diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index ca06226bdef60d7f794a8fe162ad87ae7d43b7bd..f67d82c4020d2c12098325e2b8b86f2393fa1473 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -23,10 +23,16 @@
"* [S2. Multi-area model instantiation and simulation](#section_2)\n",
" * [2.1. Insantiate a multi-area model](#section_2_1)\n",
" * [2.2. Predict firing rates from theory](#section_2_2)\n",
- " * [2.3. Extract connectivity](#section_2_3)\n",
+ " * [2.3. Extract interarea connectivity](#section_2_3)\n",
" * [2.4. Run the simulation](#section_2_4)\n",
"* [S3. Data processing and simulation results analysis](#section_3)\n",
- "* [S4. Simulation results visualization](#section_4) "
+ "* [S4. Simulation results visualization](#section_4) \n",
+ " * [4.1. Instantaneous firing rate and mean firing rate](#section_4_1)\n",
+ " * [4.2. Raster plot of spiking activity for single area](#section_4_2)\n",
+ " * [4.3. Population-averaged firing rate](#section_4_3)\n",
+ " * [4.4. Average pairwise correlation coefficients of spiking activity](#section_4_4)\n",
+ " * [4.5. Irregularity of spiking activity](#section_4_5)\n",
+ " * [4.6. Time series of population- and area-averaged firing rates](#section_4_6)"
]
},
{
@@ -50,7 +56,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "96517739",
"metadata": {
"tags": []
@@ -217,15 +223,15 @@
"metadata": {},
"source": [
"1. `scale_down_to` <br>\n",
- "`scale_down_to` is the down-scaling factor which defines the the ratio of the full scale multi-area model being down-scaled to a model with fewer neurons and indegrees so as to be simulated on machines with lower computational ability and the simulation results can be obtained within relative shorter period of time. <br> Its deafualt value if 1. meaning full scale simulation. <br> In the pre-set downscale version, it's set as 0.005, where the numer of neurons and indegrees are both scaled down to 0.5% of its full scale amount, where the model can usually be simulated on a local machine. <br> **Warning**: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow <br> \n",
+ "`scale_down_to` is the down-scaling factor which defines the the ratio of the full scale multi-area model being down-scaled to a model with fewer neurons and indegrees so as to be simulated on machines with lower computational ability and the simulation results can be obtained within relative shorter period of time. <br> Its deafualt value if `1.` meaning full scale simulation. <br> In the pre-set downscale version, it's set as `0.005`, where the numer of neurons and indegrees are both scaled down to 0.5% of its full scale amount, where the model can usually be simulated on a local machine. <br> **Warning**: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow <br> \n",
"2. `cc_weights_factor` <br>\n",
- "This scaling factor controls the cortico-cortical synaptic strength. <br> By default it's set as 1.0, where the inter-area synaptic strength is the same as the intra-areal. <br> **Important**: This factor changes the network activity from ground state to metastable state. <br>\n",
+ "This scaling factor controls the cortico-cortical synaptic strength. <br> By default it's set as `1.0`, where the inter-area synaptic strength is the same as the intra-areal. <br> **Important**: This factor changes the network activity from ground state to metastable state. <br>\n",
"3. `areas_simulated` <br>\n",
"This parameter specifies the cortical areas included in the simulation process. Its default value is `complete_area_list` meaning all the areas in the complete_area_list will be actually simulated. <br>\n",
"complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH'] <br>\n",
- "The value assigned to simulation_areas can be any sublist of the `compete_area_list` specifying areas a user want to include in his/her simulation. <br>\n",
