Fix spatial correlations

analysis/decode.py

@@ -31,7 +31,7 @@ def decode_bump(signal, axis=-1):
         dft = np.swapaxes(dft, axis, -1)
 
     m1 = 2.0 * np.absolute(dft) / length
-    phi = np.arctan2(dft.imag, dft.real) % (2.0 * np.pi)
+    phi = np.arctan2(dft.imag, dft.real)
 
     return m0, m1, phi
 

conf/config.yml

@@ -1,22 +1,176 @@
+##########################################
+# Simulation parameters
+##########################################
+IF_SAVE_DATA: 1
+# to load connectivity matrix from MAT_PATH
+IF_LOAD_MAT: 1
+# to save connectivity matrix from MAT_PATH
+IF_SAVE_MAT: 0
+
+IF_REC_SPIKE: 0
+
+# saving the last T_SAVE ms
+T_SAVE: 1000.0
+
+# path for the output files of the simulation
+DATA_PATH: /home/leon/models/lif_cpp/data/simul
+# path to load/save the connectivity matrix
+MAT_PATH: /home/leon/models/lif_cpp/data/matrix
+
+# output different prompts for debugging purpose
 verbose: 1
-N: 10000
-N_POP: 1
-K: 1000.0
 
-FRAC: [1.0]
+# Time step in ms
+DT: 0.1
+# total simulation time in ms
+DURATION: 5000.0
+# time to start showing simulation result ms
+T_STEADY: 0.0
+# Saving to files every T_WINDOW in ms
+T_WINDOW: 250.0
+
+##########################################
+# Network parameters
+##########################################
+# Total number of neurons
+N: 40000
+# Number of populations
+N_POP: 2
+# Average number of presynaptic inputs
+K: 4000.0
+# K: 4000.0
+# Fraction of neurons in each population
+FRAC: [0.8, 0.2]
 
-DT: 0.01
-DURATION: 1000.0
-T_WINDOW: 10.
+##########################################
+# Parameters for the stimulus presentation
+##########################################
+# stimulus has a cosine shape
+# time for stimulus onset/offset in ms 
+T_STIM: [1000.0, 1500.0]
+# amplitude of the stimulus
+A_STIM: [0.2, 0.0]
+# std of the stimulus
+STD_STIM: [0.0, 0.0]
+# Phase of the 1st stimulus
+PHI_STIM: [180.0, 0.0]
 
-# Lif Model
-V_THRESH: 1.0
-V_REST: 0.0
-V_LEAK: 0.0
+# Tuning of the stimulus
+KAPPA_STIM: [1.0, 0.0]
 
-TAU_MEM: [1.0]
-TAU_SYN: [1.0]
+T_DIST: [135000.0, 145000.0]
+# amplitude of the stimulus
+A_DIST: [0.0, 0.0]
+# std of the stimulus
+STD_DIST: [0.0, 0.0]
+# Phase of the 1st stimulus
+PHI_DIST: [90.0, 0.0]
+# Tuning of the stimulus
+KAPPA_DIST: [0.0, 0.0]
 
