![]() ![]() We show how animals can navigate by reading out a simple population vector of grid cell activity across multiple spatial scales, even though neural activity is intrinsically stochastic. Mammalian grid cells fire when an animal crosses the points of an imaginary hexagonal grid tessellating the environment. Taken together, our study provides the first empirical evidence that semantic relatedness of objects emerged as a by-product of object recognition in DCNNs, implying that human may acquire semantic knowledge on objects without explicit top-down conceptual guidance. Finally, the fineness of the relatedness was greatly shaped by the demand of tasks that the DCNN performed, as the higher superordinate level of object classification was, the coarser the hierarchical structure of the relatedness emerged. In addition, the developmental trajectory of the relatedness among objects during training revealed that the hierarchical structure was constructed in a coarse-to-fine fashion, and evolved into maturity before the establishment of object recognition ability. Critically, the emerged relatedness of objects in the DCNN was highly similar to the WordNet in human, implying that top-down conceptual guidance may not be a prerequisite for human learning the relatedness among objects. Specifically, we explored representational similarity among objects in a typical DCNN (e.g., AlexNet), and found that representations of object categories were organized in a hierarchical fashion, suggesting that the relatedness among objects emerged automatically when learning to recognize them. Here we challenged this idea by examining whether deep convolutional neural networks (DCNNs) could learn relations among objects purely based on bottom-up perceptual experience of objects through training for object categorization. One dominant view is that top-down conceptual guidance is necessary to form such hierarchy. Human not only can effortlessly recognize objects, but also characterize object categories into semantic concepts with a nested hierarchical structure. Distributed entorhinal input drives hippocampal activity between distinct statistical and dynamical regimes of activity, thereby unifying several empirical observations. develop a theory of entorhinal–hippocampal processing. By relating the emergent hippocampal activity patterns drawn from our model to empirical data, we explain and reconcile a diversity of recently observed, but apparently unrelated, phenomena such as generative cycling, diffusive hippocampal reactivations and jumping trajectory events. ![]() Specifically, we demonstrate that the systematic modulation along the medial entorhinal cortex dorsoventral axis of grid population input into the hippocampus facilitates a flexible generative process that can interpolate between qualitatively distinct regimes of sequential hippocampal reactivations. We theorize how the brain should adapt internally generated sequences for particular cognitive functions and propose a neural mechanism by which this may be accomplished within the entorhinal–hippocampal circuit. However, these algorithms require disparate forms of sampling dynamics for optimal performance. In short, with empirical experiments, formal proof, and neural network simulation, our study reveals the origin of the hexagonal metric of grid cells mechanistically.Įxploration, consolidation and planning depend on the generation of sequential state representations. To further examine the biological plausibility of the model, we built various forms of neural networks in their simplest architecture to transform locations in space into structured states of cognitive map, and units with hexagonal patterns emerge ubiquitously. The formal proof shows that to support the cognitive map in the HPC, the spatial input from the ERC must be in a hexagonal pattern. Accordingly, we built a computational model depicting the spatial rhythm of 3Hz as scaffolds to transform discrete spatial locations into structured states of cognitive map that allows continuous updates of location estimates with each movement made. When the participants navigated from a location in the ring to the center in the space, we observed hexagonal signals in the ERC, and more importantly, a spatial rhythm of 3Hz in the hippocampus (HPC). Unbeknownst to the participants, the object variants were arranged on a ring centering on the prototype in an object space constructed by the object parts. Here we designed an object matching task where participants adjusted two parts of object variants to match their prototype in fMRI scanner. However, little is known about how and why the hexagonal metric emerges. To achieve the computational goal of navigating both natural and mental spaces, the brain adopts a hexagonal metric of grid cells in the entorhinal cortex (ERC) to chart the spaces. ![]()
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