372 research outputs found
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement
The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, but both belong to the category of felines. In other words, tigers and leopards are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in the computational neurosciences. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the approach successully establishes category and subcategory representations
3D Maps Representation Using GNG
Current RGB-D sensors provide a big amount of valuable information for mobile robotics tasks like 3D map reconstruction, but the storage and processing of the incremental data provided by the different sensors through time quickly become unmanageable. In this work, we focus on 3D maps representation and propose the use of the Growing Neural Gas (GNG) network as a model to represent 3D input data. GNG method is able to represent the input data with a desired amount of neurons or resolution while preserving the topology of the input space. Experiments show how GNG method yields a better input space adaptation than other state-of-the-art 3D map representation methods.This work was partially funded by the Spanish Government DPI2013-40534-R grant
An incremental clustering and associative learning architecture for intelligent robotics
The ability to learn from the environment and memorise the acquired knowledge is
essential for robots to become autonomous and versatile artificial companions. This
thesis proposes a novel learning and memory architecture for robots, which performs
associative learning and recall of sensory and actuator patterns. The approach
avoids the inclusion of task-specific expert knowledge and can deal with any kind of
multi-dimensional real-valued data, apart from being tolerant to noise and supporting
incremental learning. The proposed architecture integrates two machine learning
methods: a topology learning algorithm that performs incremental clustering, and
an associative memory model that learns relationship information based on the
co-occurrence of inputs.
The evaluations of both the topology learning algorithm and the associative
memory model involved the memorisation of high-dimensional visual data as well as
the association of symbolic data, presented simultaneously and sequentially. Moreover,
the document analyses the results of two experiments in which the entire architecture
was evaluated regarding its associative and incremental learning capabilities. One
experiment comprised an incremental learning task with visual patterns and text
labels, which was performed both in a simulated scenario and with a real robot. In a
second experiment a robot learned to recognise visual patterns in the form of road
signs and associated them with di erent con gurations of its arm joints.
The thesis also discusses several learning-related aspects of the architecture
and highlights strengths and weaknesses of the proposed approach. The developed
architecture and corresponding ndings contribute to the domains of machine learning
and intelligent robotics
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