matlab code for
Chow S-F, Wick SD, Riecke H (2012) Neurogenesis Drives Stimulus
Decorrelation in a Model of the Olfactory Bulb. PLoS Comput Biol 8(3):
e1002398. doi:10.1371/journal.pcbi.1002398
run main.m for figures similar to fig.2, but with only 26 channels
set sim = 10 for 442 channels
run main2.m for figures similar to fig.9
enrich = [1 2]; for related enrichment
enrich = [3 4]; for unrelated enrichment
figures:
1: input patterns and correlations
2: output patterns and correlations, connection
3: GC information
4: correlation
blue: mean correlation of all paris
red: mean correlation of tracked pairs (see parameter tracking)
101: 2d representation of input patterns
102: 2d representation of output patterns
parameters:
sim: level of down sampling
for the default input set, sim 40 -> 26 channels, sim 10 ->
442 channels
odor_names: file names of patterns from http://gara.bio.uci.edu/
choose: odors chosen to be in the training set
non_lin: 0 for linear network, 1 for rectified nonlinear network
(much slower)
conn: number of connection each GC makes (mean # connection for
prob_conn = 1)
CS: coupling strength
ts: threshold of survival function
gamma: steepness of survival function
th: minimal GC activity that would count towards survival
rm, rg: thresholds for rectifer, only works for non_lin == 1
cont_density: 0 for discrete GC population, 1 for population description
population description version runs very slow for large network
exp_time: total experiment time
step: plotting/output interval
dt: for equation stepping
tracking:
track mean correlation for a subset of the odor pairs
We gratefully acknowledge the support of NSF grant DMS-0719944
20121127 matlab code in main.m main2.m modified by replacing ~ with
the variable "ignore" for backwards compatibility with matlab versions
before R2009b.