8,224 research outputs found
Information flow through a model of the C. elegans klinotaxis circuit
Understanding how information about external stimuli is transformed into
behavior is one of the central goals of neuroscience. Here we characterize the
information flow through a complete sensorimotor circuit: from stimulus, to
sensory neurons, to interneurons, to motor neurons, to muscles, to motion.
Specifically, we apply a recently developed framework for quantifying
information flow to a previously published ensemble of models of salt
klinotaxis in the nematode worm C. elegans. The models are grounded in the
neuroanatomy and currently known neurophysiology of the worm. The unknown model
parameters were optimized to reproduce the worm's behavior. Information flow
analysis reveals several key principles underlying how the models operate: (1)
Interneuron class AIY is responsible for integrating information about positive
and negative changes in concentration, and exhibits a strong left/right
information asymmetry. (2) Gap junctions play a crucial role in the transfer of
information responsible for the information symmetry observed in interneuron
class AIZ. (3) Neck motor neuron class SMB implements an information gating
mechanism that underlies the circuit's state-dependent response. (4) The neck
carries non-uniform distribution about changes in concentration. Thus, not all
directions of movement are equally informative. Each of these findings
corresponds to an experimental prediction that could be tested in the worm to
greatly refine our understanding of the neural circuit underlying klinotaxis.
Information flow analysis also allows us to explore how information flow
relates to underlying electrophysiology. Despite large variations in the neural
parameters of individual circuits, the overall information flow architecture
circuit is remarkably consistent across the ensemble, suggesting that
information flow analysis captures general principles of operation for the
klinotaxis circuit
Systems level circuit model of C. elegans undulatory locomotion: mathematical modeling and molecular genetics
To establish the relationship between locomotory behavior and dynamics of
neural circuits in the nematode C. elegans we combined molecular and
theoretical approaches. In particular, we quantitatively analyzed the motion of
C. elegans with defective synaptic GABA and acetylcholine transmission,
defective muscle calcium signaling, and defective muscles and cuticle
structures, and compared the data with our systems level circuit model. The
major experimental findings are: (i) anterior-to-posterior gradients of body
bending flex for almost all strains both for forward and backward motion, and
for neuronal mutants, also analogous weak gradients of undulatory frequency,
(ii) existence of some form of neuromuscular (stretch receptor) feedback, (iii)
invariance of neuromuscular wavelength, (iv) biphasic dependence of frequency
on synaptic signaling, and (v) decrease of frequency with increase of the
muscle time constant. Based on (i) we hypothesize that the Central Pattern
Generator (CPG) is located in the head both for forward and backward motion.
Points (i) and (ii) are the starting assumptions for our theoretical model,
whose dynamical patterns are qualitatively insensitive to the details of the
CPG design if stretch receptor feedback is sufficiently strong and slow. The
model reveals that stretch receptor coupling in the body wall is critical for
generation of the neuromuscular wave. Our model agrees with our behavioral
data(iii), (iv), and (v), and with other pertinent published data, e.g., that
frequency is an increasing function of muscle gap-junction coupling.Comment: Neural control of C. elegans motion with genetic perturbation
Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait
Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system
A Neural Pattern Generator that Exhibits Frequency-Dependent In-Phase and Anti-Phase Oscillations
This article describes a. neural pattern generator based on a cooperative-competitive feedback neural network. The two-channel version of the generator supports both in-phase and anti-phase oscillations. A scalar arousal level controls both the oscillation phase and frequency. As arousal increases, oscillation frequency increases and bifurcations from in-phase to anti-phase, or anti-phase to in-phase oscillations can occur. Coupled versions of the model exhibit oscillatory patterns which correspond to the gaits used in locomotion and other oscillatory movements by various animals.Air Force Office of Scientific Research (90-0128, 90-0175); National Science Foundation (IRI-90-24877); Army Research Office (DAAL03-88-k-0088
Evolution of central pattern generators for the control of a five-link bipedal walking mechanism
Central pattern generators (CPGs), with a basis is neurophysiological
studies, are a type of neural network for the generation of rhythmic motion.
While CPGs are being increasingly used in robot control, most applications are
hand-tuned for a specific task and it is acknowledged in the field that generic
methods and design principles for creating individual networks for a given task
are lacking. This study presents an approach where the connectivity and
oscillatory parameters of a CPG network are determined by an evolutionary
algorithm with fitness evaluations in a realistic simulation with accurate
physics. We apply this technique to a five-link planar walking mechanism to
demonstrate its feasibility and performance. In addition, to see whether
results from simulation can be acceptably transferred to real robot hardware,
the best evolved CPG network is also tested on a real mechanism. Our results
also confirm that the biologically inspired CPG model is well suited for legged
locomotion, since a diverse manifestation of networks have been observed to
succeed in fitness simulations during evolution.Comment: 11 pages, 9 figures; substantial revision of content, organization,
and quantitative result
Continuous breakdown of Purcell's scallop theorem with inertia
Purcell's scallop theorem defines the type of motions of a solid body -
reciprocal motions - which cannot propel the body in a viscous fluid with zero
Reynolds number. For example, the flapping of a wing is reciprocal and, as was
recently shown, can lead to directed motion only if its frequency Reynolds
number, Re_f, is above a critical value of order one. Using elementary
examples, we show the existence of oscillatory reciprocal motions which are
effective for all arbitrarily small values of the frequency Reynolds number and
induce net velocities scaling as (Re_f)^\alpha (alpha > 0). This demonstrates a
continuous breakdown of the scallop theorem with inertia.Comment: 6 pages, 1 figur
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