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Neural and Computational Architecture for Complex Navigation and Subjective Self-Location

用于复杂导航和主观自定位的神经和计算架构

基本信息

批准号：
10664227
负责人：
Jean-Paul Noel
金额：
$ 12.69万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10664227
关键词：
Accounting Adaptive Behaviors Animals Architecture Area Award Behavior Behavioral Belief Biological Biological Psychiatry Brain Cells Code Cognition Cognitive Cognitive Science Communication Complement Complex Coupling Darkness Data Collection Data Science Development Plans Disease Disparity Environment Feedback Fireflies Foundations Genetic Variation Goals Grant Health Hippocampal Formation Hippocampus Human Human Resources Illusions Institution International Knowledge Link Location Maps Measures Mediating Mentors Modeling Motion Motor Movement Mus Neurons Neurosciences Personal Space Phase Photons Physiology Positioning Attribute Prize Process Research Retina Rodent Rodent Model Schizophrenia Scientist Sensory Signal Transduction System Techniques Technology Testing Training Visual Motion Work Writing autism spectrum disorder career career development cranium design experience member mouse model multisensory neglect neural neural circuit neurophysiology nonhuman primate novel novel therapeutics object motion optic flow phenomenological models place fields preference receptive field skills virtual virtual reality visual motor way finding

项目摘要

Project Summary Candidate and Career Goals: I intend to become an independent scientist at a research-first academic institution, bridging across levels of description (i.e., from computations to neurons) and furthering our understanding of how the brain infers the world around us, as well as ourselves within it. I trained as a cognitive scientist (winning the Glushko Prize for best dissertation in Cognitive Science, worldwide), with a focus on understanding our sense of self-location; where am “I” located in space. I am now training in systems neuroscience, developing expertise in large-scale rodent neurophysiology and with a focus on the dynamic aspects of self-location; spatial navigation. These experiences complement each other; from behavioral computations to single-units, and from static to dynamic self-location. Environment and Career Development Plan: I am mentored by Dr. Dora Angelaki (NYU, expertise in navigation) and co-mentored by Drs. David Schneider (NYU, rodent self-generated actions) and Cristina Savin (NYU, data science). Further, I am a scientist member of the International Brain Lab, allowing me the opportunity to leverage world-class expertise (22 labs) in rodent neurophysiology. My training during the K99 will focus on model-based analyses of behavior and neurons during continuous and complex naturalistic tasks, as well as lab management skills (i.e., personnel, grant-writing, communication). Research Plan: Spatial navigation is central to adaptive behavior, underlying our ability to trade-off the exploitation of our current location with the exploration of novel ones. Beautiful work has detailed a number of spatial codes (e.g., place and grid cells) in the hippocampal formation, yet we (1) lack a normative framework accounting for the complexities of natural navigation, (2) do not understand how spatial codes from the hippocampal formation interact with cortex, and (3) have focused on understanding how we build internal models of the world around us, while neglecting its starting point – ourselves. During the K99 phase of the award, I will develop a naturalistic navigation task in virtual reality where rodents will be required to disambiguate complex signals. These animals will be trained to integrate velocity signals derived from motion across their retina (i.e., optic flow) into a position estimate, in order to path integrate to the location of a latent target. Then, they will be tested in a novel situation, one where optic flow may be caused by self- and/or target-motion. I expect animals to behave in line with Bayesian Causal Inference (BCI) – a canonical computation wherein estimation biases emerge during small, but not large, signal disparities (i.e., when observers operate under the incorrect internal model). Further, I will broadly map neural activity throughout the rodent’s brain during BCI by leveraging novel large-scale neurophysiology techniques. During the R00 phase of the award, I will directly manipulate the subjective sense of self-location – the initial condition for navigation - and measure this phenomenology in rodents via the task developed during the K99. Beyond establishing BCI as a fundamental computation guiding naturalistic navigation, I expect the proposed project to inform next-generation therapeutics for disorders of inference, such as Autism and Schizophrenia.

项目总结