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Neural circuit theory and trained recurrent network modeling of rapid learning

神经回路理论与快速学习的训练循环网络建模

基本信息

批准号：
9983227
负责人：
XIAO-JING WANG
金额：
$ 22.33万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9983227
关键词：
Algorithms Animals Area Artificial Intelligence Back Base of the Brain Behavioral Biological Brain Categories Code Cognition Computer Simulation Data Development Dimensions Frontotemporal Dementia Future Goals Hippocampus (Brain)Human Knowledge Learning Lesion Location Machine Learning Measures Memory Metaplasia Modeling Monkeys Neural Network Simulation Neurons Neurosciences Population Dynamics Prefrontal Cortex Primates Principal Component Analysis Process Protocols documentation Psyche structure Psychological reinforcement Recurrence Research Role Sampling Semantic memory Semantics Sensory Stimulus Structure Study models Synapses Testing Thinness Time Training Weight base brain research classical conditioning cognitive task computational neuroscience computer framework experimental study flexibility frontier insight learning algorithm network models neural circuit neural network neurophysiology nonhuman primate recurrent neural network relating to nervous system supervised learning theories tool two-dimensional

项目摘要

Humans have remarkable ability to acquire a rich repertoire of concepts stored in semantic memory, which can be deplored in “learning to lean” that facilitates rapid new learning or even one-shot learning. Nonhuman animals are also endowed with “learning to learn”; on the other hand, there is evidence that primates but not rodents possess this mental capability. The underlying brain mechanisms are completely unknown and represent a widely open question at the frontier of Neuroscience today. The present computational project, in conjecture with the experimental projects of this application, has the primary goal of elucidating the neural circuit basis of rapid learning. Progress is this research direction will represent a major step forward in bridging nonhuman primate and human neuroscientific understanding of higher cognition. Our modeling approach integrates large-scale circuit modeling of primate brain based on measured mesoscopic connectivity and training recurrent neural networks to perform cognitive tasks. Together with the proposed experiments in this application, we will develop tools to describe and elucidate neural population dynamics in single trials, which is crucial for neurophysiological analysis of rapid learning (even one-shot learning) without averaging over many repetitive trials in a steady state situation. The main hypothesis is that learning to learn depends on the formation of an abstraction of sensori-motor representations, such as that of task structure or “schema”, which is manifested in a shift of neural representation from the hippocampus to the prefrontal cortex; this conceptual representation enables rapid future learning by efficient changes of connection weights within a low dimensional subspace. This hypothesis will be tested using the state space analysis and dimensionality reduction of the recurrent neural network dynamics. Aim 1 will to be to advance a mesoscopic connectivity-based multi-regional neural network model for rapid learning in categorization, flexible sensori-motor mapping and object-location association. The model will be systematically tested and validated by comparison with behavioral data from category learning and associative learning tasks. Aim 2 will be to uncover neural population dynamics and circuit mechanism of rapid learning in single trials, using state-space analysis and identifying a subspace of neural population dynamics as well as a subspace of connection weights that may correspond to the formation of semantic memory. Aim 3 will be to dissect the differential roles of HPC, PFC, PPC and their dynamical interactions underlying rapid learning, by simulating “area lesion” at different time points of a learning process. A spiking network version of our model will enable us to uncover inter-areal dynamical interactions and their role in rapid learning. Advances in this area would not only be important for the Neuroscience of learning and memory, but also have potentially major implications for the future development of AI, and for shedding insights into the brain mechanism of deficits in semantic memory, which is at the core of fronto-temporal dementia.

人类在获取存储在语义记忆中的丰富概念方面具有非凡的能力，