683 research outputs found
A neural circuit for navigation inspired by C. elegans Chemotaxis
We develop an artificial neural circuit for contour tracking and navigation
inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to
harness the computational advantages spiking neural networks promise over their
non-spiking counterparts, we develop a network comprising 7-spiking neurons
with non-plastic synapses which we show is extremely robust in tracking a range
of concentrations. Our worm uses information regarding local temporal gradients
in sodium chloride concentration to decide the instantaneous path for foraging,
exploration and tracking. A key neuron pair in the C. elegans chemotaxis
network is the ASEL & ASER neuron pair, which capture the gradient of
concentration sensed by the worm in their graded membrane potentials. The
primary sensory neurons for our network are a pair of artificial spiking
neurons that function as gradient detectors whose design is adapted from a
computational model of the ASE neuron pair in C. elegans. Simulations show that
our worm is able to detect the set-point with approximately four times higher
probability than the optimal memoryless Levy foraging model. We also show that
our spiking neural network is much more efficient and noise-resilient while
navigating and tracking a contour, as compared to an equivalent non-spiking
network. We demonstrate that our model is extremely robust to noise and with
slight modifications can be used for other practical applications such as
obstacle avoidance. Our network model could also be extended for use in
three-dimensional contour tracking or obstacle avoidance
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
Neural Architecture of Hunger-Dependent Multisensory Decision Making in C. elegans
Little is known about how animals integrate multiple sensory inputs in natural environments to balance avoidance of danger with approach to things of value. Furthermore, the mechanistic link between internal physiological state and threat-reward decision making remains poorly understood. Here we confronted C. elegans worms with the decision whether to cross a hyperosmotic barrier presenting the threat of desiccation to reach a source of food odor. We identified a specific interneuron that controls this decision via top-down extrasynaptic aminergic potentiation of the primary osmosensory neurons to increase their sensitivity to the barrier. We also establish that food deprivation increases the worm's willingness to cross the dangerous barrier by suppressing this pathway. These studies reveal a potentially general neural circuit architecture for internal state control of threat-reward decision making
Modeling the chemotaxis behaviors of C. Elegans using neural network: from artificial to biological approach
Ph.DDOCTOR OF PHILOSOPH
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