310 research outputs found
Design of artificial neural oscillatory circuits for the control of lamprey- and salamander-like locomotion using evolutionary algorithms
This dissertation investigates the evolutionary design of oscillatory artificial neural
networks for the control of animal-like locomotion. It is inspired by the neural organ¬
isation of locomotor circuitries in vertebrates, and explores in particular the control
of undulatory swimming and walking. The difficulty with designing such controllers
is to find mechanisms which can transform commands concerning the direction and
the speed of motion into the multiple rhythmic signals sent to the multiple actuators
typically involved in animal-like locomotion. In vertebrates, such control mechanisms
are provided by central pattern generators which are neural circuits capable of pro¬
ducing the patterns of oscillations necessary for locomotion without oscillatory input
from higher control centres or from sensory feedback. This thesis explores the space of
possible neural configurations for the control of undulatory locomotion, and addresses
the problem of how biologically plausible neural controllers can be automatically generated.Evolutionary algorithms are used to design connectionist models of central pattern
generators for the motion of simulated lampreys and salamanders. This work is inspired
by Ekeberg's neuronal and mechanical simulation of the lamprey [Ekeberg 93]. The
first part of the thesis consists of developing alternative neural controllers for a similar
mechanical simulation. Using a genetic algorithm and an incremental approach, a
variety of controllers other than the biological configuration are successfully developed
which can control swimming with at least the same efficiency. The same method
is then used to generate synaptic weights for a controller which has the observed
biological connectivity in order to illustrate how the genetic algorithm could be used
for developing neurobiological models. Biologically plausible controllers are evolved
which better fit physiological observations than Ekeberg's hand-crafted model. Finally,
in collaboration with Jerome Kodjabachian, swimming controllers are designed using a
developmental encoding scheme, in which developmental programs are evolved which
determine how neurons divide and get connected to each other on a two-dimensional
substrate.The second part of this dissertation examines the control of salamander-like swimming
and trotting. Salamanders swim like lampreys but, on the ground, they switch to a
trotting gait in which the trunk performs a standing wave with the nodes at the girdles.
Little is known about the locomotion circuitry of the salamander, but neurobiologists
have hypothesised that it is based on a lamprey-like organisation. A mechanical sim¬
ulation of a salamander-like animat is developed, and neural controllers capable of
exhibiting the two types of gaits are evolved. The controllers are made of two neural
oscillators projecting to the limb motoneurons and to lamprey-like trunk circuitry. By
modulating the tonic input applied to the networks, the type of gait, the speed and
the direction of motion can be varied.By developing neural controllers for lamprey- and salamander-like locomotion, this
thesis provides insights into the biological control of undulatory swimming and walking, and shows how evolutionary algorithms can be used for developing neurobiological
models and for generating neural controllers for locomotion. Such a method could potentially be used for designing controllers for swimming or walking robots, for instance
Complex Dynamics in Dedicated / Multifunctional Neural Networks and Chaotic Nonlinear Systems
We study complex behaviors arising in neuroscience and other nonlinear systems by combining dynamical systems analysis with modern computational approaches including GPU parallelization and unsupervised machine learning. To gain insights into the behaviors of brain networks and complex central pattern generators (CPGs), it is important to understand the dynamical principles regulating individual neurons as well as the basic structural and functional building blocks of neural networks. In the first section, we discuss how symbolic methods can help us analyze neural dynamics such as bursting, tonic spiking and chaotic mixed-mode oscillations in various models of individual neurons, the bifurcations that underlie transitions between activity types, as well as emergent network phenomena through synergistic interactions seen in realistic neural circuits, such as network bursting from non-intrinsic bursters. The second section is focused on the origin and coexistence of multistable rhythms in oscillatory neural networks of inhibitory coupled cells. We discuss how network connectivity and intrinsic properties of the cells affect the dynamics, and how even simple circuits can exhibit a variety of mono/multi-stable rhythms including pacemakers, half-center oscillators, multiple traveling-waves, fully synchronous states, as well as various chimeras. Our analyses can help generate verifiable hypotheses for neurophysiological experiments on central pattern generators. In the last section, we demonstrate the inter-disciplinary nature of this research through the applications of these techniques to identify the universal principles governing both simple and complex dynamics, and chaotic structure in diverse nonlinear systems. Using a classical example from nonlinear laser optics, we elaborate on the multiplicity and self-similarity of key organizing structures in 2D parameter space such as homoclinic and heteroclinic bifurcation curves, Bykov T-point spirals, and inclination flips. This is followed by detailed computational reconstructions of the spatial organization and 3D embedding of bifurcation surfaces, parametric saddles, and isolated closed curves (isolas). The generality of our modeling approaches could lead to novel methodologies and nonlinear science applications in biological, medical and engineering systems
Bio-Inspired Robotics
Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field
Natural smartness in hypothetical animals. Of paddlers and glowballs
To obtain a reasonably self-contained and complete simulation of navigational sensori-motor behaviour, a neuroethological model of a hypothetical animal, the paddler, has been developed
- …