Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

A framework for quantification and physical modeling of cell mixing applied to oscillator synchronization in vertebrate somitogenesis

In development and disease, cells move as they exchange signals. One example is found in vertebrate development, during which the timing of segment formation is set by a ‘segmentation clock’, in which oscillating gene expression is synchronized across a population of cells by Delta-Notch signaling. Delta-Notch signaling requires local cell-cell contact, but in the zebrafish embryonic tailbud, oscillating cells move rapidly, exchanging neighbors. Previous theoretical studies proposed that this relative movement or cell mixing might alter signaling and thereby enhance synchronization. However, it remains unclear whether the mixing timescale in the tissue is in the right range for this effect, because a framework to reliably measure the mixing timescale and compare it with signaling timescale is lacking. Here, we develop such a framework using a quantitative description of cell mixing without the need for an external reference frame and constructing a physical model of cell movement based on the data. Numerical simulations show that mixing with experimentally observed statistics enhances synchronization of coupled phase oscillators, suggesting that mixing in the tailbud is fast enough to affect the coherence of rhythmic gene expression. Our approach will find general application in analyzing the relative movements of communicating cells during development and disease.Fil: Uriu, Koichiro. Kanazawa University; JapónFil: Bhavna, Rajasekaran. Max Planck Institute of Molecular Cell Biology and Genetics; Alemania. Max Planck Institute for the Physics of Complex Systems; AlemaniaFil: Oates, Andrew C.. Francis Crick Institute; Reino Unido. University College London; Reino UnidoFil: Morelli, Luis Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina. Max Planck Institute for Molecular Physiology; Alemania. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentin

Network Dynamics, Synchronization, and Self-Propelled Particles in Chemical Systems

Neural networks are a class of biological networks of great importance. They are a key component of the central nervous system that coordinates body functions. The exploration of the detailed mechanism of biological neural networks remains extremely active. Inspired by the structure of biological neural networks, artificial neural networks have been designed to solve a variety of problems in pattern recognition, prediction, optimization and control. However, few studies have been reported that explore the dynamics of biological neural networks using chemical systems. As part of this thesis, an experimentally trainable network based on the photosensitive Belousov-Zhabotinsky reaction is developed, where the individual node is a catalyst loaded micro-particle. The interactions between nodes in the network are created by arranging links with different weights, similar to the excitable and inhibitory synapses in biological neural networks. The distribution of the weights of the excitable links has been studied. The results indicate that a stable distribution of the weights is exhibited.;Synchronization in coupled nonlinear oscillators is a remarkable and ubiquitous phenomenon in nature. Application of periodic global feedback to oscillators allows the creation of new kinds of wave patterns with the coexistence of stable phase states. In experiments with the photosensitive BZ reaction, periodic global feedback is implemented by varying the illumination intensity. In a 1:1 frequency-locked entrainment, 2pi phase fronts called phase kinks have been observed in the photosensitive BZ reaction. Generally, a phase kink represents the existence of stable phase differences, propagating as an analog of traveling waves in 2D excitable media. By modifying the conditions of local forcing, the experiments show that a phase kink can be trapped to form a closed pattern.;Self-propulsion is an essential feature of many living systems. There are numerous realizations of self-propelled particles in biological systems, such as the bacteria Listeria monocytogenes in cells. Such biological phenomena inspire the creation of artificial self-propelled particles. Recently, nonbiological micro- to nanoscale particles, that convert chemical energy into translational motion, have been investigated. Studies show that Pt-coated polystyrene particles, coated on one hemisphere with Pt, exhibit self-propulsion in dilute H2O2 solutions. Here, we experimentally study the dynamical behavior of silica particles that are asymmetrically coated with Pt in H2O2 solutions, similar to Pt-coated polystyrene particles. The focus of our study is on the particle orientation with respect to the direction of motion. This is investigated using velocity autocorrelation and propulsion direction analyses