Grid cells in the mammalian brain are fundamental to spatial navigation, and therefore crucial to how animals perceive and interact with their environment. Traditionally, grid cells are thought support path integration through highly symmetric hexagonal lattice firing patterns. However, recent findings show that their firing patterns become distorted in the presence of significant spatial landmarks such as rewarded locations. This introduces a novel perspective of dynamic, subjective, and action-relevant interactions between spatial representations and environmental cues. Here, we propose a practical and theoretical framework to quantify and explain these interactions.

Grid cells in the mammalian brain are fundamental to spatial navigation, and therefore crucial to how animals perceive and interact with their environment. Traditionally, grid cells are thought support path integration through highly symmetric hexagonal lattice firing patterns. However, recent findings show that their firing patterns become distorted in the presence of significant spatial landmarks such as rewarded locations. This introduces a novel perspective of dynamic, subjective, and action-relevant interactions between spatial representations and environmental cues. Here, we propose a practical and theoretical framework to quantify and explain these interactions.

The human ability to navigate through familiar environments, such as one's residence, even under low light conditions, is underpinned by a sophisticated cognitive mechanism referred to as spatial navigation Chen et al. [2023a], Wilkins [2011]. Humans use spatial navigation as a complex cognitive process that is important in finding their way around the environment by utilizing different senses and areas of the brain McNamara and Chen [2022], Chen et al. [2023b], Garg et al. [2024], Verghese and Blumen [2022]. It involves cues such as landmarks and information on self-motion to determine positions and achieve goals Roth et al. [2020]. A thorough understanding of spatial navigation is essential for improving destination efficiency and reducing anxiety in unfamiliar settings. Assessing spatial navigation is crucial for evaluating cognitive health, especially in neurological and neurodegenerative diseases Roth et al. [2020]. Spatial navigation tasks can detect structural changes in subcortical brain areas related to cognitive decline risk Chen et al. [2023b]. Different neurodegenerative conditions see impaired spatial navigation as a symptom at the onset; thus, it can be a valuable predictor of dementia in subjective cognitive decline patients or those with mild cognitive impairment Tangen et al. [2022]. These deficits worsen with aging, highlighting the urgent need for efficient assessment tools such as the Virtual Environments Navigation Assessment (VIENNA), which evaluates spatial navigation abilities Rekers and Finke [2024a]. This research is critical for detecting cognitive impairments and guiding clinical decisions.

Given a memory with infinite capacity, can we solve the learning problem? Apparently, nature has solved this problem as evidenced by the evolution of mammalian brains. Inspired by the organizational principles underlying hippocampal-neocortical systems, we present a navigation-based approach to manifold learning using memory and context. The key insight is to navigate on the manifold and memorize the positions of each route as inductive/design bias of direct-fit-to-nature. We name it navigation-based because our approach can be interpreted as navigating in the latent space of sensorimotor learning via memory (local maps) and context (global indexing). The indexing to the library of local maps within global coordinates is collected by an associative memory serving as the librarian, which mimics the coupling between the hippocampus and the neocortex. In addition to breaking from the notorious bias-variance dilemma and the curse of dimensionality, we discuss the biological implementation of our navigation-based learning by episodic and semantic memories in neural systems. The energy efficiency of navigation-based learning makes it suitable for hardware implementation on non-von Neumann architectures, such as the emerging in-memory computing paradigm, including spiking neural networks and memristor neural networks.

Summary: A new AI model that mimics the neural architecture and connections of the hippocampus is able to alter its synaptic connections as it moves a car-like virtual robot. HBP researchers at the Institute of Biophysics of the National Research Council (IBF-CNR) in Palermo, Italy, have mimicked the neuronal architecture and connections of the brain's hippocampus to develop a robotic platform capable of learning as humans do while the robot navigates around a space. The simulated hippocampus is able to alter its own synaptic connections as it moves a car-like virtual robot. Crucially, this means it needs to navigate to a specific destination only once before it is able to remember the path. This is a marked improvement over current autonomous navigation methods that rely on deep learning, and which have to calculate thousands of possible paths instead.

The hippocampal-entorhinal complex plays a major role in the organization of memory and thought. The formation of and navigation in cognitive maps of arbitrary mental spaces via place and grid cells can serve as a representation of memories and experiences and their relations to each other. The multi-scale successor representation is proposed to be the mathematical principle underlying place and grid cell computations. Here, we present a neural network, which learns a cognitive map of a semantic space based on 32 different animal species encoded as feature vectors. The neural network successfully learns the similarities between different animal species, and constructs a cognitive map of 'animal space' based on the principle of successor representations with an accuracy of around 30% which is near to the theoretical maximum regarding the fact that all animal species have more than one possible successor, i.e. nearest neighbor in feature space. Furthermore, a hierarchical structure, i.e. different scales of cognitive maps, can be modeled based on multi-scale successor representations. We find that, in fine-grained cognitive maps, the animal vectors are evenly distributed in feature space. In contrast, in coarse-grained maps, animal vectors are highly clustered according to their biological class, i.e. amphibians, mammals and insects. This could be a possible mechanism explaining the emergence of new abstract semantic concepts. Finally, even completely new or incomplete input can be represented by interpolation of the representations from the cognitive map with remarkable high accuracy of up to 95%. We conclude that the successor representation can serve as a weighted pointer to past memories and experiences, and may therefore be a crucial building block for future machine learning to include prior knowledge, and to derive context knowledge from novel input.

