The brain is an inherently dynamic system, it evolved under strong selective pressures to allow animals to interact with the environment in real-time, and predict and prepare for future events. For these reasons, understanding neural dynamics, and how the brain tells and encodes time is fundamental to understanding brain function. The importance of neural dynamics and timing to brain function emphasizes the need for techniques that allow for the collection and analysis of massively parallel single neuron recordings across multiple structures in behaving animals. This project will combine novel electrophysiological, behavioral, analytical and computational methods to reverse engineer the neural circuits underlying learning and timing.
The first aim i s to combine large-scale neural recordings with computational approaches to determine how time is represented in the striatum and prefrontal cortex, two interacting brain areas that are closely implicated in temporal processing. We will specifically examine whether encoding of time relies on absolute, relative, or stimulus-specific coding mechanisms. Recordings will be carried out in awake, head-fixed mice trained on a classical trace reward conditioning task in which two cues predict reward with a different delay period. When animals learn the cue-reward association, they engage in robust anticipatory licking that precedes the reward presentation; moreover, the timing of this behavior is dependent on the cue-reward delay time.
The second aim i s to combine electrophysiology and optogenetics to determine if temporal coding in the striatum and prefrontal cortex is perturbed by transiently disrupting network activity. The hypothesis is that if dynamics of the timing circuits are perturbed then the ensuing activity patterns will be irreversibly altered, thus reducing the accuracy or precision of timed behavioral responses.
The third aim i s to develop a novel computational framework based on recurrent neural networks models that can predict future patterns of neural ensemble activity based on present patterns. The ultimate goal of this work is to integrate highly innovative electrophysiological and computational methods for reverse engineering brain circuit function at the level of networks of hundreds of neurons in the striatum and prefrontal cortex.
Learning to produce appropriately timed actions is fundamental to many aspects of behavior, and disruption of the brain circuits underlying this process is implicated in many neurological and psychiatric disorders. This project will develop an integrated approach to studying the mechanisms of timed motor behavior by combining large-scale neural recordings from multiple brain areas and computational modeling of neural networks.
|Lee, Kwang; Holley, Sandra M; Shobe, Justin L et al. (2018) Parvalbumin Interneurons Modulate Striatal Output and Enhance Performance during Associative Learning. Neuron 99:239|
|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|
|Li, Bingzhao; Lee, Kwang; Masmanidis, Sotiris C et al. (2018) A nanofabricated optoelectronic probe for manipulating and recording neural dynamics. J Neural Eng 15:046008|
|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|
|Shobe, Justin L; Bakhurin, Konstantin I; Claar, Leslie D et al. (2017) Selective Modulation of Orbitofrontal Network Activity during Negative Occasion Setting. J Neurosci 37:9415-9423|
|Lee, Kwang; Holley, Sandra M; Shobe, Justin L et al. (2017) Parvalbumin Interneurons Modulate Striatal Output and Enhance Performance during Associative Learning. Neuron 93:1451-1463.e4|