The laminar organization of 'index' versus 'attribute' coding in neocortex

新皮质中“索引”与“属性”编码的层状组织

• 批准号: 10205913
• 负责人: BRUCE L MCNAUGHTON
• 金额: $ 198.83万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10205913
• 关键词: 3-Dimensional; Adaptive Behaviors; Aging; Behavior; Body Weight Changes; Brain; Categories; Cells; Characteristics; Clinical assessments; Code; Cognition; Cognition Disorders; Cognitive deficits; Complex; Coupling; Data; Disease; Electrophysiology (science); Episodic memory; Event; Exhibits; Fright; Goals; Hippocampal Formation; Hippocampus (Brain); Hunger; Image; Intelligence; Intervention; Knowledge; Lead; Lesion; Light; Link; Location; Memory; Memory Disorders; Memory impairment; Motor; Mus; Neocortex; Neurons; Output; Pattern; Process; Property; Recurrence; Rest; Retrieval; Scheme; Schizophrenia; Sensory; Silicon; Sleep; Structure; Supporting Cell; Synapses; Testing; Trauma; Weight; Work; artificial neural network; association cortex; autism spectrum disorder; base; density; detector; experience; experimental study; improved; indexing; information processing; learning network; memory acquisition; memory recall; neural circuit; relating to nervous system; sensory discrimination; sensory input; statistics; theories; trend; two-photon

We propose a circuit-level principal underlying how brains acquire 'episodic' memories and reprocess them into compact, efficient 'schemas': The attributes or 'contents' of experience are represented primarily in the deeper layers of neocortex (NC), whereas the superficial layers are dedicated to encoding the contexts in which the attributes occur. Synaptic associations between superficial context codes and deep attribute codes permit contexts to evoke appropriate attribute output hence enabling memory recall and predictive behavior. The hippocampus (HC) is essential for acquisition of memories and for their reprocessing into efficient, schematic representations of the world. NC exhibits dense local but sparse long-range connectivity, which severely limits its ability to make rapid, long-range associations. HC likely solves this dilemma by merging the totality of the brain's current internal state (i.e., sensory input and internal variables such as hunger, fear, current goals) into a unique, 'index' code that is projected to NC, and associated with its current, distributed, attribute representation. Retrieval of an index code evokes the corresponding attributes. Such HC-orchestrated retrieval may enable the gradual rewiring of NC circuitry in a manner that captures the overall statistics of experience, much the same way as deep, artificial, neural networks learn incrementally by small connection weight adjustments directed by the overall statistics of the input. Our hypothesis on the laminar division of labor in this process is based on the facts that HC output is directed primarily to upper layers of NC, which implements a 'spatial' coding scheme that is lost after HC lesions; and that the deeper layers of NC frequently exhibit more robust responsiveness to and discrimination of sensory inputs than the superficial ones. We propose to record cellular level, neural ensemble activity simultaneously from deep and superficial layers in primary and association cortex, using high-density, electrophysiological recording. First, we attempt to establish the 'attribute vs index' principal by showing that deep cells shift their firing locations with shifts in the relevant sensory attributes, whereas superficial cells do not. Next we test the hypothesis that, as NC accumulates large amounts of diverse experience, attribute representations in deeper layers becomes sparser and more categorically organized, whereas superficial layer coding is relatively unchanged. To accomplish this, we employ a recent chemogenetic advance that enables us to acquire large amounts of resting-state cellular data, in which we expect the predicted changes will be most easily observed. We also explore the statistics of excitatory-inhibitory cell functional connectivity that may underlie such coding statistics changes. The expected advances in understanding cortical memory and schema encoding circuits will ultimately improve clinical assessment of, and intervention in memory and cognitive disorders.

我们提出了一个回路水平的原则，它是大脑如何获得“情节”记忆和再加工的基础。 经验的属性或“内容”主要表现在 新皮层（NC）的深层，而表层则致力于编码上下文， 这些属性的出现。表层语境代码与深层属性代码的突触关联 允许上下文唤起适当的属性输出，从而实现记忆回忆和预测行为。 海马体（HC）对于记忆的获得和将其重新加工成有效的， 世界的示意图。NC具有密集的局部但稀疏的长程连通性， 严重限制了它进行快速、长距离联想的能力。HC可能通过合并 大脑当前内部状态的总体（即，感官输入和内部变量，如饥饿，恐惧， 当前目标）转换为唯一的“索引”代码，该代码被投影到NC，并与NC的当前，分布式， 属性表示检索索引代码会调用相应的属性。这样的HC编排 检索可以使得能够以捕获NC电路的总体统计的方式逐渐重新布线。 经验，就像深层的人工神经网络通过小连接逐渐学习一样 权重调整由输入的总体统计信息指导。我们关于 这一过程中的劳动力是基于这样一个事实，即HC输出主要针对NC的上层， 实施了一个“空间”编码方案，在HC病变后丢失; NC的深层经常 对感觉输入的反应和辨别能力比表面的要强。 我们建议从深层和浅层同时记录细胞水平、神经整体活动 层的初级和联合皮质，使用高密度，电生理记录。首先，我们试图 建立“属性与索引”的原则，通过显示深层细胞随着 相关的感觉属性，而表面细胞没有。接下来，我们测试假设，作为NC 积累了大量不同的经验，更深层的属性表示变得稀疏 并且更分类地组织，而表面层编码相对不变。为了实现这一点， 我们利用最近的化学发生学进展，使我们能够获得大量的静息状态的细胞， 数据，在这些数据中，我们预计预测的变化将最容易观察到。我们还研究了 兴奋性-抑制性细胞的功能连接，可能是这种编码统计变化的基础。预期 在理解皮层记忆和图式编码电路方面的进展将最终改善临床 评估和干预记忆和认知障碍。

项目成果

Learning in deep neural networks and brains with similarity-weighted interleaved learning.

• DOI: 10.1073/pnas.2115229119
• 发表时间: 2022-07-05
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Saxena, Rajat;Shobe, Justin L.;McNaughton, Bruce L.
• 通讯作者: McNaughton, Bruce L.

Cortical reactivation of spatial and non-spatial features coordinates with hippocampus to form a memory dialogue.

• DOI: 10.1038/s41467-023-43254-7
• 发表时间: 2023-11-27
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Chang, HaoRan;Esteves, Ingrid M;Neumann, Adam R;Mohajerani, Majid H;McNaughton, Bruce L
• 通讯作者: McNaughton, Bruce L