Explore the vast range of titles available.

Recent models of episodic memory propose a division of labor among medial temporal lobe cortices comprising the parahippocampal gyrus. Specifically, perirhinal and lateral entorhinal cortices are thought to comprise an object/item information pathway, whereas parahippocampal and medial entorhinal cortices are thought to comprise a spatial/contextual information pathway. Although several studies in human subjects have demonstrated a perirhinal/parahippocampal division, such a division among subregions of the human entorhinal cortex has been elusive. Other recent work has implicated pattern separation computations in the dentate gyrus and CA3 subregions of the hippocampus as a mechanism supporting the resolution of mnemonic interference. However, the nature of contributions of medial temporal lobe cortices to downstream hippocampal computations is largely unknown. We used high-resolution fMRI during a task selectively taxing mnemonic discrimination of object identity or spatial location, designed to differentially engage the two information pathways in the medial temporal lobes. Consistent with animal models, we demonstrate novel evidence for a domain-selective dissociation between lateral and medial entorhinal cortex in humans, and between perirhinal and parahippocampal cortex as a function of information content. Conversely, hippocampal dentate gyrus/CA3 demonstrated signals consistent with resolution of mnemonic interference across domains. These results provide insight into the information processing capacities and hierarchical interference resolution throughout the human medial temporal lobe. Significance Episodic memories are complex records of experience, consisting of “what” happened as well as “where” and “when” it happened. Animal studies have demonstrated distinct brain networks supporting memory for information about what experience occurred and information about where the experience occurred. However, such dissociations have been elusive in humans. Using a memory interference task that pits object (i.e., what) vs. spatial (i.e., where) memories against each other and high-resolution fMRI, we report evidence for two parallel but interacting networks in the human hippocampus and its input regions, supporting prior work in animals. We propose a conceptual model of how object and spatial interference are reduced in the regions providing input to the hippocampus, allowing rich, distinct memories to be built.

Neural computation is often traced in terms of either rate- or phase-codes. However, most circuit operations will simultaneously affect information across both coding schemes. It remains unclear how phase and rate coded information is transmitted, in the face of continuous modification at consecutive processing stages. Here, we study this question in the entorhinal cortex (EC)- dentate gyrus (DG)- CA3 system using three distinct computational models. We demonstrate that DG feedback inhibition leverages EC phase information to improve rate-coding, a computation we term phase-to-rate recoding. Our results suggest that it i) supports the conservation of phase information within sparse rate-codes and ii) enhances the efficiency of plasticity in downstream CA3 via increased synchrony. Given the ubiquity of both phase-coding and feedback circuits, our results raise the question whether phase-to-rate recoding is a recurring computational motif, which supports the generation of sparse, synchronous population-rate-codes in areas beyond the DG. How neural codes are passed from one brain area to the next remains poorly understood. Here, the authors show how neuronal feedback inhibition converts incoming temporal information into sparse rate information in a biophysical network model of the dentate gyrus.

The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment 1 . Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations 2 , and are organized in modules 3 that collectively form a population code for the animal’s allocentric position 1 . The invariance of the correlation structure of this population code across environments 4 , 5 and behavioural states 6 , 7 , independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern 1 , 8 – 11 . However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models 12 . This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells. Simultaneous recordings from hundreds of grid cells in rats, combined with topological data analysis, show that network activity in grid cells resides on a toroidal manifold that is invariant across environments and brain states.

The encoding of time and its binding to events are crucial for episodic memory, but how these processes are carried out in hippocampal–entorhinal circuits is unclear. Here we show in freely foraging rats that temporal information is robustly encoded across time scales from seconds to hours within the overall population state of the lateral entorhinal cortex. Similarly pronounced encoding of time was not present in the medial entorhinal cortex or in hippocampal areas CA3–CA1. When animals’ experiences were constrained by behavioural tasks to become similar across repeated trials, the encoding of temporal flow across trials was reduced, whereas the encoding of time relative to the start of trials was improved. The findings suggest that populations of lateral entorhinal cortex neurons represent time inherently through the encoding of experience. This representation of episodic time may be integrated with spatial inputs from the medial entorhinal cortex in the hippocampus, allowing the hippocampus to store a unified representation of what, where and when. Temporal information that is useful for episodic memory is encoded across a wide range of timescales in the lateral entorhinal cortex, arising inherently from its representation of ongoing experience.

The hippocampus and the medial entorhinal cortex are part of a brain system that maps self-location during navigation in the proximal environment 1 , 2 . In this system, correlations between neural firing and an animal’s position or orientation are so evident that cell types have been given simple descriptive names, such as place cells 3 , grid cells 4 , border cells 5 , 6 and head-direction cells 7 . While the number of identified functional cell types is growing at a steady rate, insights remain limited by an almost-exclusive reliance on recordings from rodents foraging in empty enclosures that are different from the richly populated, geometrically irregular environments of the natural world. In environments that contain discrete objects, animals are known to store information about distance and direction to those objects and to use this vector information to guide navigation 8 – 10 . Theoretical studies have proposed that such vector operations are supported by neurons that use distance and direction from discrete objects 11 , 12 or boundaries 13 , 14 to determine the animal’s location, but—although some cells with vector-coding properties may be present in the hippocampus 15 and subiculum 16 , 17 —it remains to be determined whether and how vectorial operations are implemented in the wider neural representation of space. Here we show that a large fraction of medial entorhinal cortex neurons fire specifically when mice are at given distances and directions from spatially confined objects. These ‘object-vector cells’ are tuned equally to a spectrum of discrete objects, irrespective of their location in the test arena, as well as to a broad range of dimensions and shapes, from point-like objects to extended surfaces. Our findings point to vector coding as a predominant form of position coding in the medial entorhinal cortex. Cells in the mouse medial entorhinal cortex that fire when mice are at a specific distance and direction from a stationary object suggest that vector coding is important for rodent navigation.

