3,641 research outputs found
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Integrated information increases with fitness in the evolution of animats
One of the hallmarks of biological organisms is their ability to integrate
disparate information sources to optimize their behavior in complex
environments. How this capability can be quantified and related to the
functional complexity of an organism remains a challenging problem, in
particular since organismal functional complexity is not well-defined. We
present here several candidate measures that quantify information and
integration, and study their dependence on fitness as an artificial agent
("animat") evolves over thousands of generations to solve a navigation task in
a simple, simulated environment. We compare the ability of these measures to
predict high fitness with more conventional information-theoretic processing
measures. As the animat adapts by increasing its "fit" to the world,
information integration and processing increase commensurately along the
evolutionary line of descent. We suggest that the correlation of fitness with
information integration and with processing measures implies that high fitness
requires both information processing as well as integration, but that
information integration may be a better measure when the task requires memory.
A correlation of measures of information integration (but also information
processing) and fitness strongly suggests that these measures reflect the
functional complexity of the animat, and that such measures can be used to
quantify functional complexity even in the absence of fitness data.Comment: 27 pages, 8 figures, one supplementary figure. Three supplementary
video files available on request. Version commensurate with published text in
PLoS Comput. Bio
Braitenberg Vehicles as Developmental Neurosimulation
The connection between brain and behavior is a longstanding issue in the
areas of behavioral science, artificial intelligence, and neurobiology.
Particularly in artificial intelligence research, behavior is generated by a
black box approximating the brain. As is standard among models of artificial
and biological neural networks, an analogue of the fully mature brain is
presented as a blank slate. This model generates outputs and behaviors from a
priori associations, yet this does not consider the realities of biological
development and developmental learning. Our purpose is to model the development
of an artificial organism that exhibits complex behaviors. We will introduce
our approach, which is to use Braitenberg Vehicles (BVs) to model the
development of an artificial nervous system. The resulting developmental BVs
will generate behaviors that range from stimulus responses to group behavior
that resembles collective motion. Next, we will situate this work in the domain
of artificial brain networks. Then we will focus on broader themes such as
embodied cognition, feedback, and emergence. Our perspective will then be
exemplified by three software instantiations that demonstrate how a BV-genetic
algorithm hybrid model, multisensory Hebbian learning model, and multi-agent
approaches can be used to approach BV development. We introduce use cases such
as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges
connecting behavioral and neural models (multisensory Hebbian learning model),
and cumulative classification (multi-agent approaches). In conclusion, we will
revisit concepts related to our approach and how they might guide future
development.Comment: 32 pages, 8 figures, 2 table
Intelligence Without Reason
Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation
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
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