420 research outputs found
Modeling the chemotaxis behaviors of C. Elegans using neural network: from artificial to biological approach
Ph.DDOCTOR OF PHILOSOPH
Improvements in optical techniques to investigate the behavior and neuronal network dynamics over long timescales
Developments in optical technology have produced an important shift in experimental neuroscience from electrophysiological methods for observation and stimulation to all-optical solutions. One expects this trend to continue as future developments continue to deliver, and improve upon, the original promises of the technology: 1) minimally invasive actuation and recording of neurons, and 2) a drastic increase in targets that can be treated simultaneously. Moreover, as the high costs of the technology are reduced, one may expect its larger-scale adoption in the neuroscience community. In this thesis, I describe the development and implementation of two alloptical solutions for the analysis of behavior, neuronal signaling, and stimulation, which improve on previous state-of-the-art: (1) A minimally-invasive, high signal-to-noise twophoton microscopy setup capable of simultaneous, live-imaging of a large subset of sensory neurons post activation, and (2) a low-cost tracking solution to stimulate and record behavior. I begin this thesis with a review of recent advances in optical neuroscience techniques for the study of neuronal networks with the focus on work done in Caenorhabditis elegans. Then, in chapter 2, I describe my implementation of a two-photon temporal focusing microscopy setup and show significant improvements through the use of a high power/ high pulse repetition rate excitation system, enabling live imaging with high resolution for extended periods of time. I model temperature increase during a physiological imaging scenario for different repetition rates at fixed peak intensities and find range centered around 1 MHz to be optimal. Lastly, I describe the low-cost tracking setup with the ability to stimulate and record behavior over the course of hours. The setup is capable of two-color stimulation of optogenetic proteins over the area of the behavioral arena in combination with volatile chemicals. To showcase the utility of the system, I demonstrate behavioral analysis of integration of contradictory cues. In summary, I present a set of techniques for the interrogation of neural networks from animal behavior to neuronal activity, over timescales of potentially hours and days. These techniques can be used to address a new dimension of scientific questions.Okinawa Institute of Science and Technology Graduate Universit
Mathematical models for chemotaxis and their applications in self-organisation phenomena
Chemotaxis is a fundamental guidance mechanism of cells and organisms,
responsible for attracting microbes to food, embryonic cells into developing
tissues, immune cells to infection sites, animals towards potential mates, and
mathematicians into biology. The Patlak-Keller-Segel (PKS) system forms part of
the bedrock of mathematical biology, a go-to-choice for modellers and analysts
alike. For the former it is simple yet recapitulates numerous phenomena; the
latter are attracted to these rich dynamics. Here I review the adoption of PKS
systems when explaining self-organisation processes. I consider their
foundation, returning to the initial efforts of Patlak and Keller and Segel,
and briefly describe their patterning properties. Applications of PKS systems
are considered in their diverse areas, including microbiology, development,
immunology, cancer, ecology and crime. In each case a historical perspective is
provided on the evidence for chemotactic behaviour, followed by a review of
modelling efforts; a compendium of the models is included as an Appendix.
Finally, a half-serious/half-tongue-in-cheek model is developed to explain how
cliques form in academia. Assumptions in which scholars alter their research
line according to available problems leads to clustering of academics and the
formation of "hot" research topics.Comment: 35 pages, 8 figures, Submitted to Journal of Theoretical Biolog
Sensory computation and decision making in C. elegans: a computational approach
In Caenorhabditis elegans (C. elegans) and in neuroscience generally, a hierarchical view of nervous systems prevails. Roughly speaking, sensory neurons encode the external environment, interneurons encode internal state and decisions, and motor neurons encode muscle activation. Here, using an integrated approach to model sensory computation and decision making in C. elegans, I show a striking phenomenon. Via the simplest modulation possible, sensitization and desensitization, sensory neurons in C. elegans can also encode the animal’s internal state.
In this thesis, I present a modeling framework, and use it to implement two detailed models of sensory adaptation and decision making. In the first model I consider a decision making task, in which worms need to cross a lethal barrier in order to reach an attractant on the other side. My model captures the experimental results, and predicts a minimal set of requirements. This model‘s mechanism is reminiscent of similar top-down attention modulation motifs in mammalian cortex.
In the second model, I consider a form of plasticity in which animals alternate their perception of a signal from attractive to repulsive. I show how the model encodes high and low-level behavioral states, balancing attraction and aversion, exploration and exploitation, pushing the ‘decision making’ into the sensory layer. Furthermore, this model predicts that specific sensory neurons may have the capacity to selectively control distinct motor programs.
