GitHub - ModelDBRepository/243447: SHOT-CA3, RO-CA1 Training, & Simulation CODE in models of hippocampal replay (Nicola & Clopath 2019)

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Code Instructions.pdf		Code Instructions.pdf
FIGURE_1.m		FIGURE_1.m
FIGURE_2.m		FIGURE_2.m
FIGURE_3.m		FIGURE_3.m
FIGURE_5.m		FIGURE_5.m
FORCE_TRAINING.m		FORCE_TRAINING.m
SORTING_SCRIPT.m		SORTING_SCRIPT.m
TRAINING_REVERSION_NEURONS.m		TRAINING_REVERSION_NEURONS.m
readme.html		readme.html

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<p><center><h3>Code Instructions</h3>
Update 07/2019</center><p/>
Note that for linux users, the MATLAB scripts referenced save .mat files with capital letters. This
generated problems in linux (but not Windows) in opening these files. All MATLAB scripts have
now been updated to save files with lowercase letters. Also, some of the MATLAB scripts may
require computers with greater than 4GB of RAM (or increased allocation of virtual memory in
the MATLAB preferences).<p/>
<center>Wilten Nicola, 06/2019</center><p/>
The code in these folders contains the scripts necessary to reproduce some of the figures in the
manuscript: A Diversity of Interneurons and Hebbian Plasticity Facilitates Rapid Compressible Learning
in Hippocampus, (2019), Nature Neuroscience, Wilten Nicola and Claudia Clopath. The DOI
code for this manuscript is <a href="http://doi.org/10.1038/s41593-019-0415-2">10.1038/s41593-019-0415-2</a>.<p/>
Note that the background current parameters for the RO-CA1 network in figure 5 were mislabeled as
-37.5 pA (RO-CA1 I) and -40 pA (RO-CA1E). The values used in figure 5 were actually -40 pA (RO-CA1I)
and -42.5 (RO-CA1E). We apologize for this confusion. This code was prototyped on MATLAB V. 2016a.<p/>
<h4>Note that there are a sequence of steps required in order for the figure scripts to run without error.</h4>
These steps are:<br/>
<center><h3>Steps for Running Code</h3></center>
<h4>Step 1: Run FORCE_training.m</h4>
Due to file size issues, the user has to run the training process themselves before activating the other
scripts, in order to generate the trained weight matrices in FORCE_trained.mat and the phase
preferences/ordering for the excitatory neurons in the file sortingid.mat. <b>Thus, the user should first run
the script FORCE_training.m to generate these files for subsequent use</b>, other wise MATLAB error
messages for missing files will appear when the figure scripts are used. The training script should take on
the order of an hour with reasonable computing resources while the other scripts vary in duration, but
are generally faster to run. Finally, the data file generated for the FORCE_training script is 1-2 gigabytes.
The FORCE_training.m automatically calls the sorting_script.m to phase sort the weights/neurons.<p/>
Note that the random number seed was not fixed when we conducted these simulations in the
manuscript, so some small numerical differences in the outputs may occur due to differences in the final
trained weight matrix. However, we have fixed the random number seed in this code to (1) and
recommend leaving it at that value in order to implement the reversion neuron population (Figure 2).
The reason for this is that the reversion neuron implementation is dependent on a phase relationship
between the reversion interneuron population and the SHOT-interneuron population. We manually
searched for a component in the SHOT-CA3 FORCE components that has the correct phase relationship
and the implementation is based on this (See Materials and Methods in manuscript). If the random
number seed is altered, the user has to manually search the integer parameter "qin" in the code for
Figure_3.m in order to implement the reversion. This typically only takes 1-2 minutes as the phase
relationship need not be exact (see Materials and Methods of manuscript).
<h4>Step 2: Run Training_Reversion_Neurons.m</h4>
This script trains a population of reversion neurons with a series of weights (determined offline, not
through FORCE) in order for the reversion interneuron population to decode a bank of oscillators. The
decoded FORCE trained oscillators (decoded from the SHOT-CA3 population) serve as the supervisors for
the reversion interneuron population. One of these reversion-interneuron-decoded oscillators is then
used to implement the reversion current in Figure_3.m (the "qin" parameter selects the particular
oscillator used).<p/>
After Steps 1. And 2. are completed, one can run the following script files:<p/>
<b>Figure_1.m:</b> Runs the population of SHOT-CA3 neurons post training with INP-MS on, and then off at
time t = tcrit.<p/>
<b>Figure_2.m:</b> Runs the population of SHOT-CA3 neurons with recurrent excitatory connections. The
default parameter values in this script consider the case where both E-to-E and E-to-I connections are
present.<p/>
<b>Figure_3.m:</b> Runs the mirror reversion protocol as in Figure 3 of the main manuscript with a population
of reversion interneurons, and the SHOT-CA3 network.<p/>
<b>Figure_5.m:</b> Runs the SHOT-CA3 and RO-CA1 training task with the local Fourier rule operating as in
Figure 5 of the main manuscript. Note that for the time interval considered where the Medial Septum is
off, different random seeds of this simulation may yield different results (or phase distortion), or
different levels of activity in RO-CA1.
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