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Deterministic behavior can be modeled conveniently in the framework of finite automata. We present a recurrent neural network model based on biologically plausible circuit motifs that can learn deterministic transition models from given input sequences. Furthermore, we introduce simple structural constraints on the connectivity that are inspired by biology. Simulation results show that this leads...
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...
Time perception is a fundamental component of intelligence that structures the way humans act in various contexts. As action evolves over time, timing is necessary to appreciate environmental contingencies, estimate relations between events and predict the effects of our actions at future moments. Despite the fundamental role of time in human cognition it remains largely unexplored in the field of...
Animals show remarkable capabilities in navigating their habitat in a fully autonomous and energy-efficient way. In many species, these capabilities rely on a process called path integration, which enables them to estimate their current location and to find their way back home after long-distance journeys. Path integration is achieved by integrating compass and odometric cues. Here we introduce a...
We demonstrate a spiking neural network for navigation motivated by the chemotaxis circuit of Caenorhabditis elegans. Our network uses information regarding temporal gradients in intensity of local variables such as chemical concentration, temperature, radiation, etc., to make navigational decisions for contour tracking and obstacle avoidance. The gradient information is determined by mimicking the...
Typical navigation systems do not use object recognition as part of their autonomous driving systems. People often use hand-crafted features (e.g. lanes, traffic lights, intersections) based on programmers' knowledge about the environment. Those agents are usually brittle during real-world tests. However, landmarks, as a type of object, need to be recognized for an autonomous navigation system to...
The automatic design of control systems for multi-robot teams that operate in real time is not affordable with traditional evolutionary algorithms mainly due to the huge computational requirements they imply. Embodied Evolution (EE) is an evolutionary paradigm that aims to address this problem through the embodiment of the individuals that make up the population in the physical robots. The interest...
This study presents a robotic application of neural associative memory-based control system that imparts online learning and predictive control strategies to a cost-effective quadrotor helicopter, the Parrot AR.Drone 2.0. The control system is extended with to tackle a fundamental and challenging problem for the quadcoptor: hovering control. The proposed system is based on self-organizing incremental...
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