Source 1primary paper2013
Toolkit/generalised computational modeling technique for optogenetic mechanisms
generalised computational modeling technique for optogenetic mechanisms
Also known as: computational modeling technique
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
This tool is a generalised computational modeling technique for simulating optogenetic mechanisms in the NEURON environment. It was presented using channelrhodopsin-2 and halorhodopsin to model optical activation and optical silencing in neurons.
Usefulness & Problems
Why this is useful
The method is useful for in silico analysis of how different optogenetic actuators affect neuronal activity under light stimulation. The cited demonstration shows that it can represent both excitation and inhibition and evaluate spatially dependent effects such as whole-cell illumination in a modeled layer 5 cortical pyramidal neuron.
Problem solved
It addresses the need for a general simulation framework for diverse optogenetic mechanisms within neuronal models. The reported application specifically enabled analysis of how halorhodopsin illumination hyperpolarizes and silences a neuron even in the presence of driving input.
Problem links
Need precise spatiotemporal control with light input
DerivedThis tool is a generalised computational modeling technique for simulating optogenetic mechanisms in the NEURON environment. It was demonstrated with channelrhodopsin-2 and halorhodopsin to model optical activation and silencing in neurons.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
optical activationoptical activationoptical silencingoptical silencingspatially dependent modulation under concurrent illuminationspatially dependent modulation under illuminationTechniques
Computational DesignTarget processes
No target processes tagged yet.
Input: Light
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationimplementation constraint: spectral hardware requirementoperating role: builder
The method was implemented in the NEURON simulation environment. The available evidence indicates demonstration constructs based on channelrhodopsin-2 and halorhodopsin, but it does not provide further details on model equations, parameterization, illumination geometry implementation, or software distribution.
The supplied evidence is limited to a single 2013 source and a demonstration with channelrhodopsin-2 and halorhodopsin. No evidence here describes experimental validation against biological recordings, performance benchmarks, parameter inference procedures, or use beyond the reported modeled neuron context.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
In the modeled layer 5 cortical pyramidal neuron, whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and can silence the cell even when driving input is present.
We show that whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and is able to silence the cell even when driving input is present.
In the modeled layer 5 cortical pyramidal neuron, whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and can silence the cell even when driving input is present.
We show that whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and is able to silence the cell even when driving input is present.
In the modeled layer 5 cortical pyramidal neuron, whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and can silence the cell even when driving input is present.
We show that whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and is able to silence the cell even when driving input is present.
In the modeled layer 5 cortical pyramidal neuron, whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and can silence the cell even when driving input is present.
We show that whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and is able to silence the cell even when driving input is present.
In the modeled layer 5 cortical pyramidal neuron, whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and can silence the cell even when driving input is present.
We show that whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and is able to silence the cell even when driving input is present.
When channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the neural response is modulated toward depolarization.
However, when channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the response is modulated with a balance towards depolarization.
When channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the neural response is modulated toward depolarization.
However, when channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the response is modulated with a balance towards depolarization.
When channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the neural response is modulated toward depolarization.
However, when channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the response is modulated with a balance towards depolarization.
When channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the neural response is modulated toward depolarization.
However, when channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the response is modulated with a balance towards depolarization.
When channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the neural response is modulated toward depolarization.
However, when channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the response is modulated with a balance towards depolarization.
Approval Evidence
1 source2 linked approval claimsfirst-pass slug generalised-computational-modeling-technique-for-optogenetic-mechanisms
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
Source:
method demonstrationsupports
The computational modeling technique was demonstrated using channelrhodopsin-2 and halorhodopsin as examples of optical activation and silencing mechanisms.
It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).
Source:
method presentationsupports
The paper presents a generalised computational modeling technique for various optogenetic mechanisms implemented in the NEURON simulation environment.
Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment.
Source:
Comparisons
Source-backed strengths
A key strength is that the technique was explicitly described as generalised for various optogenetic mechanisms and implemented in the widely used NEURON simulation environment. It was demonstrated with both channelrhodopsin-2 and halorhodopsin, covering optical activation and silencing, and reproduced a specific simulation result in which whole-cell halorhodopsin illumination most effectively hyperpolarized a modeled layer 5 cortical pyramidal neuron.
Compared with light-emitting diode illumination
generalised computational modeling technique for optogenetic mechanisms and light-emitting diode illumination address a similar problem space.
Shared frame: shared mechanisms: optical activation; same primary input modality: light
Compared with mathematical model of light-induced expression kinetics
generalised computational modeling technique for optogenetic mechanisms and mathematical model of light-induced expression kinetics address a similar problem space.
Shared frame: same top-level item type; same primary input modality: light
Compared with model bioinformatics analysis
generalised computational modeling technique for optogenetic mechanisms and model bioinformatics analysis address a similar problem space.
Shared frame: same top-level item type; same primary input modality: light
Ranked Citations
- 1.