13,175 research outputs found
Session 5: Development, Neuroscience and Evolutionary Psychology
Proceedings of the Pittsburgh Workshop in History and Philosophy of Biology, Center for Philosophy of Science, University of Pittsburgh, March 23-24 2001 Session 5: Development, Neuroscience and Evolutionary Psycholog
New Ideas for Brain Modelling
This paper describes some biologically-inspired processes that could be used
to build the sort of networks that we associate with the human brain. New to
this paper, a 'refined' neuron will be proposed. This is a group of neurons
that by joining together can produce a more analogue system, but with the same
level of control and reliability that a binary neuron would have. With this new
structure, it will be possible to think of an essentially binary system in
terms of a more variable set of values. The paper also shows how recent
research associated with the new model, can be combined with established
theories, to produce a more complete picture. The propositions are largely in
line with conventional thinking, but possibly with one or two more radical
suggestions. An earlier cognitive model can be filled in with more specific
details, based on the new research results, where the components appear to fit
together almost seamlessly. The intention of the research has been to describe
plausible 'mechanical' processes that can produce the appropriate brain
structures and mechanisms, but that could be used without the magical
'intelligence' part that is still not fully understood. There are also some
important updates from an earlier version of this paper
An evolutionary behavioral model for decision making
For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud
of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud
the same evolutionary mechanism is able to assemble quite\ud
complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies
Investigation of sequence processing: A cognitive and computational neuroscience perspective
Serial order processing or sequence processing underlies
many human activities such as speech, language, skill
learning, planning, problem-solving, etc. Investigating
the neural bases of sequence processing enables us to
understand serial order in cognition and also helps in
building intelligent devices. In this article, we review
various cognitive issues related to sequence processing
with examples. Experimental results that give evidence
for the involvement of various brain areas will be described.
Finally, a theoretical approach based on statistical
models and reinforcement learning paradigm is
presented. These theoretical ideas are useful for studying
sequence learning in a principled way. This article
also suggests a two-way process diagram integrating
experimentation (cognitive neuroscience) and theory/
computational modelling (computational neuroscience).
This integrated framework is useful not only in the present
study of serial order, but also for understanding
many cognitive processes
Progressive Neural Architecture Search
We propose a new method for learning the structure of convolutional neural
networks (CNNs) that is more efficient than recent state-of-the-art methods
based on reinforcement learning and evolutionary algorithms. Our approach uses
a sequential model-based optimization (SMBO) strategy, in which we search for
structures in order of increasing complexity, while simultaneously learning a
surrogate model to guide the search through structure space. Direct comparison
under the same search space shows that our method is up to 5 times more
efficient than the RL method of Zoph et al. (2018) in terms of number of models
evaluated, and 8 times faster in terms of total compute. The structures we
discover in this way achieve state of the art classification accuracies on
CIFAR-10 and ImageNet.Comment: To appear in ECCV 2018 as oral. The code and checkpoint for PNASNet-5
trained on ImageNet (both Mobile and Large) can now be downloaded from
https://github.com/tensorflow/models/tree/master/research/slim#Pretrained.
Also see https://github.com/chenxi116/PNASNet.TF for refactored and
simplified TensorFlow code; see https://github.com/chenxi116/PNASNet.pytorch
for exact conversion to PyTorc
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