Related Documentation:

GENESIS: Documentation

1 Publication System: GUI Functionality

The examples in this document are based on publications [3][5][4][?]

This first section gives an overview of what is needed, and the technical comments follow in the next section.

1.1 Loading the different Purkinje cell models into G-3

The following sections list the purkinje cells that we use as targets for the implementation.

1.1.1 Passive Segev Model

Publication [?]

1.1.2 DeSchutter: 3 different guinea pigs dendritic morphologies

Publications: [3][5]

1.1.3 Sergio model

Publication: [?]

1.1.4 Japan model

Publications: [1][6]

1.1.5 Different species

We have morphological data for fish, turtle, rat, guinea pig and mouse purkinje cells. We can ask Rachael if she wants to contribute the zebra finch purkinje cells that she has. These morphologies can be handled as passive models (see Segev model above).

1.1.6 The NEURON Simulator Purkinje Cell Model

Importing Neuron models can be made possible. It assumed to take less time than the backward compatibility module because the Neuron script languages are not as sophisticated as the G-2 SLI.

1.1.7 Allan Purkinje Cell Model

Publication: [2]

Allan’s model is based on entirely different concepts than any of the other Purkinje cell models, but a user does not care much about such differences. Implementation of Allan’s model in G-3 is an interesting exercise we should do to check the flexibility and robustness of both high-level concepts and technical implementation.

1.2 Model Comparison

I have divided differently from what we discussed during the last meeting:

1.2.1 Structural Differences between Models

The G-Tube shows multiple morphology, passive models, channel characteristics and channel distribution tables simultaneously.

Both quantitative and structural differences can be visualized using different colors (green color: same parameters / structure, red color: differences in parameters / structure).

1.2.2 Behavioral Difference between Models

Behavioral differences between models can be shown by running simulations that simulate common experimental protocols:

A library of SSP schedules is used to configure the simulation required to produce these traces (see also below). The G-Tube allows to browse the SSP library and select a schedule for running a single simulation. More details about this library can be found in section 2.

1.2.3 Functional Difference between Models

1.3 Publication, Attribution and Lineage

The G-Tube allows to identify who has contributed what to a model, and to identify the impact of each contribution.

The fundamental building block of a publication is a publication atom. A publication atom is a direct component of a model (such as a channel instance, a synapse instance). Each publication atom is contributed by an author.

Every publication atom starts with an equal impact factor. The use-count of a publication atom determines its overall impact. This attribution model obviously gives high attribution scores to Hodgkin and Huxley and to Wilfrid Rall.

The G-Tube shows a table of all the people who has contributed to a model and what model component they have contributed.

1.4 Reconstruct the figures in the papers from the model in real time

A G-Tube publication has a browsable menu of bullet points / narrative components. It allows to explore the publication in depth. Its structure is outline as:

2 Technical Comments

2.1 Loading Different Models into the G-Tube

  1. The model name selection box of the G-Tube lists the names of all the models that can be loaded.
  2. Both the G-Tube and the gshell currently work with one implicit workspace, which limits them to importation of only one model at a time. The model-container uses ’namespaces’ as an abstraction for user workspaces. Gshell commands need to be implemented to access the interface to manage the model-container’s namespaces. The G-Tube connects to the gshell over its standard I/O stream connection to use the new commands.
  3. A namespace edit box will popup every time a new model is loaded to allow the user to edit the name of the namespace. The edit box suggests a namespace for use.
  4. The name of the namespace that was used to load the model, is visible in the model loader selection box.
  5. The namespaces known to the G-Tube are also available from a separate ’Recent’ sub-menu in the file menu.

2.2 Morphology Characterization

The following quantities are visible for both models simultaneously in a small table. This table with morphology characteristics is available from a button in the menu ’Model Construction’ –> ’Explore Model’. (this table should be split in two: one for morphology, one for passive parameters).

  1. soma dimensions (different between the two models)
  2. electrotonic length of the longest and shortest compartments (edsjb1994 has a very very long compartment).
  3. total dendritic length, surface area and volume.
  4. Number of branch points, average branch order of dendritic tips.
  5. RM, CM, RA, ELEAK, number of spines, number of compartments.
  6. List of transmembrane currents.

The quantities mentioned above can be computed by the model container and are made available as yaml text files to the GUI from the standard I/O connection with the gshell.

For example .

2.3 Channel Kinetics and Distribution

The GUI shows a list with all the transmembrane currents / channels found in the model.

  1. The channel characteristics table shows the gate parameters and the reversal potential for each channel (table 1 paper [3]).
  2. The channel distribution table shows the current densities (table 2 paper [3]).
  3. Clicking a button in the table with channel characteristics shows a plot of the steady-state and time constant against the membrane potential. This relationship is calculated by heccer and made available to the GUI via the gshell ’tabulate’ command.
  4. The voltage clamp current can be made visible too, using a library of SSP schedules (see below).

For example see .

2.4 SSP Library of Simulation Configurations

2.5 Importing Neuron Models

2.6 Other Models

Implementing Allan’s model in G3 will require a profound investigation for how to:

2.7 Publication and Attribution

2.8 Reconstruct the figures in the papers from the model in real time


[1]   K. Chono, H. Takagi, S. Koyama, H. Suzuki, and E. Ito. A cell model study of calcium influx mechanism regulated by calcium-dependent potassium channels in purkinje cell dendrites. J Neurosci Methods., 129(2):115–27, Oct 2003.

[2]   A. D. Coop and G. N. Reeke, Jr. The composite neuron: A realistic one-compartment purkinje cell model suitable for large-scale neuronal network simulations. J Comput Neurosci, 10:173–86, 2001.

[3]   E. De Schutter and J. Bower. An active membrane model of the cerebellar Purkinje cell I. Simulation of current clamps in slice. Journal of Neurophysiology, 71:375–400, 1994.

[4]   E. De Schutter and J. Bower. Simulated responses of cerebellar Purkinje cells are independent of the dentritic location of granule cell synaptic inputs. Proceedings of the National Academy of Sciences USA, 91:4736–4740, 1994.

[5]   E. De Schutter and J. M. Bower. An active membrane model of the cerebellar Purkinje cell II. Simulation of synaptic responses. Journal of Neurophysiology, 71:401–419, 1994.

[6]   T. Miyasho, H. Takagi, H. Suzuki, S. Watanabe, M. Inoue, Y. Kudo, and H. Miyakawa. Low-threshold potassium channels and a low-threshold calcium channel regulate Ca2+ spike firing in the dendrites of cerebellar purkinje neurons: A modeling study. Brain Res., 891(1–2):106–15, Feb 2001.