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parameters_2.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Oct 25 20:39:04 2016
@author: daniel
"""
#from math import sqrt
results_directory = './results'
#-----------------------------------------------------------#
# 1. General recording and simulation parameters #
#-----------------------------------------------------------#
step = 20.0
record_step = 1
record_step_v = 1
record_step_PDC = 1000
skip_first_x_ms = 100
nrn_dots_per_1ms = 1.0/record_step
time_to_avg_over = 20 # in seconds
simtime = 600
training_mode = 'supra'
num_trials = 20
NUMBER_OF_PROCESSES = 7
#-----------------------------------#
# 2. Synaptic parameters #
#-----------------------------------#
esyn_tau = 6
isyn_tau = 6
isyn_plateau_tau = 10
e_esyn = 0
e_gaba = -60
erev_NMDA = 15
erate = 0.2
irate = 0.2
pos = 0.05
# NMDA parameters
#Mg = 1.0
#alpha = 0.072
#eta = 0.28
#
Mg = 1.0
alpha = 0.062
eta = 0.381679389
#Mg = 1.4
#alpha = 0.099
#eta = 1.0/12
g_ramp_max = 0.000255
nmda_ampa_ratio = 1
gAMPAmax = 1.0e-3
gNMDAmax = gAMPAmax*nmda_ampa_ratio
gGABAmax = 1.5e-3
g_expsyn_max = 0.1e-3
g_inhexpsyn_max = gGABAmax
U = 0.3
u0 = 0.0
tauF = 5.0
gAMPAmax_plateau = 3.0*1.0e-3#0.75*1.0e-3
gNMDAmax_plateau = 3.0*1.0e-3#4.5*1.0e-3
gGABAmax_plateau = 1.5e-3
nmda_ampa_ratio = gNMDAmax_plateau/gAMPAmax_plateau
ratio_glutamate_syn = 1.0
ratio_distributed_synapses = 0.2
nmda_ca_fraction = 0.175
gmaxAMPA_spillover = 15e-3 #0.015
gmaxNMDA_spillover = 2.5e-3
gmaxNMDAe_spillover = 5.0e-3 #0.0075
gmaxAMPA_pf = 2.5e-3
gmaxNMDA_pf = 4.5e-3
Beta = 0.01
weight = 0.25
Cdur = 1.
Cdur_pf = 50
eCdur_init = 50
eCdur_factor = 200
eCdur = eCdur_init + eCdur_factor*weight
width = 0.05
delay_exnmda = 5
random_initial_weights = False
start_weight = 0.15
end_weight = 0.25
distribution = 'uniform'
deterministic_interval = 1
net_con_interval = 0
num_spikes = 1
exglu_weight = 0.37
exglu_tau = simtime
exglu_norm_factor = 0.25*1/num_spikes
tau1_NMDA = 2.76
tau2_NMDA = 115.5
tau1_exp2syn = 1.9
tau2_exp2syn = 4.8
tau1_inhexp2syn = 1
tau2_inhexp2syn = 10
tau_cadyn_nmda = 100
tau_caldyn = 100
tau_catdyn = 100
#-----------------------------------------#
# 3. Synaptic input parameters #
#-----------------------------------------#
plateau_syn_rate = 400
plateau_burst_start = 100
plateau_burst_end = 130
plateau_cluster_size = 20
plateau_cluster_size_max = 41
cluster_start_pos = 0.45
cluster_end_pos = 0.60
xor_input_window = 35
xor_input_size = 30
syns_per_feature = 5
pf_input_rate = 1
pf_input_start = plateau_burst_start
pf_input_window = 70
pf_input_end = plateau_burst_end+pf_input_window
pf_input_size = 10
pf_num_spikes = 3
pf_input_interval = 1
inhibitory_syn_rate = 105.0
inhibitory_burst_start = 180
inhibitory_burst_end = 230
inhibitory_cluster_size = 5
inh_cluster_start_pos = 0.45
inh_cluster_end_pos = 0.60
distributed_input_rate = 1000.0/40
distributed_input_start = 130
distributed_input_end = 200
distributed_input_size = 0
distributed_input_window = 50
correlated_distributed_inputs = False
ramp_syn_rate = 100.0
ramp_slope = 0.5 # Hz/ms
ramp_burst_start = 200
ramp_burst_end = 1000
e_interval = 1.0/erate*(10**3)
i_interval = 1.0/irate*(10**3)
plateau_syn_interval = 1.0/plateau_syn_rate*(10**3)
distributed_input_interval = 1.0/distributed_input_rate*(10**3)
ramp_syn_interval = 1.0/ramp_syn_rate*(10**3)
inhibitory_syn_interval = 1.0/inhibitory_syn_rate*(10**3)
