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.