Modeling place cells and grid cells in multi-compartment environments: Entorhinal–hippocampal loop as a multisensory integration circuit

A spatial transformation-based CAN model for information integration within grid cell modules

The hippocampal-entorhinal circuit is considered to play an important role in the spatial cognition of animals. However, the mechanism of the information flow within the circuit and its contribution to the function of the grid-cell module are still topics of discussion. Prevailing theories suggest that grid cells are primarily influenced by self-motion inputs from the Medial Entorhinal Cortex, with place cells serving a secondary role by contributing to the visual calibration of grid cells. However, recent evidence suggests that both self-motion inputs and visual cues may collaboratively contribute to the formation of grid-like patterns. In this paper, we introduce a novel Continuous Attractor Network model based on a spatial transformation mechanism. This mechanism enables the integration of self-motion inputs and visual cues within grid-cell modules, synergistically driving the formation of grid-like patterns. From the perspective of individual neurons within the network, our model successfully replicates grid firing patterns. From the view of neural population activity within the network, the network can form and drive the activated bump, which describes the characteristic feature of grid-cell modules, namely, path integration. Through further exploration and experimentation, our model can exhibit significant performance in path integration. This study provides a new insight into understanding the mechanism of how the self-motion and visual inputs contribute to the neural activity within grid-cell modules. Furthermore, it provides theoretical support for achieving accurate path integration, which holds substantial implications for various applications requiring spatial navigation and mapping.

A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain's Cognitive Mechanism

Rats possess exceptional navigational abilities, allowing them to adaptively adjust their navigation paths based on the environmental structure. This remarkable ability is attributed to the interactions and regulatory mechanisms among various spatial cells within the rat's brain. Based on these, this paper proposes a navigation path search and optimization method for mobile robots based on the rat brain's cognitive mechanism. The aim is to enhance the navigation efficiency of mobile robots. The mechanism of this method is based on developing a navigation habit. Firstly, the robot explores the environment to search for the navigation goal. Then, with the assistance of boundary vector cells, the greedy strategy is used to guide the robot in generating a locally optimal path. Once the navigation path is generated, a dynamic self-organizing model based on the hippocampal CA1 place cells is constructed to further optimize the navigation path. To validate the effectiveness of the method, this paper designs several 2D simulation experiments and 3D robot simulation experiments, and compares the proposed method with various algorithms. The experimental results demonstrate that the proposed method not only surpasses other algorithms in terms of path planning efficiency but also yields the shortest navigation path. Moreover, the method exhibits good adaptability to dynamic navigation tasks.

Interactions between rodent visual and spatial systems during navigation

Many behaviours that are critical for animals to survive and thrive rely on spatial navigation. Spatial navigation, in turn, relies on internal representations about one's spatial location, one's orientation or heading direction and the distance to objects in the environment. Although the importance of vision in guiding such internal representations has long been recognized, emerging evidence suggests that spatial signals can also modulate neural responses in the central visual pathway. Here, we review the bidirectional influences between visual and navigational signals in the rodent brain. Specifically, we discuss reciprocal interactions between vision and the internal representations of spatial position, explore the effects of vision on representations of an animal's heading direction and vice versa, and examine how the visual and navigational systems work together to assess the relative distances of objects and other features. Throughout, we consider how technological advances and novel ethological paradigms that probe rodent visuo-spatial behaviours allow us to advance our understanding of how brain areas of the central visual pathway and the spatial systems interact and enable complex behaviours.

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.

A robotic model of hippocampal reverse replay for reinforcement learning

Hippocampal reverse replay, a phenomenon in which recently active hippocampal cells reactivate in the reverse order, is thought to contribute to learning, particularly reinforcement learning (RL), in animals. Here, we present a novel computational model which exploits reverse replay to improve stability and performance on a homing task. The model takes inspiration from the hippocampal-striatal network, and learning occurs via a three-factor RL rule. To augment this model with hippocampal reverse replay, we derived a policy gradient learning rule that associates place-cell activity with responses in cells representing actions and a supervised learning rule of the same form, interpreting the replay activity as a 'target' frequency. We evaluated the model using a simulated robot spatial navigation task inspired by the Morris water maze. Results suggest that reverse replay can improve performance stability over multiple trials. Our model exploits reverse reply as an additional source for propagating information about desirable synaptic changes, reducing the requirements for long-time scales in eligibility traces combined with low learning rates. We conclude that reverse replay can positively contribute to RL, although less stable learning is possible in its absence. Analogously, we postulate that reverse replay may enhance RL in the mammalian hippocampal-striatal system rather than provide its core mechanism.

What the Metaverse Is (Really) and Why We Need to Know About It

Major technology companies are investing significant sums of money in the creation of the metaverse whose main feature will be the fusion between the virtual world and the physical world. To allow this possibility is one of the less obvious features of the metaverse: the metaverse works like our minds. This ability makes the metaverse a significantly different technology from its predecessors. If television and social media are persuasive technologies, because of their ability to influence people's attitudes and behaviors, the metaverse is instead a transformative technology, capable of modifying what people think reality is. To achieve this goal, the technologies of the metaverse hack different key cognitive mechanisms: the experience of being in a place and in a body, the processes of brain-to-brain attunement and synchrony, and the ability of experiencing and inducing emotions. Clearly, these possibilities define totally new scenarios with positive and negative outcomes. Educating ourselves as to its promise, and the challenges it may present, is a necessity. This requires a "humane," integrated, and multidisciplinary approach, with stakeholders at the supranational level joining in the conversation.

Surviving COVID-19: The Neuroscience of Smart Working and Distance Learning

The persistence of the coronavirus-caused respiratory disease (COVID-19) and the related restrictions to mobility and social interactions are forcing a significant portion of students and workers to reorganize their daily activities to accommodate the needs of distance learning and agile work (smart working). What is the impact of these changes on the bosses/teachers' and workers/students' experience? This article uses recent neuroscience research findings to explore how distance learning and smart working impact the following three pillars that reflect the organization of our brain and are at the core of school and office experiences: (a) the learning/work happens in a dedicated physical place; (b) the learning/work is carried out under the supervision of a boss/professor; and (c) the learning/work is distributed between team members/classmates. For each pillar, we discuss its link with the specific cognitive processes involved and the impact that technology has on their functioning. In particular, the use of videoconferencing affects the functioning of Global Positioning System neurons (neurons that code our navigation behavior), mirror neurons, self-attention networks, spindle cells, and interbrain neural oscillations. These effects have a significant impact on many identity and cognitive processes, including social and professional identity, leadership, intuition, mentoring, and creativity. In conclusion, just moving typical office and learning processes inside a videoconferencing platform, as happened in many contexts during the COVID-19 pandemic, can in the long term erode corporate cultures and school communities. In this view, an effective use of technology requires us to reimagine how work and teaching are done virtually, in creative and bold new ways.