevance

Human behavior and cognition rely on the computations that take place within local cortical networks composed of tens of thousands of neurons. Many neurological disorders, including some forms of mental retardation, schizophrenia, and autism, appear to be the consequence of abnormal development and processing within these circuits. It is unlikely we will be able to cure some neurological disorders without first elucidating some of the fundamental underpinnings of how cortical circuits perform computations. The current proposal is aimed at achieving this goal.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Neurobiology of Learning and Memory Study Section (LAM)
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Glanzman, Dennis L
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University of California Los Angeles
Schools of Medicine
Los Angeles
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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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