+CHECK_BISTABILITY: 0
+BUMP_SWITCH: [0, 0]
+##############
+# Network Dynamics
+##############
+# Threshold in mV
+V_THRESH: -50.0
+# Resting potential in mV
+V_REST: -70.0
+# Leak in mV
+V_LEAK: -50.0
+# Reversal in mV
+V_REV: [-20.0, -20.0]
+
+# Conductance based
+IF_COND_BASE: 0
+# Refractory period
+IF_THRESH_DYN: 0
+# threshold adaptation
+DELTA_THRESH: 5.0
+# absolute ref
+TAU_AREF: [2.0, 2.0]
+# refraction
+TAU_REF: [10.0, 10.0]
+
+# Membrane time constants in ms
+TAU_MEM: [20.0, 10.0]
+# Synaptic time constants in ms
+TAU_SYN: [4.0, 2.0]
+
+# Adding NMDA currents
+IF_NMDA: 1
+# NMDA time constants in s
+TAU_NMDA: [80.0, 40.0]
+# NMDA strength ratio
+R_NMDA: [0.5, 0.5]
+
+# Network's gain
 GAIN: 1.0
-Iext: [2.0]
-Jab: [-1.0]
+
+# Feedforward inputs strengths
+Iext: [2.0, 2.0]
+# FF rate
+M0: 1.0
+# Synaptic strengths 
+# Jab: [15.0, -1.35, 2.5, -2.0]
+# Jab: [4.5, -1.125, 0.625, -1.7]
+Jab: [3.5, -0.85, 0.6, -1.27]
+
+##############
+# CONNECTIVITY
+##############
+# PROBA can be 'cos', 'spec', 'gauss', 'None'
+# By default the matrix is a random sparse matrix Cij
+# 'cos' gives a sparse matrix with strong cosine structure,
+# Pij = (1 + KAPPA cos(theta_ij) / sqrt(Kb)), Cij = 1 with proba Pij
+# 'spec' gives a sparse matrix with weak cosine structure,
+# Pij = (1 + KAPPA cos(theta_ij) / sqrt(Kb)) , Cij = 1 with proba Pij
+
+PROBA: ['cos', 'cos', 'cos', 'cos']
+KAPPA: [1.0, 0.95, 1.0, 1.0]
+
+# PROBA: ['spec', 'rand', 'rand', 'rand']
+# KAPPA: [10.0, 0.0, 0.0, 0.0]
+
+# PROBA: ['lr', 'rand', 'rand', 'rand']
+# KAPPA: [3.0, 0.0, 0.0, 0.0]
+
+##############
+# STP
+##############
+# adds STP (as in Mato & Hansel, J Neurosci, 2012)
+IF_STP: 1
+IS_STP: [1, 0, 0, 0]
+USE: [0.03, 0.05, 0.5, 0.05]
+TAU_FAC: [600, 400.0, 250, 400]
+TAU_REC: [250, 850, 600, 850]
+
+############################
+# Feed Forward Input
+############################
+# adds gaussian noise to feedforward
+IF_FF_NOISE: 0
+# variance of the noise
+STD_FF: [0.01, 0.01]
+
+# adds cosine correlation to the feedforward
+IF_FF_CORR: 2
+# amplitude of the correlations
+A_CORR: [2.0, 1.7]
+# tuning of the correlations
+CORR_FF: [0.01, 0.01]
+
+####################
+# Low rank
+####################
+LR_SEED: 1
+LR_LOAD: 0
+
+LR_RANK: 3
+LR_MEAN: [0.0, 0.0, 0.0]
+LR_STD: [1.0, 1.0, 1.0]
+# ksi_1 ksi_2, ksi_1 h_s, ksi_2 h_s
+# LR_RHO: [0.05, 0.8, 0.2]
+# LR_RHO: [0.0, 0.8, -0.2]
+LR_RHO: [0.0, 1.0, 0.0]