To accomplish these results, the modeling framework was designed to simulate a full sensory motor pathway and an in silico simulation arena, allowing it to reproduce experimental findings from multiple assays. Hopefully, this allows the model to be used by the C. elegans community and to be extended, bringing us closer to the larger aim of understanding distributed computation and the integrated neural control of behavior in a whole animal
Bio-inspired robotic locomotion model: Response towards food gradient changes and temperature variation
The nervous system is a complex yet efficient structure - with superior information processing capabilities that surely surpass any man-made high-performance computer. Understanding this technology and utilising it in robotic
navigation applications is essential to understand its underlying mechanism. One of the approaches is using a nematode’s biological network model, as having a
simple network structure while holding a complex locomotion behaviour. For instance, its ability to navigate via local concentration cue (chemotaxis) and the ability to dynamically respond towards surrounding temperature (thermotaxis). To date, the simulation of currently available models is on static environment conditions and the nematode’s movement decision is based on the deterministic
non-linear response towards gradient changes. Commonly, parameters of these models were optimised based on static conditions and require adjustment if simulated within a dynamic environment. Therefore, this work proposed a new
nematode’s biological locomotion model where the movement trajectory is determined by the probability of “Run” and “Turn” signals. The model is simulated within a 2D virtual environment with complex concentration gradient and variants of temperature distribution. The analysis result shows the nematode’s movement of the proposed model agreed with the finding from experimental studies. Later, the proposed model in this work will be employed to develop a biological inspired multi-sensory robotic system for navigating within a dynamic and complex environmen
A Mechanism for Spatial Orientation Based on Sensory Adaptation in Caenorhabditis Elegans
During chemotaxis, animals compute spatial information about odor gradients to make navigational choices for finding or avoiding an odor source. The challenge to the neural circuitry is to interpret and respond to odor concentrations that change over time as animals traverse a gradient. In this thesis, I ask how a nervous system regulates spatial navigation by studying the chemotaxis response of Caenorhabditis elegans to diacetyl. A behavioral analysis demonstrated that AWA sensory neurons drive chemotaxis over several orders of magnitude in odor concentration, providing an entry point for dissecting the mechanistic basis of chemotaxis at the level of neural activity. Precise microfluidic stimulation enabled me to dissociate space from time in the olfactory input to characterize how odor sensing relates to behavior. I systematically measured neuronal responses to odor in the diacetyl chemotaxis circuit, aided by a newly developed imaging system with flexible stimulus delivery and elevated throughput. I found reliable sensory responses to the behaviorally relevant range of odor concentrations. I then followed odor-evoked activity to downstream interneurons that integrate sensory input. Adaptation of neuronal responses to odor yielded a highly sensitive response to small increases in odor concentration at the interneuron level, providing a mechanism for efficient gradient sensing during klinokinesis. Adaptation dynamics at the sensory level were stimulus-dependent and cell-autonomously altered in several classes of mutant animals. Behavioral responses to different concentrations of diacetyl resulted from overlapping contributions from multiple sensory neurons. AWA was specifically required for orientation behavior in response to small increases in odor concentration that are encountered in shallow gradients, demonstrating functional specialization amongst sensory neurons for stimulus characteristics. This work sheds light on an algorithm underlying acute behavioral computation and its biological implementation. The experimental results are presented in two parts: Chapter 2 describes the development of a microscope for high-throughput imaging of neuronal activity in Caenorhabditis elegans. I present a characterization of chemosensory responses to odor and its correlation with behavior. This work has been published (Larsch et al., 2013). Chapter 3 describes the functional architecture of the AWA chemosensory circuit and the role of adaptation in maintaining sensitivity over a wide range of stimulus intensities. This work is currently being prepared for publication
Recommended from our members
Temporal Processing by Caenorhabditis elegans Sensory Neurons
Caenorhabditis elegans is a promising organism for trying to understand how nervous systems generate real-time behavior. Its low neuron count suggests that we may be able to observe all of the constituents of the computation of sophisticated sensorimotor behavior. However, its appropriateness as a system for quantitative dynamical study has yet to be established. We show that C. elegans chemosensory neurons can operate in a highly deterministic and low-noise mode, and they act as reliable linear filters of their input. We then use dynamical systems analysis in combination with classical genetic perturbation to uncover cellular and circuit mechanisms of temporal processing. This work should firmly establish C. elegans as a viable platform for applying quantitative dynamical systems methods to understanding how a nervous system processes sensory information, integrates it with an evolving internal state, and produces goal-directed, coordinated behavior
- …