64 research outputs found
Assessing the robustness of critical behavior in stochastic cellular automata
There is evidence that biological systems, such as the brain, work at a critical regime robust to noise, and are therefore able to remain in it under perturbations. In this work, we address the question of robustness of critical systems to noise. In particular, we investigate the robustness of stochastic cellular automata (CAs) at criticality. A stochastic CA is one of the simplest stochastic models showing criticality. The transition state of stochastic CA is defined through a set of probabilities. We systematically perturb the probabilities of an optimal stochastic CA known to produce critical behavior, and we report that such a CA is able to remain in a critical regime up to a certain degree of noise. We present the results using error metrics of the resulting power-law fitting, such as Kolmogorov–Smirnov statistic and Kullback–Leibler divergence. We discuss the implication of our results in regards to future realization of brain-inspired artificial intelligence systems.publishedVersio
Towards a Plant Bio-Machine
Plants are very efficient computing machines. They are able to sense diverse environmental conditions and quickly react through chemical and electrical signaling. In this paper, we present an interface between plants and machines (a cybernetic plant), with the goal of augmenting the capabilities of plants towards the creation of plant biosensors. We implement a data acquisition system able to stimulate the plant through different electrical signals, as well as record the electrical activity of plants in response to changing electrical stimulations, light conditions, and chemicals. The results serve as a proof of concept that sensing capabilities of plants are a viable option for the development of plant bio-machines. Different future scenarios (some speculative) are discussed. The work herein is carried out as a collaboration between the EU project Flora Robotica and the EU project NASCENCE
CA-NEAT: Evolved Compositional Pattern Producing Networks for Cellular Automata Morphogenesis and Replication
Cellular Automata (CA) are a remarkable example of morphogenetic system, where cells grow and self-organise through local interactions. CA have been used as abstractions of biological development and artificial life. Such systems have been able to show properties that are often desirable but difficult to achieve in engineered systems, e.g. morphogenesis and replication of regular patterns without any form of centralized coordination. However, cellular systems are hard to program (i.e. evolve) and control, especially when the number of cell states and neighbourhood increase. In this paper, we propose a new principle of morphogenesis based on Compositional Pattern Producing Networks (CPPNs), an abstraction of development that has been able to produce complex structural motifs without local interactions. CPPNs are used as Cellular Automata genotypes and evolved with a NeuroEvolution of Augmenting Topologies (NEAT) algorithm. This allows complexification of genomes throughout evolution with phenotypes emerging from self-organisation through development based on local interactions. In this paper, the problems of 2D pattern morphogenesis and replication are investigated. Results show that CA-NEAT is an appropriate means of approaching cellular systems engineering, especially for future applications where natural levels of complexity are targeted. We argue that CA-NEAT could provide a valuable mapping for morphogenetic systems, beyond cellular automata systems, where development through local interactions is desired
Evolved Art with Transparent, Overlapping, and Geometric Shapes
In this work, an evolutionary art project is presented where images are
approximated by transparent, overlapping and geometric shapes of different
types, e.g., polygons, circles, lines. Genotypes representing features and
order of the geometric shapes are evolved with a fitness function that has the
corresponding pixels of an input image as a target goal. A
genotype-to-phenotype mapping is therefore applied to render images, as the
chosen genetic representation is indirect, i.e., genotypes do not include
pixels but a combination of shapes with their properties. Different
combinations of shapes, quantity of shapes, mutation types and populations are
tested. The goal of the work herein is twofold: (1) to approximate images as
precisely as possible with evolved indirect encodings, (2) to produce visually
appealing results and novel artistic styles.Comment: Proceedings of the Norwegian AI Symposium 2019 (NAIS 2019),
Trondheim, Norwa
Collective control of modular soft robots via embodied Spiking Neural Cellular Automata
Voxel-based Soft Robots (VSRs) are a form of modular soft robots, composed of
several deformable cubes, i.e., voxels. Each VSR is thus an ensemble of simple
agents, namely the voxels, which must cooperate to give rise to the overall VSR
behavior. Within this paradigm, collective intelligence plays a key role in
enabling the emerge of coordination, as each voxel is independently controlled,
exploiting only the local sensory information together with some knowledge
passed from its direct neighbors (distributed or collective control). In this
work, we propose a novel form of collective control, influenced by Neural
Cellular Automata (NCA) and based on the bio-inspired Spiking Neural Networks:
the embodied Spiking NCA (SNCA). We experiment with different variants of SNCA,
and find them to be competitive with the state-of-the-art distributed
controllers for the task of locomotion. In addition, our findings show
significant improvement with respect to the baseline in terms of adaptability
to unforeseen environmental changes, which could be a determining factor for
physical practicability of VSRs.Comment: Workshop on "From Cells to Societies: Collective Learning across
Scales" at the International Conference on Learning Representations
(Cells2Societies@ICLR
Characterization of spin-orbit interactions of GaAs heavy holes using a quantum point contact
We present transport experiments performed in high quality quantum point
contacts embedded in a GaAs two-dimensional hole gas. The strong spin-orbit
interaction results in peculiar transport phenomena, including the previously
observed anisotropic Zeeman splitting and level-dependent effective g-factors.
Here we find additional effects, namely the crossing and the anti-crossing of
spin-split levels depending on subband index and magnetic field direction. Our
experimental observations are reconciled in an heavy hole effective spin-orbit
Hamiltonian where cubic- and quadratic-in-momentum terms appear. The spin-orbit
components, being of great importance for quantum computing applications, are
characterized in terms of magnitude and spin structure. In the light of our
results, we explain the level dependent effective g-factor in an in-plane
field. Through a tilted magnetic field analysis, we show that the QPC
out-of-plane g-factor saturates around the predicted 7.2 bulk value
Optimization of a Hydrodynamic Computational Reservoir through Evolution
As demand for computational resources reaches unprecedented levels, research
is expanding into the use of complex material substrates for computing. In this
study, we interface with a model of a hydrodynamic system, under development by
a startup, as a computational reservoir and optimize its properties using an
evolution in materio approach. Input data are encoded as waves applied to our
shallow water reservoir, and the readout wave height is obtained at a fixed
detection point. We optimized the readout times and how inputs are mapped to
the wave amplitude or frequency using an evolutionary search algorithm, with
the objective of maximizing the system's ability to linearly separate
observations in the training data by maximizing the readout matrix determinant.
Applying evolutionary methods to this reservoir system substantially improved
separability on an XNOR task, in comparison to implementations with
hand-selected parameters. We also applied our approach to a regression task and
show that our approach improves out-of-sample accuracy. Results from this study
will inform how we interface with the physical reservoir in future work, and we
will use these methods to continue to optimize other aspects of the physical
implementation of this system as a computational reservoir.Comment: Accepted at the 2023 Genetic and Evolutionary Computation Conference
(GECCO 2023). 9 pages, 8 figure
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