CRCNS: Coordinating learning by top-down gating of plasticity in dendrites

CRCNS：通过树突可塑性的自上而下门控来协调学习

• 批准号: 10830625
• 负责人: AARON D MILSTEIN
• 金额: $ 41.36万
• 依托单位: RUTGERS BIOMEDICAL AND HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-14 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10830625
• 关键词: Behavior; Benchmarking; Biological; Biology; Brain; Brain region; Calcium Spikes; Cognitive; Complex; Computer Models; Cues; Dendrites; Discrimination; Discrimination Learning; Dopamine; Environment; Feedback; Future; Goals; Grain; Hippocampus; Image; Interneurons; Knowledge; Learning; Location; Machine Learning; Medial; Memory; Memory Disorders; Modeling; Mus; Network-based; Neurons; Outcome; Output; Pattern; Performance; Physiology; Population; Process; Regulation; Research; Rewards; Role; Sensory; Sensory Process; Shapes; Signal Transduction; Special Event; Stimulus; Synapses; Synaptic plasticity; System; Techniques; Testing; Therapeutic Intervention; Work; artificial neural network; awake; behavioral outcome; candidate identification; cognitive function; computational neuroscience; design; entorhinal cortex; experimental study; hippocampal pyramidal neuron; improved; in vivo; innovation; insight; learning algorithm; network models; neural circuit; neuronal circuitry; neuroregulation; sensory input; supervised learning; synergism; theories

There is a central problem in biological learning known as the “credit assignment problem”: how does information about the outcome of a decision or behavior modify the right synapses in the right neurons across multiple brain regions to improve future performance? The standard solution to this problem in artificial neural networks is to perform direct gradient descent, which minimizes error in the output of a network by precisely adjusting the strengths of every connection in proportion to that error. However, it is unlikely that the brain is able to compute the impact of each synapse on performance error and “backpropagate” fine-grained error signals across multiple layers of neuronal circuitry to every synapse. Recent work identified a new candidate biological mechanism for supervised learning in the brain. In addition to “bottom-up” connections that process sensory inputs, neurons also send “top-down” connections to the dendrites of neurons in lower layers. This feedback drives special events called “dendritic calcium spikes” that induce a potent form of synaptic plasticity and cause neurons to become selective for stimulus features in as few as a single trial, a phenomenon called “one-shot learning.” This project aims to develop new learning theory inspired by these experimental observations, and to experimentally test predictions of this theory in awake, behaving mice to better understand how top-down instructive signals in the brain coordinate learning across multiple layers of neuronal circuitry by regulating dendritic calcium spiking and associated plasticity. The team synergizes expertise in neuronal cellular and synaptic physiology, systems and computational neuroscience, and machine learning to better understand an important cognitive function - memory formation during goal-directed learning. A major objective is to develop and critically test a new theory of learning based on the regulation of dendritic calcium spikes and associated synaptic plasticity. Computational modeling will directly inform the proposed experiments, which entail imaging and manipulating neuronal population activity in vivo during spatial foraging behavior in mice. Preliminary results suggest that incorporation of these insights from biology into artificial neural networks leads to enhanced performance compared to standard techniques, highlighting the transformative potential of the proposed approach.

在生物学学习中有一个中心问题，称为“学分分配问题”： 关于决策或行为结果的信息会改变正确神经元中的正确突触 来提高未来的表现吗这个问题的标准解决方案是 人工神经网络是执行直接梯度下降，这最大限度地减少了输出的误差， 通过精确地调整每个连接的强度与该误差成比例来构建网络。但据 大脑不太可能计算出每个突触对性能错误的影响， “反向传播”细粒度的错误信号通过多层神经元电路到达每个突触。 最近的工作确定了大脑中监督学习的新候选生物机制。在 除了处理感觉输入的“自下而上”的连接外，神经元还发送“自上而下”的信息。 与下层神经元树突的连接。这种反馈推动了称为 “树突状钙尖峰”，诱导突触可塑性的有效形式，并导致神经元成为 选择性的刺激功能，在短短的一个单一的试验，一种现象称为“一次性学习”。这 该项目旨在开发新的学习理论，这些实验观察的启发，并 在清醒的、行为正常的老鼠身上实验性地测试这一理论的预测，以更好地理解自上而下的 大脑中的指导信号通过调节神经元回路的多个层来协调学习， 树突状钙尖峰和相关的可塑性。 该团队协同神经元细胞和突触生理学，系统和计算 神经科学和机器学习，以更好地了解一个重要的认知功能-记忆 在目标导向学习中形成。一个主要目标是发展和批判性地测试一个新的理论， 基于树突钙峰和相关突触可塑性的调节的学习。 计算建模将直接为拟议的实验提供信息，这些实验需要成像和 在小鼠的空间觅食行为期间操纵体内神经元群体活性。初步 结果表明，将这些来自生物学的见解结合到人工神经网络中， 与标准技术相比，性能得到增强，突出了 建议的方法。