Mapping the function of neuronal ion channels in model and experiment

Podlaski, William F; Seeholzer, Alexander; Groschner, Lukas N.; Miesenböck, Gero; Ranjan, Rajnish; Vogels, Tim P.

doi:10.7554/elife.22152

Cited by 35 publications

(41 citation statements)

References 71 publications

(104 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, some models tended to generate small transient oscillations in response to ramp stimuli in burst mode. This result is not surprising, considering that the exact kinetics for all the ionic currents are not available and that there are known limitations in models of ionic channels derived from the literature or from other models (36,37). In particular, modifications of the kinetics of the low-threshold calcium current was shown to explain the propensity to generate oscillatory bursts in TC neurons of other nuclei and species (38).…”

Section: Discussionmentioning

confidence: 97%

Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

Iavarone

Shi

et al. 2019

Preprint

View full text Add to dashboard Cite

Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies.

show abstract

Section: Discussionmentioning

confidence: 97%

Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

Iavarone

Shi

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, a major issue in computational science generally, and computational neuroscience in particular, is the crisis of reproducibility of computational models (LeVeque et al, 2012;Eglen et al, 2017;Podlaski et al, 2017;Manninen et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Brian 2: an intuitive and efficient neural simulator

Stimberg

Brette

Goodman

2019

Preprint

113

View full text Add to dashboard Cite

To be maximally useful for neuroscience research, neural simulators must make it possible to define original models. This is especially important because a computational experiment might not only need descriptions of neurons and synapses, but also models of interactions with the environment (e.g. muscles), or the environment itself. To preserve high performance when defining new models, current simulators offer two options: low-level programming, or mark-up languages (and other domain specific languages). The first option requires time and expertise, is prone to errors, and contributes to problems with reproducibility and replicability. The second option has limited scope, since it can only describe the range of neural models covered by the ontology. Other aspects of a computational experiment, such as the stimulation protocol, cannot be expressed within this framework. "Brian" 2 is a complete rewrite of Brian that addresses this issue by using runtime code generation with a procedural equation-oriented approach. Brian 2 enables scientists to write code that is particularly simple and concise, closely matching the way they conceptualise their models, while the technique of runtime code generation automatically transforms high level descriptions of models into efficient low level code tailored to different hardware (e.g. CPU or GPU). We illustrate it with several challenging examples: a plastic model of the pyloric network of crustaceans, a closed-loop sensorimotor model, programmatic exploration of a neuron model, and an auditory model with real-time input from a microphone. from brian2 import * 2 defaultclock.dt = 0.01*ms; 3 Delta_T = 17.5*mV ; v_T = -40*mV ; tau = 2*ms ; tau_adapt = .02*second 4 tau_Ca = 150*ms ; tau_x = 2*second ; v_r = -68*mV ; tau_z = 5*second 5

show abstract

“…Future approaches should recognize that behavior emerges from disparate combinations of tightly cross‐coupled multi‐scale emergent properties, each diverging and converging at each scale of analysis through degeneracy spanning complex parametric and interactional spaces. Large‐scale databases related to neuronal morphology, models and physiology—such as the Allen brain atlas (Sunkin et al, ), ICGenealogy (Podlaski et al, ), Channelpedia (Ranjan et al, ), Neuromorpho (Ascoli, Donohue, & Halavi, ), ModelDB (Hines, Morse, Migliore, Carnevale, & Shepherd, ) and Neuroelectro (Tripathy, Savitskaya, Burton, Urban, & Gerkin, )—provide ideal tools for such analyses involving large parametric spaces, and could provide critical insights about the role of degeneracy in the emergence of robust brain physiology and its links to behavior.…”

Section: The Causality Conundrummentioning

confidence: 99%

“…(a-d) Artificial data on "percentage change in measurement" belonging to two groups (Group 1 and Group 2) reported as mean with error bars representing SEM (a), as median with all four quartiles (b), as a beeswarm plot (c; also showing mean and SEM) and as a cumulative histogram of percentage changes (d) [Color figure can be viewed at wileyonlinelibrary.com] that behavior emerges from disparate combinations of tightly crosscoupled multi-scale emergent properties, each diverging and converging at each scale of analysis through degeneracy spanning complex parametric and interactional spaces. Large-scale databases related to neuronal morphology, models and physiology-such as the Allen brain atlas (Sunkin et al, 2013), ICGenealogy (Podlaski et al, 2017), Channelpedia (Ranjan et al, 2011), Neuromorpho (Ascoli, Donohue, & Halavi, 2007), ModelDB (Hines, Morse, Migliore, Carnevale, & Shepherd, 2004) and…”

Section: Degeneracy: the Way Forwardmentioning

confidence: 99%

Degeneracy in hippocampal physiology and plasticity

2019

View full text Add to dashboard Cite

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross‐interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding‐homeostasis system in accomplishing its tasks in an input‐ and state‐dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

show abstract

Mapping the function of neuronal ion channels in model and experiment

Cited by 35 publications

References 71 publications

Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

Brian 2: an intuitive and efficient neural simulator

Degeneracy in hippocampal physiology and plasticity

Contact Info

Product

Resources

About