Neurons important for cognitive function are often classified by their morphology and integrative properties. However, it is unclear if within a single class of neuron these properties tune synaptic responses to the salient features of the information that each neuron represents. We demonstrate that for stellate neurons in layer II of the medial entorhinal cortex, the waveform of postsynaptic potentials, the time window for detection of coincident inputs, and responsiveness to gamma frequency inputs follow a dorsal-ventral gradient similar to the topographical organization of grid-like spatial firing fields of neurons in this area. We provide evidence that these differences are due to a membrane conductance gradient mediated by HCN and leak potassium channels. These findings suggest key roles for synaptic integration in computations carried out within the medial entorhinal cortex and imply that tuning of neural information processing by membrane ion channels is important for normal cognitive function.

SummaryDeep layers of the medial entorhinal cortex are considered to relay signals from the hippocampus to other brain structures, but pathways for routing of signals to and from the deep layers are not well established. Delineating these pathways is important for a circuit level understanding of spatial cognition and memory. We find that neurons in layers 5a and 5b have distinct molecular identities, defined by the transcription factors Etv1 and Ctip2, and divergent targets, with extensive intratelencephalic projections originating in layer 5a, but not 5b. This segregation of outputs is mirrored by the organization of glutamatergic input from stellate cells in layer 2 and from the hippocampus, with both preferentially targeting layer 5b over 5a. Our results suggest a molecular and anatomical organization of input-output computations in deep layers of the MEC, reveal precise translaminar microcircuitry, and identify molecularly defined pathways for spatial signals to influence computation in deep layers.

SummarySpatial learning requires estimates of location that may be obtained by path integration or from positional cues. Grid and other spatial firing patterns of neurons in the superficial medial entorhinal cortex (MEC) suggest roles in behavioral estimation of location. However, distinguishing the contributions of path integration and cue-based signals to spatial behaviors is challenging, and the roles of identified MEC neurons are unclear. We use virtual reality to dissociate linear path integration from other strategies for behavioral estimation of location. We find that mice learn to path integrate using motor-related self-motion signals, with accuracy that decreases steeply as a function of distance. We show that inactivation of stellate cells in superficial MEC impairs spatial learning in virtual reality and in a real world object location recognition task. Our results quantify contributions of path integration to behavior and corroborate key predictions of models in which stellate cells contribute to location estimation.

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