23,969 research outputs found
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Toward bio-inspired information processing with networks of nano-scale switching elements
Unconventional computing explores multi-scale platforms connecting
molecular-scale devices into networks for the development of scalable
neuromorphic architectures, often based on new materials and components with
new functionalities. We review some work investigating the functionalities of
locally connected networks of different types of switching elements as
computational substrates. In particular, we discuss reservoir computing with
networks of nonlinear nanoscale components. In usual neuromorphic paradigms,
the network synaptic weights are adjusted as a result of a training/learning
process. In reservoir computing, the non-linear network acts as a dynamical
system mixing and spreading the input signals over a large state space, and
only a readout layer is trained. We illustrate the most important concepts with
a few examples, featuring memristor networks with time-dependent and history
dependent resistances
Shannon entropy of brain functional complex networks under the influence of the psychedelic Ayahuasca
The entropic brain hypothesis holds that the key facts concerning
psychedelics are partially explained in terms of increased entropy of the
brain's functional connectivity. Ayahuasca is a psychedelic beverage of
Amazonian indigenous origin with legal status in Brazil in religious and
scientific settings. In this context, we use tools and concepts from the theory
of complex networks to analyze resting state fMRI data of the brains of human
subjects under two distinct conditions: (i) under ordinary waking state and
(ii) in an altered state of consciousness induced by ingestion of Ayahuasca. We
report an increase in the Shannon entropy of the degree distribution of the
networks subsequent to Ayahuasca ingestion. We also find increased local and
decreased global network integration. Our results are broadly consistent with
the entropic brain hypothesis. Finally, we discuss our findings in the context
of descriptions of "mind-expansion" frequently seen in self-reports of users of
psychedelic drugs.Comment: 27 pages, 6 figure
Metastability, Criticality and Phase Transitions in brain and its Models
This essay extends the previously deposited paper "Oscillations, Metastability and Phase Transitions" to incorporate the theory of Self-organizing Criticality. The twin concepts of Scaling and Universality of the theory of nonequilibrium phase transitions is applied to the role of reentrant activity in neural circuits of cerebral cortex and subcortical neural structures
Revealing networks from dynamics: an introduction
What can we learn from the collective dynamics of a complex network about its
interaction topology? Taking the perspective from nonlinear dynamics, we
briefly review recent progress on how to infer structural connectivity (direct
interactions) from accessing the dynamics of the units. Potential applications
range from interaction networks in physics, to chemical and metabolic
reactions, protein and gene regulatory networks as well as neural circuits in
biology and electric power grids or wireless sensor networks in engineering.
Moreover, we briefly mention some standard ways of inferring effective or
functional connectivity.Comment: Topical review, 48 pages, 7 figure
- …