+ "The value assigned to simulation_areas can be any sublist of the compete_area_list specifying areas a user want to include in his/her simulation. <br>\n",
"4. `replace_non_simulated_areas` <br>\n",
- "The paramter `replace_non_simulated_areas` defines how non-simulated areas will be replaced. <br> It's set as None by default when the parameter areas_simulated is set as full_area_list where all areas will be simulated so that no areas need to be replaced. <br> Other options are: 'hom_poisson_stat', 'het_poisson_stat', and 'het_current_nonstat'. 'hom_poisson_stat' is a manually set parameter which can be tuned. When it's set as 'het_poisson_stat' or 'het_current_nonstat', the data to replace the cortico-cortical input is loaded from 'replace_cc_input_source' which is the firing rates of our full scale simulation results. The differenc between 'het_poisson_stat' and 'het_current_nonstat' is that 'het_poisson_stat' is the mean of the time-series firing rate so that it's static, yet 'het_current_nonstat' is time-varying specific current, which is varying by time. "
+ "The paramter `replace_non_simulated_areas` defines how non-simulated areas will be replaced. <br> It's set as `None` by default when the parameter areas_simulated is set as full_area_list where all areas will be simulated so that no areas need to be replaced. <br> Other options are: `'hom_poisson_stat'`, `'het_poisson_stat'`, and `'het_current_nonstat'`. `'hom_poisson_stat'` is a manually set parameter which can be tuned. When it's set as 'het_poisson_stat' or 'het_current_nonstat', the data to replace the cortico-cortical input is loaded from 'replace_cc_input_source' which is the firing rates of our full scale simulation results. The differenc between 'het_poisson_stat' and 'het_current_nonstat' is that 'het_poisson_stat' is the mean of the time-series firing rate so that it's static, yet 'het_current_nonstat' is time-varying specific current, which is varying by time. "
]
},
{
@@ -264,48 +270,50 @@
"metadata": {},
"outputs": [],
"source": [
- "# Connection parameters (conn_params)\n",
+ "# Connection parameters\n",
"conn_params = {\n",
- " # # It defines how non-simulated areas will be replaced\n",
- " # Whether to replace non-simulated areas by Poisson sources with the same global rate rate_ext ('hom_poisson_stat') or by specific rates ('het_poisson_stat') or by time-varying specific current ('het_current_nonstat'). In the two latter cases, the data to replace the cortico-cortical input is loaded from `replace_cc_input_source`\n",
- " 'replace_non_simulated_areas': 'het_poisson_stat', \n",
- "\n",
- " 'g': -11.,\n",
- " 'K_stable': 'K_stable.npy',\n",
- " # Increase the external input to 2/3E and 5E in area TH\n",
- " 'fac_nu_ext_TH': 1.2,\n",
- " # Increase the external Poisson indegree onto 5E\n",
- " 'fac_nu_ext_5E': 1.125,\n",
- " # Increase the external Poisson indegree onto 6E\n",
- " 'fac_nu_ext_6E': 1.41666667,\n",
- " # Adjust the average indegree in V1 based on monkey data\n",
- " 'av_indegree_V1': 3950.\n",
+ " 'replace_non_simulated_areas': 'het_poisson_stat', # Whether to replace non-simulated areas by Poisson sources with the same global rate, by default: None\n",
+ " 'g': -11., # It sets the relative inhibitory synaptic strength, by default: -16.\n",
+ " 'K_stable': 'K_stable.npy', # Whether to apply the stabilization method of Schuecker, Schmidt et al. (2017), by default: None\n",
+ " 'fac_nu_ext_TH': 1.2, # Increase the external input to 2/3E and 5E in area TH\n",
+ " 'fac_nu_ext_5E': 1.125, # Increase the external Poisson indegree onto 5E\n",
+ " 'fac_nu_ext_6E': 1.41666667, # Increase the external Poisson indegree onto 6E\n",
+ " 'av_indegree_V1': 3950. # Adjust the average indegree in V1 based on monkey data\n",
