Place cells in the rat hippocampus have been extensively studied in relation to spatial navigation and the building of cognitive maps. Most place cell spatial models developed in relation to spatial navigation are independent of temporal components, where speed is not involved in learning goal-oriented tasks. In this paper we describe a spatio-temporal navigation learning model where speed modulates navigation trajectory learning. Prior work by Scleidorovich et al. in 2022, demonstrated that a rat was able to follow two different spatial trajectories by training on the same path using two different sets of speed encodings. These experiments were first studied using trained rats and the corresponding computational modeling using a reservoir recurrent neural network. The original rat experiments and reservoir network were limited to produce a single decision output with the rat turning either left or right depending on the sequence of speed encodings. In this work we expand on the original computational model and navigation tasks with more extensive decision outputs while using a LSTM recurrent neural network. We use various variations in speed encoding and analyze multiple output sequences. The computational model results are tested in a robot simulation environment where a robot learns to distinguish between multiple trajectories based on speed discrimination for the spatio-temporal learning. The experiments are then run under different noise conditions.

We show how hippocampal replay could explain latent learning, a phenomenon observed in animals where unrewarded pre-exposure to an environment, i.e. habituation, improves task learning rates once rewarded trials begin. We first describe a computational model for spatial navigation inspired by rat studies. The model exploits offline replay of trajectories previously learned by applying reinforcement learning. Then, to assess our hypothesis, the model is evaluated in a "multiple T-maze" environment where rats need to learn a path from the start of the maze to the goal. Simulation results support our hypothesis that pre-exposed or habituated rats learn the task significantly faster than non-pre-exposed rats. Results also show that this effect increases with the number of pre-exposed trials.

We present a biologically-inspired computational model of the rodent hippocampus based on recent studies of the hippocampus showing that its longitudinal axis is involved in complex spatial navigation. While both poles of the hippocampus, i.e. septal (dorsal) and temporal (ventral), encode spatial information; the septal area has traditionally been attributed more to navigation and action selection; whereas the temporal pole has been more involved with learning and motivation. In this work we hypothesize that the septal-temporal organization of the hippocampus axis also provides a multi-scale spatial representation that may be exploited during complex rodent navigation. To test this hypothesis, we developed a multi-scale model of the hippocampus evaluated it with a simulated rat on a multi-goal task, initially in a simplified environment, and then on a more complex environment where multiple obstacles are introduced. In addition to the hippocampus providing a spatial representation of the environment, the model includes an actor-critic framework for the motivated learning of the different tasks.

As rats learn to search for multiple sources of food or water in a complex environment, they generate increasingly efficient trajectories between reward sites. Such spatial navigation capacity involves the replay of hippocampal place-cells during awake states, generating small sequences of spatially related place-cell activity that we call "snippets". These snippets occur primarily during sharp-wave-ripples (SWRs). Here we focus on the role of such replay events, as the animal is learning a traveling salesperson task (TSP) across multiple trials. We hypothesize that snippet replay generates synthetic data that can substantially expand and restructure the experience available and make learning more optimal. We developed a model of snippet generation that is modulated by reward, propagated in the forward and reverse directions. This implements a form of spatial credit assignment for reinforcement learning. We use a biologically motivated computational framework known as 'reservoir computing' to model prefrontal cortex (PFC) in sequence learning, in which large pools of prewired neural elements process information dynamically through reverberations. This PFC model consolidates snippets into larger spatial sequences that may be later recalled by subsets of the original sequences. Our simulation experiments provide neurophysiological explanations for two pertinent observations related to navigation. Reward modulation allows the system to reject non-optimal segments of experienced trajectories, and reverse replay allows the system to "learn" trajectories that it has not physically experienced, both of which significantly contribute to the TSP behavior.

