Neurons are simulated environments for individual modeling and neuron networks. It was mainly developed by Michael Hines, John W. Moore, and Ted Carnevale at Yale and Duke.
Neurons model individual neurons through the use of sections that are automatically split into individual compartments, rather than requiring the user to manually create compartments. The main scripting language is hoc but the Python interface is also available. Programs can be written interactively in the shell, or taken from a file. Neurons support parallelization through the MPI protocol.
Neurons are able to handle diffusion reaction models, and integrate diffusion functions into synaptic models and cellular networks. Parallelization is possible through internal multithread routines, for use on multi-core computers. The properties of the membrane channels of the neurons are simulated using a compiled mechanism written in NMODL or with a compiled routine that operates on the internal data structures set up with Channel Builder.
Along with the GENESIS analog device platform, Neuron is the basis for instruction in computational neuroscience in many programs and laboratories around the world.
Video Neuron (software)
User interface
Neuron features a graphical user interface (GUI), for use by individuals with minimal programming experience. The GUI comes with builders for single and dual compartment cells, networks, network cells, channels and linear electrical circuits. Single and multiple compartment cells differ in some compartment cells that display several "sections", each with potentially different parameters for dimensions and kinetics. Tutorials are available on the Neuron website, including to get a basic model of cell, channel and network makers. With this builder, users can form the basis of all simulations and models.
Cell Creator
Cell Builder allows users to create and modify cell structure stick figures. These sections form the basis of different areas of the neuron.
Users can specify different functional group sections. The parts that branch off from each other can be labeled "dendrites," while others, single sections projecting from the same center can be labeled as "axons." Users can specify parameters along which particular values ​​are variables as a function across sections. For example, the length of a path along a subset can be defined as a domain, a function which can then be determined later.
Users can select individual sections, or groups and set the appropriate parameters for length, diameter, area and length for the group or section. These values ​​can be specified as long functions or some other parameters of the appropriate section. Users can set the number of functional segments in a section, which is a strategy for spatial resolution. The higher the number of segments, the more precisely the Neuron can handle a function in a part. A segment is the point at which point process managers can be attributed.
Users can define kinetic and electro-physiological functions in both subsets and sections. Neurons are equipped with probabilistic models of Hodgkin-Huxley Model squid axon kinetics giant, as well as a function to model the kinetics of passive leakage channels. Both of these functions, and the features described, can be added to the constructed cell membrane. Values ​​for leakage, sodium conductance, and potassium conductance can be established for this kinetic modeling can be set as a function over the parameters domain. Channels become available to be implemented in cell membranes.
Channel Creator
Users can generate voltage and ligand-gated channel models. The Channel Maker supports local point channels, commonly used for single and large channels whose functions must be modeled, and common channels that density across cells can be determined. Maximum conductance, reversal potential, ligand sensitivity, ion permeability, and precise dynamics of the transition state using activation and inactivation variables, and including differential conductance, can be determined.
Network Builder and Cell Network
Neurons allow for mixed model generation, filled with artificial cells and neurons. Artificial cells essentially serve as point processes, implemented into the network. Artificial cells only require a point process, with specified parameters. Users can create the structure and dynamics of network cells. Users can create synapses, using synapse point synthesis as archetypes. The parameters in this point process can be manipulated to simulate an inhibitory and excitatory response. Synapses can be placed in certain segments of the built cell, where, again, they will behave as a point process, except that they are sensitive to the activity of pre-synaptic elements. The cells can be managed. The user creates a network cell base network, retrieving a previously completed network cell as an archetype. Connection can be defined between the source cell and the target synapses in other cells. The cell containing the target synapse becomes a post-synaptic element, while the source cells act as a pre-synaptic element. Weights can be added to determine the strength of synapse activation by pre-synaptic cells. The plot option can be activated to open a spike graph over time for individual neurons.
Simulation and recording
Neurons are equipped with many simulation tools. Notably, it includes several "point processes," which are simple functions in certain segments of the cell. The point process includes voltage simulation, patch, single electrode and current clamp, as well as several synapse simulations. The synapse point process is different because of their ability to model the intensity of stimulation that varies non-linearly over time. These can be placed on any segment of any part of the built cell, individual or network, and their exact values, including amplitude and duration of stimulation, activation delay times in run and time decay parameters (for synapses), can be defined from modules process management point. When implemented into a network as synapses, the point process parameters are defined in the synapse builder for specific network cells. Graphs depicting voltage, conductance, and current axes over time can be used to describe changes in electrical states at the location of each segment of the cell. Neurons make it possible to chart changes at both individual points over time, and across sections all the time. Run length can be set. All point processes, including those standing for synthetic cells or synthetic neurons, and all graphs reflect the duration.
Maps Neuron (software)
Example
This example creates a simple cell, with one soma compartment and a multi-compartment axon. It has the dynamics of cell membranes being simulated using Hodgkin-Huxley squid axon kinetics. The simulator stimulates the cell and runs for 50 ms.
Plots can be generated showing trace voltages ranging from soma and distal ends of axon. The action potential at the end of the axon arrives a little slower than it appears in the soma at the stimulation point. The plot is the membrane voltage with respect to time.
References
External links
- NEURON software website
- The NEURON Book
- Neuron Tutorial
- Tutorial for building model examples
- A network builder snippet, showing a complete simple network
- The creation of a function describing the differential conductance of several ions, taking the path length imitated from a part as a domain
- Tutorial about the functionality of the cell builder biophysics menu
- A zip file that creates a complete model of a simple neuron, with voltage vs. time and voltage vs. distance between time charts.
- Tutorials that include use of the run controller, process point manager, and graphics
- "Tutorial explaining the interface and network creation process".
Source of the article : Wikipedia
0 Comments