Publications
9 results found
Elices I, Kulkarni A, Escoubet N, et al., 2023, An electrophysiological and kinematic model of Paramecium, the "swimming neuron"., PLoS Comput Biol, Vol: 19
Paramecium is a large unicellular organism that swims in fresh water using cilia. When stimulated by various means (mechanically, chemically, optically, thermally), it often swims backward then turns and swims forward again in a new direction: this is called the avoiding reaction. This reaction is triggered by a calcium-based action potential. For this reason, several authors have called Paramecium the "swimming neuron". Here we present an empirically constrained model of its action potential based on electrophysiology experiments on live immobilized paramecia, together with simultaneous measurement of ciliary beating using particle image velocimetry. Using these measurements and additional behavioral measurements of free swimming, we extend the electrophysiological model by coupling calcium concentration to kinematic parameters, turning it into a swimming model. In this way, we obtain a model of autonomously behaving Paramecium. Finally, we demonstrate how the modeled organism interacts with an environment, can follow gradients and display collective behavior. This work provides a modeling basis for investigating the physiological basis of autonomous behavior of Paramecium in ecological environments.
Elices I, Kulkarni A, Escoubet N, et al., 2022, An electrophysiological and kinematic model of<i>Paramecium</i>, the “swimming neuron”
<jats:title>Abstract</jats:title><jats:p><jats:italic>Paramecium</jats:italic>is a large unicellular organism that swims in fresh water using cilia. When stimulated by various means (mechanically, chemically, optically, thermally), it often swims backward then turns and swims forward again in a new direction: this is called the avoiding reaction. This reaction is triggered by a calcium-based action potential. For this reason, several authors have called<jats:italic>Paramecium</jats:italic>the “swimming neuron”. Here we present an empirically constrained model of its action potential based on electrophysiology experiments on live immobilized paramecia, together with simultaneous measurement of ciliary beating using particle image velocimetry. Using these measurements and additional behavioral measurements of free swimming, we extend the electrophysiological model by coupling calcium concentration to kinematic parameters, turning it into a swimming model. In this way, we obtain a model of autonomously behaving<jats:italic>Paramecium</jats:italic>. Finally, we demonstrate how the modeled organism interacts with an environment, can follow gradients and display collective behavior. This work provides a modeling basis for investigating the physiological basis of autonomous behavior of<jats:italic>Paramecium</jats:italic>in ecological environments.</jats:p><jats:sec><jats:title>Author Summary</jats:title><jats:p>Behavior depends on a complex interaction between a variety of physiological processes, the body and the environment. We propose to examine this complex interaction in an organism consisting of a single excitable and motile cell,<jats:italic>Paramecium</jats:italic>. The behavior of<jats:italic>Paramecium</jats:italic>is based on trial and error: when it encounters an undesirable situation, it backs up and changes direction. This avoid
Kulkarni A, Kegler M, Reichenbach T, 2021, Effect of visual input on syllable parsing in a computational model of a neural microcircuit for speech processing., Journal of Neural Engineering, Vol: 5, Pages: 1-14, ISSN: 1741-2552
Seeing a person talking can help to understand them, in particular in a noisy environment. However, how the brain integrates the visual information with the auditory signal to enhance speech comprehension remains poorly understood. Here we address this question in a computational model of a cortical microcircuit for speech processing. The model consists of an excitatory and an inhibitory neural population that together create oscillations in the theta frequency range. When simulated with speech, the theta rhythm becomes entrained to the onsets of syllables, such that the onsets can be inferred from the network activity. We investigate how well the obtained syllable parsing performs when different types of visual stimuli are added. In particular, we consider currents related to the rate of syllables as well as currents related to the mouth-opening area of the talking faces. We find that currents that target the excitatory neuronal population can influence speech comprehension, both boosting it or impeding it, depending on the temporal delay and on whether the currents are excitatory or inhibitory. In contrast, currents that act on the inhibitory neurons do not impact speech comprehension significantly. Our results suggest neural mechanisms for the integration of visual information with the acoustic information in speech and make experimentally-testable predictions.