The hippocampus receives its major cortical input from the medial entorhinal cortex (MEC) and the lateral entorhinal cortex (LEC). It is commonly believed that the MEC provides spatial input to the hippocampus, whereas the LEC provides non-spatial input. We review new data which suggest that this simple dichotomy between ‘where’ versus ‘what’ needs revision. We propose a refinement of this model, which is more complex than the simple spatial–non-spatial dichotomy. MEC is proposed to be involved in path integration computations based on a global frame of reference, primarily using internally generated, self-motion cues and external input about environmental boundaries and scenes; it provides the hippocampus with a coordinate system that underlies the spatial context of an experience. LEC is proposed to process information about individual items and locations based on a local frame of reference, primarily using external sensory input; it provides the hippocampus with information about the content of an experience.

Episodic memories initially require rapid synaptic plasticity within the hippocampus for their formation and are gradually consolidated in neocortical networks for permanent storage. However, the engrams and circuits that support neocortical memory consolidation have thus far been unknown. We found that neocortical prefrontal memory engram cells, which are critical for remote contextual fear memory, were rapidly generated during initial learning through inputs from both the hippocampal–entorhinal cortex network and the basolateral amygdala. After their generation, the prefrontal engram cells, with support from hippocampal memory engram cells, became functionally mature with time. Whereas hippocampal engram cells gradually became silent with time, engram cells in the basolateral amygdala, which were necessary for fear memory, were maintained. Our data provide new insights into the functional reorganization of engrams and circuits underlying systems consolidation of memory.

Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go 1 , 2 . Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning 3 – 5 failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex 6 . Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space 7 , 8 and is critical for integrating self-motion (path integration) 6 , 7 , 9 and planning direct trajectories to goals (vector-based navigation) 7 , 10 , 11 . Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types 12 . We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments—optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation 7 , 10 , 11 , demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments. Grid-like representations emerge spontaneously within a neural network trained to self-localize, enabling the agent to take shortcuts to destinations using vector-based navigation.

Cells in the hippocampal–entorhinal circuit, which fire in response to navigational variables such as location or speed, are shown also to encode continuous, task-relevant but non-spatial variables such as sound frequency. Mapping sound in the brain (Tank 21692, Bio Letter) Map-like representations of physical space have been well-documented in the hippocampus by studies of spatial navigation, but it is unclear whether this spatial representation is part of a more general mechanism for encoding other continuous variables, such as sound. Here, David Tank and colleagues recorded from rat hippocampal neurons while they manipulated a joystick to control sound output along a continuous frequency scale. Neurons encoded for all aspects of this task and formed discrete firing fields in response to specific sound frequencies. The hippocampal cells representing this auditory axis overlapped with cells representing space during navigation. The authors suggest that representation mechanisms similar to those used during navigation may encode variables in a broader range of cognitive processes. During spatial navigation, neural activity in the hippocampus and the medial entorhinal cortex (MEC) is correlated to navigational variables such as location 1 , 2 , head direction 3 , speed 4 , and proximity to boundaries 5 . These activity patterns are thought to provide a map-like representation of physical space. However, the hippocampal–entorhinal circuit is involved not only in spatial navigation, but also in a variety of memory-guided behaviours 6 . The relationship between this general function and the specialized spatial activity patterns is unclear. A conceptual framework reconciling these views is that spatial representation is just one example of a more general mechanism for encoding continuous, task-relevant variables 7 , 8 , 9 , 10 . Here we tested this idea by recording from hippocampal and entorhinal neurons during a task that required rats to use a joystick to manipulate sound along a continuous frequency axis. We found neural representation of the entire behavioural task, including activity that formed discrete firing fields at particular sound frequencies. Neurons involved in this representation overlapped with the known spatial cell types in the circuit, such as place cells and grid cells. These results suggest that common circuit mechanisms in the hippocampal–entorhinal system are used to represent diverse behavioural tasks, possibly supporting cognitive processes beyond spatial navigation.

New silicon probes known as Neuropixels are shown to record from hundreds of neurons simultaneously in awake and freely moving rodents. High-density neural activity probe Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions. Existing technologies reliably measure activity from a relatively small number of neurons with high spatial and temporal resolution, or from a large volume of neurons with low resolution. Timothy Harris and colleagues describe the design, fabrication and performance of Neuropixels, a silicon probe that can measure well-isolated neural activity from hundreds of neurons. They integrated these probes into a lightweight system that could record activity simultaneously and with high fidelity from hundreds of neurons in awake and freely moving rodents. Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures 1 , 2 . Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca 2+ imaging 3 , 4 , 5 offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal–oxide–semiconductor (CMOS) processing-compatible low-impedance TiN 6 sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.