2015
Neuronal boost to evolutionary dynamics
Harold P. de Vladar, Eörs Szathmáry
Interface Focus 5: 20150074, 2015
Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild.
Coupled Hebbian learning and evolutionary dynamics in a formal model for structural synaptic plasticity
H.P. Vladar, E. Szathmary
arXiv:1505.04432v1 [q-bio.NC] 17 May 2015
Theoretical models of neuronal function consider dierent mechanisms through which networks learn, classify and discern inputs. A central focus of these models is to understand how associations are established amongst neurons, in order to predict spiking patterns that are compatible with empirical observations. Although these models have led to major insights and advances, they still do not account for the astonishing velocity with which the brain solves certain problems and what lies behind its creativity, amongst others features. We examine two important components that may crucially aid comprehensive understanding of said neurodynamical processes. First, we argue that once presented with a problem, dierent putative solutions are generated in parallel by dierent groups or local neuronal complexes, with the subsequent stabilization and spread of the best solutions. Using mathematical models we show that this mechanism accelerates nding the right solutions. This formalism is analogous to standard replicator-mutator models of evolution where mutation is analogous to the probability of neuron state switching (on/o). Although in evolution mutation rates are constant, we show that neuronal switching probability is determined by neuronal activity and their associative weights, described by the network of synaptic connections. The second factor that we incorporate is structural synaptic plasticity, i.e. the making of new and disbanding of old synapses, which we apply as a dynamical reorganization of synaptic connections. We show that Hebbian learning alone does not suce to reach optimal solutions. However, combining it with parallel evaluation and structural plasticity opens up possibilities for ecient problem solving. In the resulting networks, topologies converge to subsets of fully connected components. Imposing costs on synapses reduces the connectivity, although the number of connected components remains robust. The average lifetime of synapses is longer for connections that are established early, and diminishes with synaptic cost.
Search of Complex Binary Cellular Automata Using Behavioral Metrics
Juan C. López-González, Antonio Rueda-Toicen
Complex Systems, Volume 24, Issue 1 (arXiv.org)
We propose the characterization of binary cellular automata using a set of behavioral metrics based on an evaluation heuristic derived from elementary cellular automata. Behaviors characterized through these metrics are growth, decrease, chaoticity, and stability. From these metrics, two measures of global behavior are calculated: 1) a static measure that considers all possible input patterns and counts the occurrence of the proposed metrics in the truth table of the minimal Boolean expression of the automaton; 2) a dynamic measure, corresponding to the mean of the behavioral metrics in n executions of the automaton, starting from n random initial states. The correlation between these measures is used to guide a genetic search algorithm, which selects cellular automata similar to the Game of Life. Using this method, we found an extensive set of complex binary cellular automata with interesting properties, including self-replication.
Fractals in the neurosciences, part II: Clinical applications and future perspectives
Antonio Di Ieva, Francisco J. Esteban, Fabio Grizzi, Wlodzimierz Klonowski, Miguel Martín-Landrove
The Neuroscientist. 2015, Vol. 21(1) 30–43
It has been ascertained that the human brain is a complex system studied at multiple scales, from neurons and microcircuits to macronetworks. The brain is characterized by a hierarchical organization that gives rise to its highly topological and functional complexity. Over the last decades, fractal geometry has been shown as a universal tool for the analysis and quantification of the geometric complexity of natural objects, including the brain. The fractal dimension has been identified as a quantitative parameter for the evaluation of the roughness of neural structures, the estimation of time series, and the description of patterns, thus able to discriminate different states of the brain in its entire physiopathological spectrum. Fractal-based computational analyses have been applied to the neurosciences, particularly in the field of clinical neurosciences including neuroimaging and neuroradiology, neurology and neurosurgery, psychiatry and psychology, and neuro-oncology and neuropathology. After a review of the basic concepts of fractal analysis and its main applications to the basic neurosciences in part I of this series, here, we review the main applications of fractals to the clinical neurosciences for a holistic approach towards a fractal geometry model of the brain.
