1,055 research outputs found
Modeling prefrontal functions for robot navigation
Colloque avec actes et comité de lecture.This paper presents a model of cooperation between representations of correlation in the associative cortex and goal oriented representations of action in the prefrontal cortex. The model is applied to robot navigation and gives a framework for addressing planning with numerical neural techniques
A biologically inspired meta-control navigation system for the Psikharpax rat robot
A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics
Self-directedness, integration and higher cognition
In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm
Neurobiologically Inspired Mobile Robot Navigation and Planning
After a short review of biologically inspired navigation architectures, mainly relying on modeling the hippocampal anatomy, or at least some of its functions, we present a navigation and planning model for mobile robots. This architecture is based on a model of the hippocampal and prefrontal interactions. In particular, the system relies on the definition of a new cell type “transition cells” that encompasses traditional “place cells”
Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots
Building a humanlike integrative artificial cognitive system, that is, an
artificial general intelligence, is one of the goals in artificial intelligence
and developmental robotics. Furthermore, a computational model that enables an
artificial cognitive system to achieve cognitive development will be an
excellent reference for brain and cognitive science. This paper describes the
development of a cognitive architecture using probabilistic generative models
(PGMs) to fully mirror the human cognitive system. The integrative model is
called a whole-brain PGM (WB-PGM). It is both brain-inspired and PGMbased. In
this paper, the process of building the WB-PGM and learning from the human
brain to build cognitive architectures is described.Comment: 55 pages, 8 figures, submitted to Neural Network
Introduction: The Third International Conference on Epigenetic Robotics
This paper summarizes the paper and poster contributions
to the Third International Workshop on
Epigenetic Robotics. The focus of this workshop is
on the cross-disciplinary interaction of developmental
psychology and robotics. Namely, the general
goal in this area is to create robotic models of the
psychological development of various behaviors. The
term "epigenetic" is used in much the same sense as
the term "developmental" and while we could call
our topic "developmental robotics", developmental
robotics can be seen as having a broader interdisciplinary
emphasis. Our focus in this workshop is
on the interaction of developmental psychology and
robotics and we use the phrase "epigenetic robotics"
to capture this focus
Toward evolutionary and developmental intelligence
Given the phenomenal advances in artificial intelligence in specific domains like visual object recognition and game playing by deep learning, expectations are rising for building artificial general intelligence (AGI) that can flexibly find solutions in unknown task domains. One approach to AGI is to set up a variety of tasks and design AI agents that perform well in many of them, including those the agent faces for the first time. One caveat for such an approach is that the best performing agent may be just a collection of domain-specific AI agents switched for a given domain. Here we propose an alternative approach of focusing on the process of acquisition of intelligence through active interactions in an environment. We call this approach evolutionary and developmental intelligence (EDI). We first review the current status of artificial intelligence, brain-inspired computing and developmental robotics and define the conceptual framework of EDI. We then explore how we can integrate advances in neuroscience, machine learning, and robotics to construct EDI systems and how building such systems can help us understand animal and human intelligence
Emotion in Future Intelligent Machines
Over the past decades, research in cognitive and affective neuroscience has
emphasized that emotion is crucial for human intelligence and in fact
inseparable from cognition. Concurrently, there has been a significantly
growing interest in simulating and modeling emotion in robots and artificial
agents. Yet, existing models of emotion and their integration in cognitive
architectures remain quite limited and frequently disconnected from
neuroscientific evidence. We argue that a stronger integration of emotion in
robot models is critical for the design of intelligent machines capable of
tackling real world problems. Drawing from current neuroscientific knowledge,
we provide a set of guidelines for future research in artificial emotion and
intelligent machines more generally
Forced Moves or Good Tricks in Design Space? Landmarks in the Evolution of Neural Mechanisms for Action Selection
This review considers some important landmarks in animal evolution, asking to what extent specialized action-selection mechanisms play a role in the functional architecture of different nervous system plans, and looking for “forced moves” or “good tricks” (see Dennett, D., 1995, Darwin’s Dangerous Idea, Penguin Books, London) that could possibly transfer to the design of robot control systems. A key conclusion is that while cnidarians (e.g. jellyfish) appear to have discovered some good tricks for the design of behavior-based control systems—largely lacking specialized selection mechanisms—the emergence of bilaterians may have forced the evolution of a central ganglion, or “archaic brain”, whose main function is to resolve conflicts between peripheral systems. Whilst vertebrates have many interesting selection substrates it is likely that here too the evolution of centralized structures such as the medial reticular formation and the basal ganglia may have been a forced move because of the need to limit connection costs as brains increased in size
Spatial Learning and Action Planning in a Prefrontal Cortical Network Model
The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to
spatial cognition. Complementing hippocampal place coding, prefrontal
representations provide more abstract and hierarchically organized memories
suitable for decision making. We model a prefrontal network mediating
distributed information processing for spatial learning and action planning.
Specific connectivity and synaptic adaptation principles shape the recurrent
dynamics of the network arranged in cortical minicolumns. We show how the PFC
columnar organization is suitable for learning sparse topological-metrical
representations from redundant hippocampal inputs. The recurrent nature of the
network supports multilevel spatial processing, allowing structural features of
the environment to be encoded. An activation diffusion mechanism spreads the
neural activity through the column population leading to trajectory planning.
The model provides a functional framework for interpreting the activity of PFC
neurons recorded during navigation tasks. We illustrate the link from single
unit activity to behavioral responses. The results suggest plausible neural
mechanisms subserving the cognitive “insight” capability originally
attributed to rodents by Tolman & Honzik. Our time course analysis of neural
responses shows how the interaction between hippocampus and PFC can yield the
encoding of manifold information pertinent to spatial planning, including
prospective coding and distance-to-goal correlates
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