A Bionic Spatial Cognition Model and Method for Robots Based on the Hippocampus Mechanism

The hippocampus and its accessory are the main areas for spatial cognition. It can integrate paths and form environmental cognition based on motion information and then realize positioning and navigation. Learning from the hippocampus mechanism is a crucial way forward for research in robot perception, so it is crucial to building a calculation method that conforms to the biological principle. In addition, it should be easy to implement on a robot. This paper proposes a bionic cognition model and method for mobile robots, which can realize precise path integration and cognition of space. Our research can provide the basis for the cognition of the environment and autonomous navigation for bionic robots.

Comparable human spatial memory distortions in physical, desktop virtual and immersive virtual environments

Boundaries define regions of space and are integral to episodic memories. The impact of boundaries on spatial memory and neural representations of space has been extensively studied in freely-moving rodents. But less is known in humans and many prior studies have employed desktop virtual reality (VR) which lacks the body-based self-motion cues of the physical world, diminishing the potentially strong input from path integration to spatial memory. We replicated a desktop-VR study testing the impact of boundaries on spatial memory (Hartley et al., 2004) in a physical room (2.4m x 2.4m, 2m tall) by having participants (N = 27) learn the location of a circular stool and then after a short delay replace it where they thought they had found it. During the delay, the wall boundaries were either expanded or contracted. We compared performance to groups of participants undergoing the same procedure in a laser-scanned replica in both desktop VR (N = 44) and freely-walking head mounted display (HMD) VR (N = 39) environments. Performance was measured as goodness of fit between the spatial distributions of group responses and seven modelled distributions that prioritised different metrics based on boundary geometry or walking paths to estimate the stool location. The best fitting model was a weighted linear combination of all the geometric spatial models, but an individual model derived from place cell firing in Hartley et al. 2004 also fit well. High levels of disorientation in all three environments prevented detailed analysis on the contribution of path integration. We found identical model fits across the three environments, though desktop VR and HMD-VR appeared more consistent in spatial distributions of group responses than the physical environment and displayed known variations in virtual depth perception. Thus, while human spatial representation appears differentially influenced by environmental boundaries, the influence is similar across virtual and physical environments. Despite differences in body-based cue availability, desktop and HMD-VR allow a good and interchangeable approximation for examining human spatial memory in small-scale physical environments.

Evidence of learning walks related to scorpion home burrow navigation

The Navigation by Chemotextural Familiarity Hypothesis (NCFH) suggests that scorpions use their midventral pectines to gather chemical and textural information near their burrows and use this information as they subsequently return home. For NCFH to be viable, animals must somehow acquire home-directed ″tastes″ of the substrate, such as through path integration (PI) and/or learning walks. We conducted laboratory behavioral trials using desert grassland scorpions (Paruroctonus utahensis). Animals reliably formed burrows in small mounds of sand we provided in the middle of circular, sand lined behavioral arenas. We processed overnight infrared video recordings with a MATLAB script that tracked animal movements at 1-2 s intervals. In all, we analyzed the movements of 23 animals, representing nearly 1500 hours of video recording. We found that once animals established their home burrows, they immediately made one to several short, looping excursions away from and back to their burrows before walking greater distances. We also observed similar excursions when animals made burrows in level sand in the middle of the arena (i.e., no mound provided). These putative learning walks, together with recently reported PI in scorpions, may provide the crucial home-directed information requisite for NCFH.

Chronic, Mild Vestibulopathy Leads to Deficits in Spatial Tasks that Rely on Vestibular Input While Leaving Other Cognitive Functions and Brain Volumes Intact

Objectives: In this study, based on the known vestibulo-hippocampal connections, we asked whether mild chronic vestibulopathy leads only to vestibular-related deficits or whether there are effects on hippocampal function, structure, and cognition in general. In more detail, we assessed whether chronic vestibulopathy leads to (a) deficits in vestibular tasks without cognitive demand (balancing), (b) deficits in spatial cognitive tasks that require vestibular input (path integration, rotational memory), (c) deficits in spatial cognitive tasks that do not rely on vestibular input, (d) deficits in general cognitive function, and (e) atrophy in the brain. Methods: A total of 15 patients with chronic uni- or bilateral vestibulopathy (56.8 ± 10.1 years; 4 females) were included in this study and were age- and gender-matched by the control participants (57.6 ± 10.5) in a pairwise manner. Given their clinical symptoms and their deficits of the vestibulo-ocular reflex (VOR) the patients could be classified as being mildly affected. All participants of the underwent the following tests: clinical balance (CBT), triangle completion (TCT) for path integration, rotational memory (RM), the visuo-spatial subset of the Berlin intelligence structure test (BIS-4) and d2-R for attention and concentration, and a structural MRI for gray matter analysis using voxel-based morphometry (VBM). Results: Compared to the healthy controls, the vestibulopathy patients performed significantly worse in terms of CBT, TCT, and RM but showed no differences in terms of the BIS-4 and d2-R. There were also no significant volumetric gray matter differences between the two groups. Conclusions: This study provides evidence that both non-cognitive and cognitive functions that rely on vestibular input (balancing, path integration, rotational memory) are impaired, even in mild chronic vestibulopathy, while other cognitive functions, which rely on visual input (visuo-spatial memory, attention), are unimpaired in this condition, together with an overall intact brain structure. These findings may reflect a segregation between vestibular- and visual-dependent processes in the medial temporal lobe on the one hand and a structure–function dissociation on the other.

Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks

Medial entorhinal cortex (MEC) supports a wide range of navigational and memory related behaviors. Well-known experimental results have revealed specialized cell types in MEC --- e.g. grid, border, and head-direction cells --- whose highly stereotypical response profiles are suggestive of the role they might play in supporting MEC functionality. However, the majority of MEC neurons do not exhibit stereotypical firing patterns. How should the response profiles of these more "heterogeneous" cells be described, and how do they contribute to behavior? In this work, we took a computational approach to addressing these questions. We first performed a statistical analysis that shows that heterogeneous MEC cells are just as reliable in their response patterns as the more stereotypical cell types, suggesting that they have a coherent functional role. Next, we evaluated a spectrum of candidate models in terms of their ability to describe the response profiles of both stereotypical and heterogeneous MEC cells. We found that recently developed task-optimized neural network models are substantially better than traditional grid cell-centric models at matching most MEC neuronal response profiles --- including those of grid cells themselves --- despite not being explicitly trained for this purpose. Specific choices of network architecture (such as gated nonlinearities and an explicit intermediate place cell representation) have an important effect on the ability of the model to generalize to novel scenarios, with the best of these models closely approaching the noise ceiling of the data itself. We then performed "in-silica" experiments on this model to address questions involving the relative functional relevance of various cell types, finding that heterogeneous cells are likely to be just as involved in downstream functional outcomes (such as path integration) as grid and border cells. Finally, inspired by recent data showing that, going beyond their spatial response selectivity, MEC cells are also responsive to non-spatial rewards, we introduce a new MEC model that performs reward-modulated path integration. We find that this unified model matches neural recordings across all variable-reward conditions. Taken together, our results point toward a conceptually principled goal-driven modeling approach for moving future experimental and computational efforts beyond overly-simplistic single-cell stereotypes.