conf/config.yml.bak

@@ -0,0 +1,176 @@
+##########################################
+# Simulation parameters
+##########################################
+IF_SAVE_DATA: 1
+# to load connectivity matrix from MAT_PATH
+IF_LOAD_MAT: 1
+# to save connectivity matrix from MAT_PATH
+IF_SAVE_MAT: 0
+
+IF_REC_SPIKE: 0
+
+# saving the last T_SAVE ms
+T_SAVE: 1000.0
+
+# path for the output files of the simulation
+DATA_PATH: /home/leon/models/lif_cpp/data/simul
+# path to load/save the connectivity matrix
+MAT_PATH: /home/leon/models/lif_cpp/data/matrix
+
+# output different prompts for debugging purpose
+verbose: 1
+
+# Time step in ms
+DT: 0.1
+# total simulation time in ms
+DURATION: 5000.0
+# time to start showing simulation result ms
+T_STEADY: 0.0
+# Saving to files every T_WINDOW in ms
+T_WINDOW: 250.0
+
+##########################################
+# Network parameters
+##########################################
+# Total number of neurons
+N: 40000
+# Number of populations
+N_POP: 2
+# Average number of presynaptic inputs
+K: 4000.0
+# K: 4000.0
+# Fraction of neurons in each population
+FRAC: [0.8, 0.2]
+
+##########################################
+# Parameters for the stimulus presentation
+##########################################
+# stimulus has a cosine shape
+# time for stimulus onset/offset in ms 
+T_STIM: [1000.0, 1500.0]
+# amplitude of the stimulus
+A_STIM: [0.2, 0.0]
+# std of the stimulus
+STD_STIM: [0.0, 0.0]
+# Phase of the 1st stimulus
+PHI_STIM: [180.0, 0.0]
+
+# Tuning of the stimulus
+KAPPA_STIM: [1.0, 0.0]
+
+T_DIST: [135000.0, 145000.0]
+# amplitude of the stimulus
+A_DIST: [0.0, 0.0]
+# std of the stimulus
+STD_DIST: [0.0, 0.0]
+# Phase of the 1st stimulus
+PHI_DIST: [90.0, 0.0]
+# Tuning of the stimulus
+KAPPA_DIST: [0.0, 0.0]
+
+CHECK_BISTABILITY: 0
+BUMP_SWITCH: [0, 0]
+##############
+# Network Dynamics
+##############
+# Threshold in mV
+V_THRESH: -50.0
+# Resting potential in mV
+V_REST: -70.0
+# Leak in mV
+V_LEAK: -50.0
+# Reversal in mV
+V_REV: [-20.0, -20.0]
+
+# Conductance based
+IF_COND_BASE: 0
+# Refractory period
+IF_THRESH_DYN: 0
+# threshold adaptation
+DELTA_THRESH: 5.0
+# absolute ref
+TAU_AREF: [2.0, 2.0]
+# refraction
+TAU_REF: [10.0, 10.0]
+
+# Membrane time constants in ms
+TAU_MEM: [20.0, 10.0]
+# Synaptic time constants in ms
+TAU_SYN: [4.0, 2.0]
+
+# Adding NMDA currents
+IF_NMDA: 1
+# NMDA time constants in s
+TAU_NMDA: [80.0, 40.0]
+# NMDA strength ratio
+R_NMDA: [0.5, 0.5]
+
+# Network's gain
+GAIN: 1.0
+
+# Feedforward inputs strengths
+Iext: [2.0, 2.0]
+# FF rate
+M0: 1.0
+# Synaptic strengths 
+# Jab: [15.0, -1.35, 2.5, -2.0]
+# Jab: [4.5, -1.125, 0.625, -1.7]
+Jab: [3.5, -0.85, 0.6, -1.27]
+
+##############
+# CONNECTIVITY
+##############
+# PROBA can be 'cos', 'spec', 'gauss', 'None'
+# By default the matrix is a random sparse matrix Cij
+# 'cos' gives a sparse matrix with strong cosine structure,
+# Pij = (1 + KAPPA cos(theta_ij) / sqrt(Kb)), Cij = 1 with proba Pij
+# 'spec' gives a sparse matrix with weak cosine structure,
+# Pij = (1 + KAPPA cos(theta_ij) / sqrt(Kb)) , Cij = 1 with proba Pij
+
+PROBA: ['cos', 'cos', 'cos', 'cos']
+KAPPA: [1.0, 0.95, 1.0, 1.0]
+
+# PROBA: ['spec', 'rand', 'rand', 'rand']
+# KAPPA: [10.0, 0.0, 0.0, 0.0]
+
+# PROBA: ['lr', 'rand', 'rand', 'rand']
+# KAPPA: [3.0, 0.0, 0.0, 0.0]
+
+##############
+# STP
+##############
+# adds STP (as in Mato & Hansel, J Neurosci, 2012)
+IF_STP: 1
+IS_STP: [1, 0, 0, 0]
+USE: [0.03, 0.05, 0.5, 0.05]
+TAU_FAC: [600, 400.0, 250, 400]
+TAU_REC: [250, 850, 600, 850]
+
+############################
+# Feed Forward Input
+############################
+# adds gaussian noise to feedforward
+IF_FF_NOISE: 0
+# variance of the noise
+STD_FF: [0.01, 0.01]
+
+# adds cosine correlation to the feedforward
+IF_FF_CORR: 2
+# amplitude of the correlations
+A_CORR: [2.0, 1.7]
+# tuning of the correlations
+CORR_FF: [0.01, 0.01]
+
+####################
+# Low rank
+####################
+LR_SEED: 1
+LR_LOAD: 0
+
+LR_RANK: 3
+LR_MEAN: [0.0, 0.0, 0.0]
+LR_STD: [1.0, 1.0, 1.0]
+# ksi_1 ksi_2, ksi_1 h_s, ksi_2 h_s
+# LR_RHO: [0.05, 0.8, 0.2]
+# LR_RHO: [0.0, 0.8, -0.2]
+LR_RHO: [0.0, 1.0, 0.0]