Highlights d Two distinct subtypes of excitatory neurons in superficial retrosplenial cortex (RSC) d Most common neuron in layer 2/3 of RSC is excitatory lowrheobase (LR) neuron d LR intrinsic properties enable precise, sustained encoding of information d Layer 2/3 of RSC is dominated by feedforward, not feedback, inhibition

Network hyperexcitability is a feature of Alzheimer’ disease (AD) as well as numerous transgenic mouse models of AD. While hyperexcitability in AD patients and AD animal models share certain features, the mechanistic overlap remains to be established. We aimed to identify features of network hyperexcitability in AD models that can be related to epileptiform activity signatures in AD patients. We studied network hyperexcitability in mice expressing amyloid precursor protein (APP) with mutations that cause familial AD, and compared a transgenic model that overexpresses human APP (hAPP) (J20), to a knock-in model expressing APP at physiological levels (APPNL/F). We recorded continuous long-term electrocorticogram (ECoG) activity from mice, and studied modulation by circadian cycle, behavioral, and brain state. We report that while J20s exhibit frequent interictal spikes (IISs), APPNL/F mice do not. In J20 mice, IISs were most prevalent during daylight hours and the circadian modulation was associated with sleep. Further analysis of brain state revealed that IIS in J20s are associated with features of rapid eye movement (REM) sleep. We found no evidence of cholinergic changes that may contribute to IIS-circadian coupling in J20s. In contrast to J20s, intracranial recordings capturing IIS in AD patients demonstrated frequent IIS in non-REM (NREM) sleep. The salient differences in sleep-stage coupling of IIS in APP overexpressing mice and AD patients suggests that different mechanisms may underlie network hyperexcitability in mice and humans. We posit that sleep-stage coupling of IIS should be an important consideration in identifying mouse AD models that most closely recapitulate network hyperexcitability in human AD.

The granular retrosplenial cortex (RSG) is critical for both spatial and non-spatial behaviors, but the underlying neural codes remain poorly understood. Here, we use optogenetic circuit mapping in mice to reveal a double dissociation that allows parallel circuits in superficial RSG to process disparate inputs. The anterior thalamus and dorsal subiculum, sources of spatial information, strongly and selectively recruit small low-rheobase (LR) pyramidal cells in RSG. In contrast, neighboring regular-spiking (RS) cells are preferentially controlled by claustral and anterior cingulate inputs, sources of mostly non-spatial information. Precise sublaminar axonal and dendritic arborization within RSG layer 1, in particular, permits this parallel processing. Observed thalamocortical synaptic dynamics enable computational models of LR neurons to compute the speed of head rotation, despite receiving head direction inputs that do not explicitly encode speed. Thus, parallel input streams identify a distinct principal neuronal subtype ideally positioned to support spatial orientation computations in the RSG.

The granular retrosplenial cortex (RSG) is critical for both spatial navigation and fear conditioning, but the neural codes enabling these seemingly disparate functions remain unknown. Here, using optogenetic circuit mapping, we reveal a double dissociation that allows parallel circuits in superficial RSG to process navigation- versus fear-related inputs. The anterior thalamus, a source of head direction information, strongly recruits small, low rheobase (LR) pyramidal cells in RSG layer 3. Neighboring regular-spiking (RS) cells are instead preferentially controlled by claustral and anterior cingulate inputs, sources of higher-order and fear-related information. Precise sublaminar axonal and dendritic arborization within RSG layer 1 enable this parallel processing. Synaptic dynamics and computational modeling suggest LR neurons are optimally-tuned conjunctive encoders of direction and distance inputs from the thalamus and dorsal subiculum, respectively. RS cells are better positioned to support contextual fear memories. Thus, parallel input streams to computationally-distinct principal neurons help facilitate diverse RSG functions.

The retrosplenial cortex (RSC) is essential for successful memory formation and spatial navigation. However, the rate and temporal coding schemes employed by the RSC to achieve these functions remain a mystery, and no biophysically realistic computational models of RSC cells yet exist. To understand the computational principles underlying RSC function, here we systematically characterize the intrinsic physiology and local connectivity of neurons in the superficial layers of the retrosplenial granular cortex (RSG). We show that the most prominent cell type in layers 2/3 of the RSG is a hyperexcitable, small pyramidal cell. These cells have a low rheobase (LR), high input resistance, lack of spike-frequency adaptation, and spike widths that are intermediate to those of neighboring fast-spiking (FS) inhibitory neurons and regular-spiking (RS) excitatory neurons. Using paired whole-cell recordings, we show, for the first time, that these LR cells are excitatory. However, they rarely synapse onto neighboring L2/3 neurons, exciting only 17% of FS cells and 0% of other L2/3 LR or RS cells. Instead, their axons head to deeper layers and towards the corpus callosum, likely targeting contralateral RSC. LR cells receive dominant inhibition from neighboring FS cells, with FS cells inhibiting over 52% of LR cells. Given the sparsity of reciprocal LR to FS connections, this inhibition is more likely to serve a feedforward, rather than feedback, role. In terms of rhythmic computations, this also means that the superficial RSG circuit may not employ the standard rules of pyramidal-interneuron gamma (PING) generation. Our results suggest that the retrosplenial cortex uses unique coding schemes that balance hyperexcitable excitatory neurons capable of sustained long-duration firing with dominant feedforward inhibitory control.