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

我们提出了一个电路级的原理，揭示了大脑是如何获得情节记忆和重新处理的 将它们转换成紧凑、高效的“模式”：体验的属性或“内容”主要表现在 新大脑皮层(NC)的深层，而表层专门用于编码 属性发生的位置。浅层语境代码与深层属性代码之间的突触联系 允许语境唤起适当的属性输出，从而实现记忆回忆和预测性行为。 海马体(HC)对于记忆的获取和它们的再处理是必不可少的， 世界的示意图。NC表现出密集的局部但稀疏的远程连接，这 严重限制了它快速、远距离联想的能力。HC可能会通过合并 大脑当前内部状态的总体(即，感觉输入和内部变量，如饥饿、恐惧、 当前目标)被映射到NC并与其当前的、分布的、 属性表示法。索引代码的检索调用相应的属性。这样的HC-精心策划的 检索可以实现NC电路的逐步重新布线，其方式捕捉 经验，与深度的、人工的、神经网络通过小连接逐渐学习的方式大致相同 权重调整由输入的整体统计信息指导。我们关于星体层流划分的假设 这一过程中的人工是基于这样的事实，即HC输出主要指向NC的上层，这 实施一种在HC病变后丢失的空间编码方案；NC的更深层经常 对感觉输入表现出比表面输入更强的反应和辨别能力。 我们建议从深层和表层同时记录细胞水平、神经系综的活动 利用高密度电生理记录，在初级皮质和联合皮质中进行分层。首先，我们试图 通过证明深层细胞随着发射位置的改变而改变它们的发射位置，从而建立属性与指数的关系 相关的感官属性，而表层细胞则不是。接下来，我们测试假设，作为NC 积累了大量不同的经验，更深层次的属性表示变得更加稀疏 并且更明确地组织，而表层编码相对不变。要做到这一点， 我们采用了最新的化学遗传学进展，使我们能够获得大量静息状态的细胞 数据，我们预计在其中预测的变化将最容易观察到。我们还探讨了以下统计数据 兴奋性-抑制性细胞功能连接可能是这种编码统计变化的基础。预期中的 对大脑皮层记忆和图式编码电路的理解的进展最终将改善临床 对记忆和认知障碍的评估和干预。

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.