Medical robotic system guided by active vision: Handling a laparoscope
Prieto Ceron, C.E., Rodriguez, O.
2nd Colombian Conference on Automatic Control (CCAC), 14-16 Oct. 2015
The design of the structure of a robot kinematic chain to allow a manipulation of a laparoscope is shown. Use of computer active vision to guide the movement and define trajectories becomes the robotic system. We work a case study on the trajectory of a robotic manipulator designed for surgery under laparoscopic procedure.
GDR in Radiotherapy Treatment Fields with 18 MV Accelerators
R. Martín-Landrove, J. Dávila, H.R. Vega-Carrillo, M.T. Barrera, A.J. Kreiner, F. Pino, H. Barros, E.D. Greaves, L. Sajo-Bohus.
Proceedings of the 14th International Conference on Nuclear Reaction Mechanisms, At Geneva, Italy, 2015
Giant dipole photo-nuclear reactions generated during Linac radiotherapy are of concern due to the undesirable neutron dose delivered to patients. Nuclear track methodology provides an estimation of gradients of photo-neutrons fields in radiotherapy treatments for 18 MV linear accelerators and it revealed an unexpected behaviour around isocenter. Enhancement effects are observed on absorbed dose due to both scattered photo-neutrons and (,n) reactions. Thermal neutrons can give a dose boost if the tumour is loaded with 10B and a new BNCT approach combined with the standard photon field is proposed.
Extracting Information about the Rotator Cuff from Magnetic
Resonance Images Using Deterministic and Random Techniques
F. A. De Los Ríos, M. Paluszny
Computational and Mathematical Methods in Medicine, Volume 2015, Article ID 974562,
We consider some methods to extract information about the rotator cuff based on magnetic resonance images; the study aims todefine an alternative method of display that might facilitate the detection of partial tears in the supraspinatus tendon. Specifically,we are going to use families of ellipsoidal triangular patches to cover the humerus head near the affected area.These patches are going to be textured and displayed with the information of the magnetic resonance images using the trilinear interpolation technique. For the generation of points to texture each patch, we propose a new method that guarantees the uniform distribution of its points using a random statistical method. Its computational cost, defined as the average computing time to generate a fixed number of points, is significantly lower as compared with deterministic and other standard statistical techniques.
Odontological Information Along Cone Splines
Cindy González, Marco Paluszny
Analysis, Modelling, Optimization and Numerical Techniques, pp 219-233, 19 March 2015
Developable surfaces are a subset of ruled surfaces, which can be mapped onto a plane without deformation. Due to this property, they have considerable relevance in several applications. In the medical area, regarding information visualization along sections of organs, they could be useful in clinical diagnosis. They have also industrial applications, including footwear and clothing industries, where three-dimensional (3D) designs are made from flat materials. In this research, we consider the issue of approximating developable surfaces with segments of circular cones, with the aim of constructing splines that model interesting surfaces. Our emphasis will be in the odontological area. We present examples of “panoramic views” of curved sections of human jaw which contain information about all the dental pieces. Moreover, the process allows for the simultaneous display of these pieces in a flat surface, without metric distortion.
An automatic method for the enrichment of DICOM metadata using biomedical ontologies
Wilson Pérez, Andrés Tello, Víctor Saquicela, Maria-Esther Vidal, Alexandra La Cruz
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2551-2554
This work is a novel contribution for enriching medical images using semantic annotations with a strategy for unifying different ontologies and instances of DICOM medical files. We present the L-MOM library (Library for Mapping of Ontological Metadata) as a tool for making an automatic mapping between instances of DICOM medical files and different medical ontologies (e.g., FMA, RadLex, MeSH). The main contributions are: i) the domain independent L-MOM library which is able to integrate DICOM metadata with ontologies from different domains; ii) a strategy to automatically annotate DICOM data with universally accepted medical ontologies, and provide values of similarity between ontologies and DICOM metadata; and iii) a framework to traverse ontological concepts that characterized clinical studies of patients registered in the framework catalog.
