Remapping in a recurrent neural network model of navigation and context inference.

Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ('remap') in response to changing contextual factors such as environmental cues, task conditions, and behavioral states, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.

Remapping in a recurrent neural network model of navigation and context inference.

Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ("remap") in response to changing contextual factors such as environmental cues, task conditions, and behavioral state, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.

Dynamic and reversible remapping of network representations in an unchanging environment.

Neurons in the medial entorhinal cortex alter their firing properties in response to environmental changes. This flexibility in neural coding is hypothesized to support navigation and memory by dividing sensory experience into unique episodes. However, it is unknown how the entorhinal circuit as a whole transitions between different representations when sensory information is not delineated into discrete contexts. Here we describe rapid and reversible transitions between multiple spatial maps of an unchanging task and environment. These remapping events were synchronized across hundreds of neurons, differentially affected navigational cell types, and correlated with changes in running speed. Despite widespread changes in spatial coding, remapping comprised a translation along a single dimension in population-level activity space, enabling simple decoding strategies. These findings provoke reconsideration of how the medial entorhinal cortex dynamically represents space and suggest a remarkable capacity of cortical circuits to rapidly and substantially reorganize their neural representations.

Mouse entorhinal cortex encodes a diverse repertoire of self-motion signals.

Neural circuits generate representations of the external world from multiple information streams. The navigation system provides an exceptional lens through which we may gain insights about how such computations are implemented. Neural circuits in the medial temporal lobe construct a map-like representation of space that supports navigation. This computation integrates multiple sensory cues, and, in addition, is thought to require cues related to the individual's movement through the environment. Here, we identify multiple self-motion signals, related to the position and velocity of the head and eyes, encoded by neurons in a key node of the navigation circuitry of mice, the medial entorhinal cortex (MEC). The representation of these signals is highly integrated with other cues in individual neurons. Such information could be used to compute the allocentric location of landmarks from visual cues and to generate internal representations of space.

Characterization of plexinA and two distinct semaphorin1a transcripts in the developing and adult cricket Gryllus bimaculatus.

Guidance cues act during development to guide growth cones to their proper targets in both the central and peripheral nervous systems. Experiments in many species indicate that guidance molecules also play important roles after development, though less is understood about their functions in the adult. The Semaphorin family of guidance cues, signaling through Plexin receptors, influences the development of both axons and dendrites in invertebrates. Semaphorin functions have been extensively explored in Drosophila melanogaster and some other Dipteran species, but little is known about their function in hemimetabolous insects. Here, we characterize sema1a and plexA in the cricket Gryllus bimaculatus. In fact, we found two distinct predicted Sema1a proteins in this species, Sema1a.1 and Sema1a.2, which shared only 48% identity at the amino acid level. We include a phylogenetic analysis that predicted that many other insect species, both holometabolous and hemimetabolous, express two Sema1a proteins as well. Finally, we used in situ hybridization to show that sema1a.1 and sema1a.2 expression patterns were spatially distinct in the embryo, and both roughly overlap with plexA. All three transcripts were also expressed in the adult brain, mainly in the mushroom bodies, though sema1a.2 was expressed most robustly. sema1a.2 was also expressed strongly in the adult thoracic ganglia while sema1a.1 was only weakly expressed and plexA was undetectable.

Morphogenesis of neurons and glia within an epithelium.

To sense the outside world, some neurons protrude across epithelia, the cellular barriers that line every surface of our bodies. To study the morphogenesis of such neurons, we examined the C. elegans amphid, in which dendrites protrude through a glial channel at the nose. During development, amphid dendrites extend by attaching to the nose via DYF-7, a type of protein typically found in epithelial apical ECM. Here, we show that amphid neurons and glia exhibit epithelial properties, including tight junctions and apical-basal polarity, and develop in a manner resembling other epithelia. We find that DYF-7 is a fibril-forming apical ECM component that promotes formation of the tube-shaped glial channel, reminiscent of roles for apical ECM in other narrow epithelial tubes. We also identify a requirement for FRM-2, a homolog of EPBL15/moe/Yurt that promotes epithelial integrity in other systems. Finally, we show that other environmentally exposed neurons share a requirement for DYF-7. Together, our results suggest that these neurons and glia can be viewed as part of an epithelium continuous with the skin, and are shaped by mechanisms shared with other epithelia.

Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation.

To guide navigation, the nervous system integrates multisensory self-motion and landmark information. We dissected how these inputs generate spatial representations by recording entorhinal grid, border and speed cells in mice navigating virtual environments. Manipulating the gain between the animal's locomotion and the visual scene revealed that border cells responded to landmark cues while grid and speed cells responded to combinations of locomotion, optic flow and landmark cues in a context-dependent manner, with optic flow becoming more influential when it was faster than expected. A network model explained these results by revealing a phase transition between two regimes in which grid cells remain coherent with or break away from the landmark reference frame. Moreover, during path-integration-based navigation, mice estimated their position following principles predicted by our recordings. Together, these results provide a theoretical framework for understanding how landmark and self-motion cues combine during navigation to generate spatial representations and guide behavior.