Closing the loop: optimal stimulation of C. elegans neuronal network via adaptive control to exhibit full body movements

We isolated and characterized the cDNAs for the human, pig, and Caenorhabditis elegansK-Cl cotransporters. The pig and human homologs are 94% identical and contain 1,085 and 1,086 amino acids, respectively. The deduced protein of the C. elegans K-Cl cotransporter clone (CE-KCC1) contains 1,003 amino acids. The mammalian K-Cl cotransporters share ∼45% similarity with CE-KCC1. Hydropathy analyses of the three clones indicate typical KCC topology patterns with 12 transmembrane segments, large extracellular loops between transmembrane domains 5 and 6 (unique to KCC), and large COOH-terminal domains. Human KCC1 is widely expressed among various tissues. This KCC1 gene spans 23 kb and is organized in 24 exons, whereas the CE-KCC1 gene spans 3.5 kb and contains 10 exons. Transiently and stably transfected human embryonic kidney cells (HEK-293) expressing the human, pig, and C. elegans K-Cl cotransporter fulfilled two (pig) or five (human and C. elegans) criteria for increased expression of the K-Cl cotransporter. The criteria employed were basal K-Cl cotransport; stimulation of cotransport by swelling, N-ethylmaleimide, staurosporine, and reduced cell Mg concentration; and secondary stimulation of Na-K-Cl cotransport.

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Qualitative Evaluation of Full Body Movements with Gesture Description Language

2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation ◽

10.1109/aims.2014.32 ◽

2014 ◽

Cited By ~ 2

Author(s):

Tomasz Hachaj ◽

Marek R. Ogiela

Keyword(s):

Qualitative Evaluation ◽

Body Movements ◽

Description Language ◽

Full Body

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Soft-wired long-term memory in a natural recurrent neuronal network

10.1101/2020.04.01.020180 ◽

2020 ◽

Author(s):

Miguel A. Casal ◽

Santiago Galella ◽

Oscar Vilarroya ◽

Jordi Garcia-Ojalvo

Keyword(s):

Neuronal Network ◽

Neuronal Networks ◽

Initial Conditions ◽

Permutation Entropy ◽

Long Term Memory ◽

Term Memory ◽

C Elegans ◽

Living Organisms ◽

Recurrent Connections

Neuronal networks provide living organisms with the ability to process information. They are also characterized by abundant recurrent connections, which give rise to strong feed-back that dictates their dynamics and endows them with fading (short-term) memory. The role of recurrence in long-term memory, on the other hand, is still unclear. Here we use the neuronal network of the roundworm C. elegans to show that recurrent architectures in living organisms can exhibit long-term memory without relying on specific hard-wired modules. A genetic algorithm reveals that the experimentally observed dynamics of the worm’s neuronal network exhibits maximal complexity (as measured by permutation entropy). In that complex regime, the response of the system to repeated presentations of a time-varying stimulus reveals a consistent behavior that can be interpreted as soft-wired long-term memory.A common manifestation of our ability to remember the past is the consistence of our responses to repeated presentations of stimuli across time. Complex chaotic dynamics is known to produce such reliable responses in spite of its characteristic sensitive dependence on initial conditions. In neuronal networks, complex behavior is known to result from a combination of (i) recurrent connections and (ii) a balance between excitation and inhibition. Here we show that those features concur in the neuronal network of a living organism, namely C. elegans. This enables long-term memory to arise in an on-line manner, without having to be hard-wired in the brain.

