Visualization of currents in neural models with similar behavior and different conductance densities

Alonso, Leandro M.; Marder, Eve

doi:10.7554/elife.42722

Cited by 101 publications

(149 citation statements)

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“…The color lines match features of the voltage waveforms at each temperature, with features in the distributions on the right. The distributions permit visualizing changes in the waveform as the control parameter is changed (Alonso and Marder, 2019). Figure 2B shows the membrane potential distributions of each cell for 6 models.…”

Section: B E↵ect Of Temperature On Membrane Potentialmentioning

confidence: 99%

“…Because the total number of currents in the circuits is 31 it becomes cumbersome to compare them across temperatures (and models). For this reason, we computed and inspected their currentscapes (Alonso and Marder, 2019). The currentscapes use colors to show the percent contribution of each current to the total inward (or outward) current and are useful to display the dynamics of FIG.…”

Section: F Dynamics Of the Currents At Di↵erent Temperaturesmentioning

confidence: 99%

“…Figure 11 shows how the currentscapes at a specific instant change with temperature. To explore how the full currentscapes change over temperature we computed the distributions of current shares (Alonso and Marder, 2019). We computed the currentscapes at each value of temperature over the working range and inspected the time series of the currents' shares, or equivalently, the widths of the colored regions in the currentscapes.…”

Section: F Dynamics Of the Currents At Di↵erent Temperaturesmentioning

confidence: 99%

“…Finding these temperature compensated networks is nontrivial: it requires the specification of 8 ⇥ 3 + 7 = 31 maximal conductances G and 24 Q 10 values. Because it is virtually impossible to find these sets of parameters at random we employed a landscape optimization scheme described previously (Alonso and Marder, 2019) and adapted it to this particular scenario (see Methods). An alternative approach was described in O' Leary and Marder (2016).…”

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confidence: 99%

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Temperature compensation in a small rhythmic circuit

Alonso

Marder

2019

Preprint

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Temperature a↵ects the conductances and kinetics of the ionic channels that underlie neuronal activity. Each membrane conductance has a di↵erent characteristic temperature sensitivity, which raises the question of how neurons and neuronal circuits can operate robustly over wide temperature ranges. To address this, we employed computational models of the pyloric network of crabs and lobsters. We produced multiple di↵erent models that exhibit a triphasic pyloric rhythm over a range of temperatures and explored the dynamics of their currents and how they change with temperature. We found that temperature changes the relative contributions of the currents to neuronal activity so that rhythmic activity smoothly slides through changes in mechanisms. Moreover, the responses of the models to extreme perturbations-such as gradually decreasing a current type-are often qualitatively di↵erent at di↵erent temperatures.Here we explore these issues using computational models of the pyloric network-a subnetwork within the stomatogastric ganglion (STG) of crustaceans (Marder and Bucher, 2007;Maynard, 1972). The pyloric rhythm is a triphasic motor pattern that consists of bursts of action potentials in a specific sequence. This behavior is robust and stable, and the cells and their connections are This manuscript has not been peer-reviewed L.M. Alonso and E. Marder

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Section: B E↵ect Of Temperature On Membrane Potentialmentioning

confidence: 99%

Section: F Dynamics Of the Currents At Di↵erent Temperaturesmentioning

confidence: 99%

Section: F Dynamics Of the Currents At Di↵erent Temperaturesmentioning

confidence: 99%

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confidence: 99%

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Temperature compensation in a small rhythmic circuit

Alonso

Marder

2019

Preprint

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“…It remains to be seen how far the approach described here may be used in system identification of excitable membranes more complicated than the minimal, two conductance single compartment Hodgkin-Huxley configuration. Certainly, cells that contain many different types of ion channels will show a range of time-scales and historydependence (Alonso and Marder, 2019). Developing intuition into how a given set of firing properties depends on conductance densities of many channels may require new kinds of principled dimensionality reduction to complement brute-force numerical simulations.…”

Section: Discussionmentioning

confidence: 99%

Dynamic clamp constructed phase diagram of the Hodgkin-Huxley action potential model

Hazan

Marder

et al. 2019

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words]Excitability -a threshold governed transient in transmembrane voltage -is a fundamental physiological process that controls the function of the heart, endocrine, muscles and neuronal tissues. The 1950's Hodgkin and Huxley explicit formulation provides a mathematical framework for understanding excitability, as the consequence of the properties of voltage-gated sodium and potassium channels. The Hodgkin-Huxley model is more sensitive to parametric variations of protein densities and kinetics than biological systems whose excitability is apparently more robust. It is generally assumed that the model's sensitivity reflects missing functional relations between its parameters or other components present in biological systems. Here we experimentally construct excitable membranes using the dynamic clamp and voltage-gated potassium ionic channels (Kv1.3) expressed in Xenopus oocytes. We take advantage of a theoretically derived phase diagram, where the phenomenon of excitability is reduced to two dimensions defined as combinations of the Hodgkin-Huxley model parameters. This biological-computational hybrid enabled us to explore functional relations in the parameter space, experimentally validate the phase diagram of the Hodgkin-Huxley model, and demonstrate activity-dependence and hysteretic dynamics due to the impacts of slow inactivation kinetics. The increased resolution of current biological measurements is making it clear that components of individual cells and animals show considerable variability, thus raising the theoretical challenge of understanding system robustness in the face of this variability. The experimental results presented here provide new insights into the gap between technology-guided high-dimensional descriptions, and a lower, physiological dimensionality, within which biological function is embedded. Significance Statement [113 words]A long-standing issue in physiology concerns the robustness of cellular functions to variations in concentrations and kinetics of biomolecules. The robustness is believed to reflect functional relations between many underlying biological components. Here we study the relationship between sodium and potassium channel parameters and membrane excitability. To this end we used the dynamic clamp, a computer-controlled system that essentially makes it possible to do simulations with biological cells and ionic channels. We 3 established a real-time closed-loop interaction between a genetically controlled population of excitability-relevant ion channels and a low-dimensional mathematical description of excitability. The results provide new insights into how robustness of excitability arises from the properties of ion channels and their interactions.

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Degeneracy in the nervous system: from neuronal excitability to neural coding

Kamaleddin

2021

BioEssays

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Degeneracy is ubiquitous across biological systems where structurally different elements can yield a similar outcome. Degeneracy is of particular interest in neuroscience too. On the one hand, degeneracy confers robustness to the nervous system and facilitates evolvability: Different elements provide a backup plan for the system in response to any perturbation or disturbance. On the other, a difficulty in the treatment of some neurological disorders such as chronic pain is explained in light of different elements all of which contribute to the pathological behavior of the system. Under these circumstances, targeting a specific element is ineffective because other elements can compensate for this modulation. Understanding degeneracy in the physiological context explains its beneficial role in the robustness of neural circuits. Likewise, understanding degeneracy in the pathological context opens new avenues of discovery to find more effective therapies.

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Visualization of currents in neural models with similar behavior and different conductance densities

Cited by 101 publications

References 47 publications

Temperature compensation in a small rhythmic circuit

Temperature compensation in a small rhythmic circuit

Dynamic clamp constructed phase diagram of the Hodgkin-Huxley action potential model

Degeneracy in the nervous system: from neuronal excitability to neural coding

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