Kulkarni A, Ranft J, Hakim V, 2020, Synchronization, stochasticity, and phase waves in neuronal networks with spatially-structured connectivity, Frontiers in Computational Neuroscience, Vol: 14, Pages: 1-33, ISSN: 1662-5188
Oscillations in the beta/low gamma range (10–45 Hz) are recorded in diverse neuralstructures. They have successfully been modeled as sparsely synchronized oscillationsarising from reciprocal interactions between randomly connected excitatory (E) pyramidalcells and local interneurons (I). The synchronization of spatially distant oscillatory spikingE–I modules has been well-studied in the rate model framework but less so for modulesof spiking neurons. Here, we first show that previously proposed modifications ofrate models provide a quantitative description of spiking E–I modules of ExponentialIntegrate-and-Fire (EIF) neurons. This allows us to analyze the dynamical regimesof sparsely synchronized oscillatory E–I modules connected by long-range excitatoryinteractions, for two modules, as well as for a chain of such modules. For modules witha large number of neurons (> 105), we obtain results similar to previously obtained onesbased on the classic deterministic Wilson-Cowan rate model, with the added bonusthat the results quantitatively describe simulations of spiking EIF neurons. However, formodules with a moderate (∼ 104) number of neurons, stochastic variations in the spikeemission of neurons are important and need to be taken into account. On the one hand,they modify the oscillations in a way that tends to promote synchronization betweendifferent modules. On the other hand, independent fluctuations on different modules tendto disrupt synchronization. The correlations between distant oscillatory modules can bedescribed by stochastic equations for the oscillator phases that have been intenselystudied in other contexts. On shorter distances, we develop a description that also takesinto account amplitude modes and that quantitatively accounts for our simulation data.Stochastic dephasing of neighboring modules produces transient phase gradients andthe transient appearance of phase waves. We propose that these stochastically-inducedphase wa
Kulkarni A, Ranft J, Hakim V, 2020, Synchronization, stochasticity and phase waves in neuronal networks with spatially-structured connectivity
<jats:title>Abstract</jats:title><jats:p>Oscillations in the beta/low gamma range (10-45 Hz) are recorded in diverse neural structures. They have successfully been modeled as sparsely synchronized oscillations arising from reciprocal interactions between randomly connected excitatory (E) pyramidal cells and local interneurons (I). The synchronization of spatially distant oscillatory spiking E-I modules has been well studied in the rate model framework but less so for modules of spiking neurons. Here, we first show that previously proposed modifications of rate models provide a quantitative description of spiking E-I modules of Exponential Integrate-and-Fire (EIF) neurons. This allows us to analyze the dynamical regimes of sparsely synchronized oscillatory E-I modules connected by long-range excitatory interactions, for two modules, as well as for a chain of such modules. For modules with a large number of neurons (> 10<jats:sup>5</jats:sup>), we obtain results similar to previously obtained ones based on the classic deterministic Wilson-Cowan rate model, with the added bonus that the results quantitatively describe simulations of spiking EIF neurons. However, for modules with a moderate (~ 10<jats:sup>4</jats:sup>) number of neurons, stochastic variations in the spike emission of neurons are important and need to be taken into account. On the one hand, they modify the oscillations in a way that tends to promote synchronization between different modules. On the other hand, independent fluctuations on different modules tend to disrupt synchronization. The correlations between distant oscillatory modules can be described by stochastic equations for the oscillator phases that have been intensely studied in other contexts. On shorter distances, we develop a description that also takes into account amplitude modes and that quantitatively accounts for our simulation data. Stochastic dephasing of neighboring modules produces
Kulkarni A, Elices I, Escoubet N, et al., 2020, A simple device to immobilize protists for electrophysiology and microinjection, The Journal of Experimental Biology, Vol: 223, Pages: 1-5, ISSN: 0022-0949
We present a simple device to mechanically immobilize motile cells such as ciliates. It can be used in particular for intracellular electrophysiology and microinjection. A transparent filter with holes smaller than the specimen is stretched over an outlet. A flow is induced by either a peristaltic pump or a depressurized tank, mechanically entraining cells to the bottom, where they are immobilized against the filter. The cells start swimming again as soon as the flow is stopped. We demonstrate the device by recording action potentials in Paramecium and injecting a fluorescent dye into the cytosol.
Kulkarni A, Escoubet N, Pontani L-L, et al., 2019, A simple device to immobilize protists for electrophysiology and microinjection
<jats:title>ABSTRACT</jats:title><jats:p>We present a simple device to mechanically immobilize motile cells such as ciliates and flagellates. It can be used in particular for intracellular electrophysiology and microinjection. A transparent filter with holes smaller than the specimen is stretched over an outlet. A flow is induced by either a peristaltic pump or a depressurized tank, mechanically entraining cells to the bottom, where they immobilize against the filter. The cells swim again freely as soon as the flow is stopped. We demonstrate the device by recording action potentials in Paramecium and injecting a fluorescent dye in the cytosol.</jats:p>
Perez-Schuster V, Kulkarni A, Nouvian M, et al., 2016, Sustained Rhythmic Brain Activity Underlies Visual Motion Perception in Zebrafish (vol 17, pg 1098, 2016), CELL REPORTS, Vol: 17, Pages: 3089-3089, ISSN: 2211-1247
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- Citations: 3
Perez-Schuster V, Kulkarni A, Nouvian M, et al., 2016, Sustained Rhythmic Brain Activity Underlies Visual Motion Perception in Zebrafish, CELL REPORTS, Vol: 17, Pages: 1098-1112, ISSN: 2211-1247
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- Citations: 14
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