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Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2017.0377 ◽

2018 ◽

Vol 373 (1758) ◽

pp. 20170377 ◽

Cited By ~ 6

Author(s):

Hexuan Liu ◽

Jimin Kim ◽

Eli Shlizerman

Keyword(s):

Time Series ◽

Nervous System ◽

Caenorhabditis Elegans ◽

Graphical Models ◽

Neuronal Network ◽

Graphical Model ◽

Time Series Data ◽

Series Data ◽

Neuronal Responses ◽

C Elegans

We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the Caenorhabditis elegans somatic nervous system to validate and show an example of our approach. The structure and dynamics of the C. elegans nervous system are well studied and a model that generates neuronal responses is available. The resulting PGM enables us to obtain and verify underlying neuronal pathways for known behavioural scenarios and detect possible pathways for novel scenarios. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

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Consolidation Effects in a Full-Body Diving Task

Journal of Motor Learning and Development ◽

10.1123/jmld.2016-0048 ◽

2017 ◽

Vol 5 (2) ◽

pp. 291-303

Author(s):

Maxime Trempe ◽

Jean-Luc Gohier ◽

Mathieu Charbonneau ◽

Jonathan Tremblay

Keyword(s):

Skill Acquisition ◽

Training Session ◽

Video Camera ◽

The Body ◽

Initial Training ◽

Body Movements ◽

Training Tool ◽

Significant Performance ◽

The Moment ◽

Full Body

In recent years, it has been shown that spacing training sessions by several hours allows the consolidation of motor skills in the brain, a process leading to the stabilization of the skills and, sometimes, further improvement without additional practice. At the moment, it is unknown whether consolidation can lead to an improvement in performance when the learner performs complex full-body movements. To explore this question, we recruited 10 divers and had them practice a challenging diving maneuver. Divers first performed an initial training session, consisting of 12 dives during which visual feedback was provided immediately after each dive through video replay. Two retention tests without feedback were performed 30 min and 24 hr after the initial training session. All dives were recorded using a video camera and the participants’ performance was assessed by measuring the verticality of the body segments at water entry. Significant performance gains were observed in the 24-hr retention test (p < .05). These results suggest that the learning of complex full-body movements can benefit from consolidation and that splitting practice sessions can be used as a training tool to facilitate skill acquisition.

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Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks

2020 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra40945.2020.9197290 ◽

2020 ◽

Cited By ~ 2

Author(s):

Philipp Kratzer ◽

Marc Toussaint ◽

Jim Mainprice

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Body Movements ◽

Motion Optimization ◽

Full Body

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Mitochondrial Ca2+ Dynamics in MCU Knockout C. elegans Worms

International Journal of Molecular Sciences ◽

10.3390/ijms21228622 ◽

2020 ◽

Vol 21 (22) ◽

pp. 8622

Author(s):

Pilar Álvarez-Illera ◽

Paloma García-Casas ◽

Rosalba I Fonteriz ◽

Mayte Montero ◽

Javier Alvarez

Keyword(s):

Cell Activation ◽

Physiological Activity ◽

Large Cell ◽

Atp Production ◽

Physiological Conditions ◽

C Elegans ◽

Neuronal Stimulation ◽

Low Amplitude ◽

Stimulation Of

Mitochondrial [Ca2+] plays an important role in the regulation of mitochondrial function, controlling ATP production and apoptosis triggered by mitochondrial Ca2+ overload. This regulation depends on Ca2+ entry into the mitochondria during cell activation processes, which is thought to occur through the mitochondrial Ca2+ uniporter (MCU). Here, we have studied the mitochondrial Ca2+ dynamics in control and MCU-defective C. elegans worms in vivo, by using worms expressing mitochondrially-targeted YC3.60 yellow cameleon in pharynx muscle. Our data show that the small mitochondrial Ca2+ oscillations that occur during normal physiological activity of the pharynx were very similar in both control and MCU-defective worms, except for some kinetic differences that could mostly be explained by changes in neuronal stimulation of the pharynx. However, direct pharynx muscle stimulation with carbachol triggered a large and prolonged increase in mitochondrial [Ca2+] that was much larger in control worms than in MCU-defective worms. This suggests that MCU is necessary for the fast mitochondrial Ca2+ uptake induced by large cell stimulations. However, low-amplitude mitochondrial Ca2+ oscillations occurring under more physiological conditions are independent of the MCU and use a different Ca2+ pathway.

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