Balance between inhibitory cell types is necessary for flexible frequency switching in adult mouse visual cortex

Domhof, Justin W. M.; Phe, Tiesinga

doi:10.1101/2020.01.18.911271

Cited by 1 publication

(2 citation statements)

References 65 publications

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“…• a small network of Hodgkin-Huxley neurons. [19] and to understand oscillatory phenomena in the cortex [20,21] and the olfactory bulb [22]. Its behavior is described by the following pair of ODEs [20]:…”

Section: Methods or Models Common Ode Models In Computational Neurosciencementioning

confidence: 99%

“…The Izhikevich neuron (IN) model is a simplified non-linear single neuron model that has been used e.g. to build large-scale models of the brain [31] and to understand oscillatory phenomena in the cortex [32,33] and the olfactory bulb [34]. An attractive property of the IN is that a whole range of different response dynamics can be simulated (Fig.…”

Section: Single Izhikevich Neuronsmentioning

confidence: 99%

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Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models

Oesterle

Krämer

Hennig

et al. 2021

Preprint

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Understanding neural computation on the mechanistic level requires biophysically realistic neuron models. To analyze such models one typically has to solve systems of coupled ordinary differential equations (ODEs), which describe the dynamics of the underlying neural system. These ODEs are solved numerically with deterministic ODE solvers that yield single solutions with either no or only aglobal scalar bound on precision. To overcome this problem, we propose to use recently developed probabilistic solvers instead, which are able to reveal and quantify numerical uncertainties, for example as posterior sample paths. Importantly, these solvers neither require detailed insights into the kinetics of themodels nor are they difficult to implement. Using these probabilistic solvers, we show that numerical uncertainty strongly affects the outcome of typical neuroscience simulations, in particular due to the non-linearity associated with the generation of action potentials. We quantify this uncertainty in individual single Izhikevich neurons with different dynamics, a large population of coupled Izhikevich neurons, single Hodgkin-Huxley neuron and a small network of Hodgkin-Huxley-like neurons. For commonly used ODE solvers, we find that the numerical uncertainty in these models can be substantial, possibly jittering spikes by milliseconds or even adding or removing individual spikes from the simulation altogether.

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Section: Methods or Models Common Ode Models In Computational Neurosciencementioning

confidence: 99%

Section: Single Izhikevich Neuronsmentioning

confidence: 99%

Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models

Oesterle

Krämer

Hennig

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Balance between inhibitory cell types is necessary for flexible frequency switching in adult mouse visual cortex

Cited by 1 publication

References 65 publications

Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models

Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models

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