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High-probability grants
According to our matching algorithm, Luca Mazzucato is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2014 — 2017 |
Mazzucato, Luca |
K25Activity Code Description: Undocumented code - click on the grant title for more information. |
Spontaneous Activity in Gustatory Cortex @ State University New York Stony Brook
DESCRIPTION (provided by applicant): The PI is a faculty member of the Department of Neurobiology and Behavior at Stony Brook. His research focuses on spontaneous and evoked neural activity in the gustatory cortex. The candidate recently transitioned to neuroscience from a theoretical physics background. This award will provide the necessary five-year protected period for the candidate to receive training in systems and theoretical neuroscience and electrophysiological techniques, under the supervision of the proposed mentorship committee. By the end of the termed period, the candidate will achieve his long term career goals of successfully applying for independent research funding and becoming an independent investigator in the field of theoretical neuroscience. The goal of the proposed research plan is elucidating the relation between spontaneous and evoked activity in the gustatory cortex, and its modulation by anticipatory cues. Spontaneous neural activity in sensory cortices, traditionally regarded as a noisy baseline, is increasingly drawing attention: it can predict trial to trial variability; it contains information on the functional architecture of neural networks; and it was characterized as either a repertoire of possible network activities or a storage of expectations of sensory stimuli, shaped by development. In the context of taste processing, however, it has received little to no attention. The aim of this proposal is to elucidate the properties of spontaneous activity in the gustatory cortex. This is an ideal system, due to the rich temporal dynamics of responses to taste stimuli, represented in terms of taste-specific sequences of patterns of activity, synchronized across the whole neural population. The first goal of the proposed plan is to employ state-of-the-art machine learning techniques to elucidate the features of spontaneous activity in relation to evoked activity. This will provide new analytical tools for the characterization of spontaneous activity and reconcile within a single framework the different views currently held in the literature, by introducing the new feature of temporal dynamics. As a second goal of the proposed project, the role played by anticipatory cues in modulating the relation between spontaneous and evoked activity will be elucidated, and the dependence of reaction times on spontaneous activity will be clarified. A biologically plausible model of spontaneous activity in the gustatory cortex will be introduced, based on networks of spiking neurons. This model will yield a mechanistic understanding of the feature of spontaneous activity as revealed by the analysis of electrophysiological data.
|
1 |
2021 |
Mazzucato, Luca Niell, Cristopher M (co-PI) [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Crcns: a Mechanistic Theory of Serotonergic Modulation of Cortical Processing |
0.957 |