Neural pathways in the brain's hippocampus, a complex structure embedded deep into temporal lobe, enables humans to time travel through life memories, a new study reveals. A team of neuroscientists, Led by researchers at the Brain and Cognition Research Center (CerCo) at the French National Centre for Scientific Research, found these neurons, or time cells, fire during specific moments and may contribute to memory by encoding information about the time and order of events. While monitoring brain activity in an experiment, researchers said they were able to decode different moments in time based on the activity of the entire group of neurons. Along with finding this complex process in the brain, the results bring hope to those with conditions that affect memory and the ability to process time, including Alzheimer's and Dementia, as scientists could soon find a way to treat the neural pathway. Leila Reddy, a neuroscientist who led the research, told VICE: 'The hippocampus is important for judging the temporal order of events (among other things), and damage to the hippocampus can result in an impairment of memory for temporal order (for example remembering the order of a list of items).

How does the brain represent the world and allow spatial navigation? One mechanism is hippocampal place cells—neurons that fire according to where an animal is in its environment. Different place cells fire according to different locations, and together they are thought to provide a cognitive map that supports spatial navigation and memory ([ 1 ][1]). Place cells have been described in a range of mammalian species, including mice, bats, marmosets, and humans. However, most studies have used rats in small enclosures or mazes. Thus, it is unknown how such representations might underpin larger-scale, real-world navigation. On page 933 of this issue, Eliav et al. ([ 2 ][2]) show that in bats flying in a large (200-m-long) enclosure, most place cells fire in several different locations and with varying spatial scales. Such multiscale representations are likely the most efficient way for a finite number of neurons to encode large distances. Neurophysiological recordings in rats exploring relatively small “open-field” environments (∼1 m2) or running along short tracks 1 to 2 m long have revealed that a given place cell in the hippocampus typically fires when the rat is in a single area within the apparatus (called its place field) ([ 1 ][1], [ 3 ][3], [ 4 ][4]). In the few experiments that have investigated bigger open-field environments and longer tracks, place fields are typically slightly enlarged compared with those in smaller environments ([ 4 ][4]–[ 6 ][5]), and individual place cells in CA1 (the main output region of the hippocampus) fire in multiple, irregularly spaced locations ([ 5 ][6], [ 6 ][5]), with more place fields per cell in tracks of increasing length ([ 6 ][5]). Within a given environment, the different place fields of each hippocampal neuron are of a fairly uniform size, but there is an anatomical gradient, with the most dorsal hippocampal place cells having the smallest fields and ventral hippocampal cells having the largest fields ([ 3 ][3], [ 7 ][7]). Together, these studies suggest that the hippocampus provides an ensemble place code, whereby different combinations of neurons are active in any given location, and that coding of different spatial scales is provided by different neurons across the dorsal-ventral hippocampal axis. But how does the mammalian brain represent much larger spaces, on the spatial scale that animals would need to navigate in their natural environment? Eliav et al. wirelessly recorded from dorsal CA1 place cells in bats as they flew along a 200-m-long tunnel between two feeding stations. They found not only that place cells expressed multiple, irregularly spaced place fields in this very large environment but also that the size of the different place fields expressed by a given neuron varied widely: The mean ratio of the largest:smallest field was 4.4:1, but this was as high as 20:1 in some cells (see the figure). By contrast, and consistent with observations in rats, in a shorter 6-m-long tunnel, place cells expressed only one or two fields, the average field size was smaller than in the 200-mlong tunnel, and fields of the same cell were of a similar size (mean ratio <2:1). ![Figure][8] Navigating large, complex spaces Eliav et al. found that bats exhibit multiscale place cell coding. Individual place cells in the hippocampus fire according to a range of spatial scales (place fields of a single place cell indicated by circles), allowing optimal processing of a large environment with a finite number of cells. GRAPHIC: N. DESAI/ SCIENCE These findings of multiscale coding by individual place cells may help answer a puzzling question: How can a finite population of place cells encode the large environments in which mammals navigate in the wild, at both large and small spatial scales? The modeling by Eliav et al. shows that the multiscale coding mechanism seen in the bats is a particularly efficient mechanism for coding large environments. It needs fewer neurons for accurate decoding of the current location of the bat than other ensemble coding mechanisms based on individual cells having multiple fields of the same size and other cells having fields of different sizes (as had previously been assumed). It will be important to determine the extent to which multiscale coding by individual neurons is a general property of hippocampal coding across species and across different types and scales of environments. A preliminary study of rats following a moving robotic feeder in an 18.6-m2 