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Modular Remapping Model on ModelDB
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Modular Realignment of Grid Cells as a Basis for Hippocampal Remapping
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:Authors: Joseph D. Monaco [1],
L. F. Abbott [2]
:Contact: jmonaco@jhu.edu
:Organization: [1] Zanvyl Krieger Mind/Brain Institute, Department of
Neuroscience, Johns Hopkins University, Baltimore, MD, USA; [2] Department
of Neuroscience, Department of Physiology and Cellular Biophysics, Columbia
University, New York, NY, USA
:Abstract: Hippocampal place fields, the local regions of activity
recorded from place cells in exploring rodents, can undergo large
changes in relative location during remapping. This process would
appear to require some form of modulated global input. Grid-cell
responses recorded from layer II of medial entorhinal cortex in rats
have been observed to realign concurrently with hippocampal
remapping, making them a candidate input source. However, this
realignment occurs coherently across colocalized ensembles of grid
cells (Fyhn et al., 2007). The hypothesized entorhinal contribution
to remapping depends on whether this coherence extends to all grid
cells, which is currently unknown. We study whether dividing grid
cells into small numbers of independently realigning modules can
both account for this localized coherence and allow for hippocampal
remapping. To do this, we construct a model in which place-cell
responses arise from network competition mediated by global
inhibition. We show that these simulated responses approximate the
sparsity and spatial specificity of hippocampal activity while fully
representing a virtual environment without learning. Place field
locations and the set of active place cells in one environment can
be independently rearranged by changes to the underlying grid-cell
inputs. We introduce new measures of remapping to assess the
effectiveness of grid-cell modularity and to compare shift
realignments with other geometric transformations of grid-cell
responses. Complete hippocampal remapping is possible with a small
number of shifting grid modules, indicating that entorhinal
realignment may be able to generate place-field randomization
despite substantial coherence.
Installation
------------
Please see the ``INSTALL`` file for details, but you essentially need to have the
Enthought EPD python distribution installed. Then you unzip this archive, go into
the new directory and run ``sudo python setup.py install``. The model can then be
run interactively in an IPython session.
Libraries
---------
Here is a brief description of the main modules and classes:
Top-level Modules
~~~~~~~~~~~~~~~~~
``dmec``
``GridCollection``: grid cell population model
``place_network``
``PlaceNetwork``: model simulation class
``PlaceNetworkStd``: model simulation class, search-optimized parameters
``place_network_ui``
``PlaceNetworkUI``: Chaco graphical frontend for model simulation
``placemap``
``PlaceMap``: spatial map class that computes place fields
``placemap_viewer``
``PlaceMapViewer``: Chaco graphical interface for PlaceMap objects
``ratemap``
``CheckeredRatemap``: PlaceMap subclass for rasterized simulation output
``stage``
``StagingMap``: simple handler for defining and indexing the environment
``trajectories``
``RandomWalk``: naturalistic random walk trajectory definition
``BipartiteRaster``: checkered rasterization defintion
Subpackages
~~~~~~~~~~~
``core``
- Base classes for models, analyses, parameter searches, and time-series data
``analysis`` [#ip]_
- ``altmodels``: extensions to inhibitory model
* ``ModelComparison``: analysis class for running model extensions
- ``compare``:
* ``compare_AB``: function that computes remapping measures
- ``map_funcs``: functions operating on spatial maps
- ``movie``:
* ``SweepMovie``: analysis class for creating remapping videos
- ``point``:
* ``PointSample``: analysis class for gathering statistics
- ``realign``:
* ``RealignmentSweep``: analysis class for remapping sweeps
- ``scan``:
* ``MultiNetworkScan``: analysis class for sampling parameter sweeps
- ``search``:
* ``PlaceNetworkSearch``: model parameter search definition
- ``sweep``:
* ``SingleNetworkSweep``: two-dimensional parameter sweeps
- ``two_rooms``:
* ``SmoothRemap``: analysis class for progressive remapping simulations
* ``SampleRemap``: analysis class for random sampling of remapping
``tools``
- A collection of scientific and utility support functions
.. [#ip]
These classes farm simulations out to IPython ipengine instances running
on your machine. You must first start them in another terminal::
$ ipcluster local -n C
Set ``C`` to the number of cores available on your machine.
Example Usage
-------------
You can run the model itself, specifying various parameters, or you can run
pre-cooked analyses that were used as the basis of figures in the paper.
Running the model
~~~~~~~~~~~~~~~~~
Start IPython in ``-pylab`` mode::
$ ipython -pylab
Then, import the libraries and create a model instance::
In [0]: from grid_remap import *
In [1]: model = PlaceNetworkStd()
To see all the user-settable parameters, you can print the model::
In [2]: print model
PlaceNetworkStd(Model) object
--------------------------------
Parameters:
C_W : 0.33000000000000002
EC : None
J0 : 45.0
N_CA : 500
done : False
dwell_factor : 5.0
monitoring : True
mu_W : 0.5
pause : False
phi_lambda : 0.040000000000000001
phi_sigma : 0.02
refresh_orientation : False
refresh_phase : False
refresh_traj : False
refresh_weights : True
tau_r : 0.050000000000000003
traj_type : 'checker'
Important model parameter definitions::
C_W feedforward connectivity
EC the GridCollection to use as input
J0 gain of global inhibition
N_CA the number of output units; each receives input from
C_W*N_EC grid cells
dwell_factor multiple of tau_r that defines raster pixel dwell time
mu_W average weight of feedforward synapses
phi_lambda nonlinearity threshold
phi_sigma nonlinearity smoothness (gain)
refresh_* orientation/phase reset per trial; new random weight
matrix per trial
tau_r time constant of place-unit integration
Parameters can be changed by passing them as keyword arguments to the
constructor. To simulate only 100 place units, you would call
``PlaceNetworkstd(N_CA=100)``.
Run the simulation::
In [3]: model.advance()
Look at the tracked data::
In [4]: pmap = CheckeredRatemap(model)
Running analyses
~~~~~~~~~~~~~~~~
To run the figure analyses, you simply create an analysis object and run it by
calling it with analysis parameters. To run progressive realignment
experiments using the ``RealignmentSweep`` analysis class, you would run::
In [20]: fig = RealignmentSweep(desc='test')
In [21]: fig.collect_data?
The first command creates an analysis object with the description 'test'. The
second command (with the ``?``) tells IPython to print out meta-data about the
``collect_data`` method. This is the method that actually performs the analysis
when you call the object, so this tells you the available parameters along with
their descriptions. We could run the analysis with modularity on the y-axis::
In [22]: fig(y_type='modules')
This performs the simulations, collects data for the figures, and stores data,
statistics, and an *analysis.log* file in the analysis directory. When that
completes, you can bring up the resulting figure and save it::
In [23]: fig.view()
In [24]: fig.save_plots()
Running the ``view`` method renders the figures, outputs RGB image files, and
saves a *figure.log* file in the analysis directory. Some of the figures have
parameter arguments to change the figure. You will have to use the
``create_plots`` method, as this is what the ``view`` method actually calls. To
see the figure parameters and make changes::
In [25]: fig.create_plots?
In [26]: fig.create_plots(...)
The same process can be used for the other figure analysis classes. You can
create your own analyses by subclassing from ``core.analysis.BaseAnalysis`` and
implementing the ``collect_data`` and ``create_plots`` methods.
----
Please explore the code, and let me know at `jmonaco@jhu.edu
<mailto:jmonaco@jhu.edu>`_ if there are any major issues. There are no guarantees
that this code will work perfectly everywhere.
Enjoy.