3,768 research outputs found
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
Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence
Spreading processes are conventionally monitored on a macroscopic level by
counting the number of incidences over time. The spreading process can then be
modeled either on the microscopic level, assuming an underlying interaction
network, or directly on the macroscopic level, assuming that microscopic
contributions are negligible. The macroscopic characteristics of both
descriptions are commonly assumed to be identical. In this work, we show that
these characteristics of microscopic and macroscopic descriptions can be
different due to coalescence, i.e., a node being activated at the same time by
multiple sources. In particular, we consider a (microscopic) branching network
(probabilistic cellular automaton) with annealed connectivity disorder, record
the macroscopic activity, and then approximate this activity by a (macroscopic)
branching process. In this framework, we analytically calculate the effect of
coalescence on the collective dynamics. We show that coalescence leads to a
universal non-linear scaling function for the conditional expectation value of
successive network activity. This allows us to quantify the difference between
the microscopic model parameter and established macroscopic estimates. To
overcome this difference, we propose a non-linear estimator that correctly
infers the model branching parameter for all system sizes.Comment: 13 page
Informative and misinformative interactions in a school of fish
It is generally accepted that, when moving in groups, animals process
information to coordinate their motion. Recent studies have begun to apply
rigorous methods based on Information Theory to quantify such distributed
computation. Following this perspective, we use transfer entropy to quantify
dynamic information flows locally in space and time across a school of fish
during directional changes around a circular tank, i.e. U-turns. This analysis
reveals peaks in information flows during collective U-turns and identifies two
different flows: an informative flow (positive transfer entropy) based on fish
that have already turned about fish that are turning, and a misinformative flow
(negative transfer entropy) based on fish that have not turned yet about fish
that are turning. We also reveal that the information flows are related to
relative position and alignment between fish, and identify spatial patterns of
information and misinformation cascades. This study offers several
methodological contributions and we expect further application of these
methodologies to reveal intricacies of self-organisation in other animal groups
and active matter in general
White Paper 5: Brain, Mind & Behaviour
© CSICThe study of the brain will tell us what makes us humans and how our social behavior generates. Increasing our understanding of how the brain functions and interacts with the ecosystem to interpret the world will not only help to find effective means to treat and/or cure neurological and psychiatric disorders but will also change our vision on questions pertaining to philosophy and humanities and transform other fields such as economy and law. Neurosciences research at the CSIC is already valuable and should be intensified mainly focused on the eight major challenges described in this volume
Challenge 2: From genes & circuits to behavior
Understanding the brain from genes and circuits to behavior is a major scientific challenge. The large repertoire of cell activities supporting behavior stems from an equally diverse range of specialized cell types, from neuron to glia. To untangle mechanisms underlying brain function, elementary processes should be dissected, from the complex machinery of signaling pathways at the level of single cells and synapses, to the intricate phenomena leading to orchestrated ensemble activity and the establishment of engrams driving memory-guided behaviors. In this chapter we identify the main key tasks required to address some of the open questions in the field, and discuss on the main issues and strategies
French Roadmap for complex Systems 2008-2009
This second issue of the French Complex Systems Roadmap is the outcome of the
Entretiens de Cargese 2008, an interdisciplinary brainstorming session
organized over one week in 2008, jointly by RNSC, ISC-PIF and IXXI. It
capitalizes on the first roadmap and gathers contributions of more than 70
scientists from major French institutions. The aim of this roadmap is to foster
the coordination of the complex systems community on focused topics and
questions, as well as to present contributions and challenges in the complex
systems sciences and complexity science to the public, political and industrial
spheres
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
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