open-field environment reported that cells in dorsal CA1 exhibited the same type of multiscale coding as found in the tunnel-flying bats ([ 8 ][9]). This indicates that this type of firing may be a general principle of hippocampal coding of large-scale space across mammalian species. Moreover, perhaps in large, continuous spaces, multiscale place cell representation may be the rule. As with many elegant studies, the work of Eliav et al. points to promising new avenues of research. One key question is how multiscale encoding arises. The two main inputs to CA1 (where the multiscale place cells have been described) are the CA3 and the medial entorhinal cortex (MEC). CA3 also contains place cells; indeed, the dorsal-ventral gradient of small-large place fields was described in CA3 neurons in rats ([ 7 ][7]). Conversely, the MEC contains a different type of spatial cell called grid cells. Each grid cell fires in multiple locations arranged in a regular hexagonal grid pattern that repeats across the environment (again with a dorsal-ventral arrangement of grid field size and spacing) ([ 9 ][10], [ 10 ][11]). Grid cells are thought to be important for path integration, where animals use self-motion signals to estimate distances and directions traveled. Eliav et al. suggest a feed-forward model whereby the multiscale fields in CA1 result from convergence of inputs from multiple CA3 place cells with different spatial scales onto each CA1 place cell. Predictions of this model that still need to be tested are that CA3 neurons should not show multiple fields in large environments and that either grid cells should not show multiple fields or grid cell inputs do not contribute to the firing of CA1 place fields in large environments. A second question is whether there is a continuum of multiscale coding across environments of all sizes or whether (as suggested by Eliav et al. ) multiscale coding occurs only in sufficiently large environments. And if the latter, what behavioral, perceptual, and neural mechanisms trigger the transition from small-scale to large-scale encoding of space? The study of Eliav et al. provides a marker for the need to examine spatial coding in ethologically relevant environments. The multiscale place cell coding mechanism that they demonstrate may allow both fine-scale spatial localization and localization on a more extended scale, which would be required for navigating accurately between very distant locations hundreds of meters or kilometers apart. 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Recent advances in artificial intelligence (AI) and neuroscience are impressive. In AI, this includes the development of computer programs that can beat a grandmaster at GO or outperform human radiologists at cancer detection. A great deal of these technological developments are directly related to progress in artificial neural networks—initially inspired by our knowledge about how the brain carries out computation. In parallel, neuroscience has also experienced significant advances in understanding the brain. For example, in the field of spatial navigation, knowledge about the mechanisms and brain regions involved in neural computations of cognitive maps—an internal representation of space—recently received the Nobel Prize in medicine. Much of the recent progress in neuroscience has partly been due to the development of technology used to record from very large populations of neurons in multiple regions of the brain with exquisite temporal and spatial resolution in behaving animals. With the advent of the vast quantities of data that these techniques allow us to collect there has been an increased interest in the intersection between AI and neuroscience, many of these intersections involve using AI as a novel tool to explore and analyze these large data sets. However, given the common initial motivation point—to understand the brain—these disciplines could be more strongly linked. Currently much of this potential synergy is not being realized. We propose that spatial navi...

With the advent of consumer-grade products for presenting an immersive virtual environment (VE), there is a growing interest in utilizing VEs for testing human navigation behavior. However, preparing a VE still requires a high level of technical expertise in computer graphics and virtual reality, posing a significant hurdle to embracing the emerging technology. To address this issue, this paper presents Delayed Feedback based Immersive Navigation Environment (DeFINE), a framework that allows for easy creation and administration of navigation tasks within customizable VEs via intuitive graphical user interfaces and simple settings files. Importantly, DeFINE has a built-in capability to provide performance feedback to participants during an experiment, a feature that is critically missing in other similar frameworks. To demonstrate the usability of DeFINE from both experimentalists' and participants' perspectives, a case study was conducted in which participants navigated to a hidden goal location with feedback that differentially weighted speed and accuracy of their responses. In addition, the participants evaluated DeFINE in terms of its ease of use, required workload, and proneness to induce cybersickness. Results showed that the participants' navigation performance was affected differently by the types of feedback they received, and they rated DeFINE highly in the evaluations, validating DeFINE's architecture for investigating human navigation in VEs. With its rich out-of-the-box functionality and great customizability due to open-source licensing, DeFINE makes VEs significantly more accessible to many researchers.