#--------------------------------#
# 4. Spine parameters #
#--------------------------------#
head_L = 0.5
head_diam = 0.5
neck_L = 0.5
neck_diam = 0.125
neck_Ra = 1130.0
head_Ra = 150
kb_cadyn_nmda = 96
kt_cadyn_nmda = 1e-4
kd_cadyn_nmda = 1e-4#0.3e-3
include_empty_spines = False
#-----------------------------------------------------------#
# 5. XOR problem and adaptive synapse parameters #
#-----------------------------------------------------------#
event_times = [200, 500, 900]
x5_training_file = 'data_fig3_red_banana.dat'
save_weights_file = 'one_neuron.dat'
load_weights_file = 'one_neuron.dat'
random_training_sequence = False
adaptive_distributed_inputs = True
long_simulation = False
training_set_size_per_group = 40
training_set_size = training_set_size_per_group*4
training_input_length = 30
first_training_input_start = 200
time_to_reward = 400 - training_input_length
reward_length = 20
session_length = 800
test_set_size_per_group = 1
test_set_size = test_set_size_per_group*4
window_error = 2
record_step_thresh = session_length/2
#learning_rate_w_LTP = 0.000015#0.01
#learning_rate_w_LTD = 0.003#0.035
#learning_rate_w_LTD_pf = 0#0.05
#learning_rate_thresh_LTP = 0.0000001#0.00035
#learning_rate_thresh_LTD = 0.0000004#0.00035*4
#learning_rate_thresh_KD_LTD = 0.05
#lthresh_LTP_min = 0.01
learning_rate_w_LTP = 0#0.035
learning_rate_w_LTD = 0#0.035
learning_rate_w_LTD_pf = 0#0.05
learning_rate_thresh_LTP = 0#0.00035
learning_rate_thresh_LTD = 0#0.00035
learning_rate_thresh_KD_LTD = 0# 0.05
lthresh_LTP_min = 0.01
n1 = 6000
n2 = 1000
KD1 = 0.0022
KD2 = 0.02
KD_LTD = 0.001
n_LTD = 3000
KD_LTD_pf = 0.0001
n_LTD_pf = 1000
random_weights = False
read_input_config_from_file = False
input_config_file = 'xor_inputs_to_dends.dat'#'xor_dense.dat'
# input_dends = [5, 14] #[3, 5, 8, 12, 15, 22, 26, 35, 41, 47, 53, 57]#
input_dends = [3, 5, 8, 12, 15, 22, 26, 35, 41]#
pf_input_dends = []#[5, 22] #[12, 22, 26, 35, 41, 53] #
# independent_dends = [3, 5, 8, 12, 14, 15, 22, 26, 41, 47, 52]
independent_dends = [3, 5, 8, 12, 14, 15, 22, 26, 41]
input_dends = independent_dends
cluster_start_poss = [0.55, 0.45, 0.4, 0.55, 0.75, 0.3, 0.3, 0.4, 0.85, 0.4, 0.25]
cluster_end_poss = [0.7, 0.7, 0.5, 0.7, 0.95, 0.5, 0.45, 0.55, 0.95, 0.6, 0.45]
params_for_learning_functions = 'params_for_learning_funtions.dat'
input_dends_dict = {'loc': input_dends,#[3, 5, 15, 22, 53],
'pos': cluster_start_poss,#, 0.55, 0.05, 0.55],
'end_pos': cluster_end_poss,
'start': [plateau_burst_start]*2,
'end': [plateau_burst_end]*2 }
pf_input_dends_dict = {'loc': pf_input_dends,#[5, 15, 22],
'pos': cluster_start_poss,
'end_pos': cluster_end_poss,#, 0.05],
'start': [plateau_burst_start]*2,
'end': [plateau_burst_end]*2 }
#simtime = first_training_input_start + training_set_size*session_length
#simtime = first_training_input_start + test_set_size*session_length
#simtime = first_training_input_start + training_set_size*(training_input_length+
# time_to_reward + reward_length)
#----------------------------------#
# 6. Plotting parameters #
#----------------------------------#
#-----------------------------------#
# 6.1. For XOR experiment #
#-----------------------------------#
#-------------------------------------------------------#
# 7. Miscellaneous and parameter dictionaries #
#-------------------------------------------------------#
dends_per_plot = 1
scale_conductance = 1000