In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are at present identified manually on MRI to locate reference points for measurement and evaluation of pelvic organ prolapse (POP). Given that this is a time-consuming and subjective procedure, there is a need to localize pelvic bone structures automatically. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this paper, we present a model that combines support vector machines and nonlinear regression capturing global and local information to automatically identify the bounding boxes of bone structures on MRI. The model identifies the location of the pelvic bone structures by establishing the association between their relative locations and using local information such as texture features. Results show that the proposed method is able to locate the bone structures of interest accurately (dice similarity index >0.75) in 87-91% of the images. This research aims to enable accurate, consistent, and fully automated localization of bone structures on MRI to facilitate and improve the diagnosis of health conditions such as female POP.

Grid cells and place cells have shown to play an important role in spatial cognition in rats. While place cells provide global localization from external environment information, grid cells provide a “neural odometry” for path integration from internal vestibular information. In this paper we describe a spatial cognition model integrating grid cells and place cells from behavioral and neurophysiological brain studies in rats. Grid cell firing is generated from a linear oscillatory interference model that includes a reset mechanism to overcome errors in “neural odometry” readings. The model is evaluated in simulation. Future work is discussed including extensions to the model and evaluation under physical robots.

An important area of research in humanoid robots is energy consumption, as it limits autonomy, and can harm task performance. This work focuses on power aware motion planning. Its principal aim is to find joint trajectories to allow for a humanoid robot to go from crouch to stand position while minimizing power consumption. Q-Learning (QL) is used to search for optimal joint paths subject to angular position and torque restrictions. A planar model of the humanoid is used, which interacts with QL during a simulated offline learning phase. The best joint trajectories found during learning are then executed by a physical humanoid robot, the Aldebaran NAO. Position, velocity, acceleration, and current of the humanoid system are measured to evaluate energy, mechanical power, and Center of Mass (CoM) in order to estimate the performance of the new trajectory which yield a considerable reduction in power consumption.

Pelvic organ prolapse (POP) is a major women's health problem. Its diagnosis through magnetic resonance imaging (MRI) has become popular due to current inaccuracies of clinical examination. The diagnosis of POP on MRI consists of identifying reference points on pelvic bone structures for measurement and evaluation. However, it is currently performed manually, making it a time-consuming and subjective procedure. We present a new segmentation approach for automating pelvic bone point identification on MRI. It consists of a multistage mechanism based on texture-based block classification, leak detection, and prior shape information. Texture-based block classification and clustering analysis using K-means algorithm are integrated to generate the initial bone segmentation and to identify leak areas. Prior shape information is incorporated to obtain the final bone segmentation. Then, the reference points are identified using morphological skeleton operation. Results demonstrate that the proposed method achieves higher bone segmentation accuracy compared to other segmentation methods. The proposed method can also automatically identify reference points faster and with more consistency compared with the manually identified point process by experts. This research aims to enable faster and consistent pelvic measurements on MRI to facilitate and improve the diagnosis of female POP.

The study of spatial memory and learning in rats has inspired the development of multiple computational models that have lead to novel robotics architectures. Evaluation of computational models and resulting robotic architecture is usually carried out at the behavioral level by evaluating experimental tasks similar to those performed with rats. While multiple metrics are defined to evaluate behavioral performance in rats, metrics for robot task evaluation are very limited mostly to success/failure and time to complete task. In this paper we present a set of metrics taken from rat spatial memory and learning evaluation to further analyze performance in robots. The proposed set of metrics, learning latency and ability to navigate minimal distance to goal, should offer the robotics community additional tools to assess performance and validity of models in biologically-inspired robotic architectures at the task performance level. We also provide a comparative evaluation using these metrics between similar spatial tasks performed by rat and robot in comparable environments.

In this paper we present a model composed of layers of neurons designed on the basis of the neurophysiology of the rat hippocampus to control the navigation of a real robot. The model allows the robot to learn reward locations in different mazes and to return home autonomously by building a topological map of the environment. We described robotic experimentation results from our tests in a T-maze, an 8-arm radial maze and a 3-T shaped maze.

In this paper we present a model designed on the basis of the neurophysiology of the rat hippocampus to control the navigation of a real robot. The model allows the robot to learn reward locations dynamically moved in different environments, to build a topological map, and to return home autonomously. We describe robot experimentation results from our tests in a T-maze, an 8-arm radial maze and an extended maze