"}\n",
"\n",
- "# Input parameters (input_params)\n",
- "input_params = {'rate_ext': 10.}\n",
+ "# Input parameters\n",
+ "input_params = {\n",
+ " 'rate_ext': 10. # Rate of the Poissonian spike generator (in spikes/s)\n",
+ "} \n",
"\n",
- "# Neuron parameters (neuron_params)\n",
- "neuron_params = {'V0_mean': -150.,\n",
- " 'V0_sd': 50.}\n",
+ "# Neuron parameters\n",
+ "neuron_params = {\n",
+ " 'V0_mean': -150., # Mean for the distribution of initial membrane potentials, by default: -100.\n",
+ " 'V0_sd': 50.} # Standard deviation for the distribution of initial membrane potentials, by default: 50.\n",
"\n",
- "# Network parameters (network_params)\n",
- "network_params = {'N_scaling': scale_down_to,\n",
- " 'K_scaling': scale_down_to,\n",
- " 'fullscale_rates': 'tests/fullscale_rates.json',\n",
- " 'input_params': input_params,\n",
- " 'connection_params': conn_params,\n",
- " 'neuron_params': neuron_params}\n",
+ "# Network parameters\n",
+ "network_params = {\n",
+ " 'N_scaling': scale_down_to, # Scaling of population sizes, by default: 1.\n",
+ " 'K_scaling': scale_down_to, # Scaling of indegrees, by default: 1.\n",
+ " 'fullscale_rates': 'tests/fullscale_rates.json', # Absolute path to the file holding full-scale rates for scaling synaptic weights, by default: None\n",
+ " 'input_params': input_params, # Input parameters\n",
+ " 'connection_params': conn_params, # Connection parameters\n",
+ " 'neuron_params': neuron_params # Neuron parameters\n",
+ "} \n",
"\n",
- "# Simulation parameters (sim_params)\n",
- "sim_params = {'t_sim': 2000.,\n",
- " 'num_processes': 1,\n",
- " 'local_num_threads': 1,\n",
- " 'recording_dict': {'record_vm': False},\n",
- " 'rng_seed': 1} # global random seed\n",
+ "# Simulation parameters\n",
+ "sim_params = {\n",
+ " 't_sim': 2000., # Simulated time (in ms), by default: 10.0\n",
+ " 'num_processes': 1, # The number of MPI processes, by default: 1\n",
+ " 'local_num_threads': 1, # The number of threads per MPI process, by default: 1\n",
+ " 'recording_dict': {'record_vm': False},\n",
+ " 'rng_seed': 1 # global random seed\n",
+ "}\n",
"\n",
"# Theory paramters (theory_params)\n",
- "theory_params = {'dt': 0.1}"
+ "theory_params = {\n",
+ " 'dt': 0.1 # The time step of the mean-field theory integration, by default: 0.01\n",
+ "} "
]
},
{
@@ -407,7 +415,8 @@
}
],
"source": [
- "M = MultiAreaModel(network_params, simulation=True,\n",
+ "M = MultiAreaModel(network_params, \n",
+ " simulation=True,\n",
" sim_spec=sim_params,\n",
" theory=True,\n",
" theory_spec=theory_params)"
@@ -439,7 +448,7 @@
"source": [
"p, r = M.theory.integrate_siegert()\n",
"print(\"Mean-field theory predicts an average \"\n",
- " \"rate of {0:.3f} spikes/s across all populations.\".format(np.mean(r[:, -1])))"
+ " \"firing rate of {0:.3f} spikes/s across all populations.\".format(np.mean(r[:, -1])))"
]
},
{
@@ -447,7 +456,7 @@
"id": "2062ddf3",
"metadata": {},
"source": [
- "### 2.3. Extract connectivity <a class=\"anchor\" id=\"section_2_3\"></a>"
+ "### 2.3. Extract interarea connectivity <a class=\"anchor\" id=\"section_2_3\"></a>"
]
},
{
@@ -455,9 +464,7 @@
"id": "8a7c09e0",
"metadata": {},
"source": [
- "The connectivity and neuron numbers are stored in the attributes of the model class. Neuron numbers are stored in `M.N` as a dictionary (and in `M.N_vec` as an array), indegrees in `M.K` as a dictionary (and in `M.K_matrix` as an array). Number of synapses can also be access via `M.synapses` (and in `M.syn_matrix` as an array). <br>\n",
- "\n",
- "**Warning**: memory explosion"
+ "The connectivity and neuron numbers are stored in the attributes of the model class. Neuron numbers are stored in `M.N` as a dictionary (and in `M.N_vec` as an array), indegrees in `M.K` as a dictionary (and in `M.K_matrix` as an array). Number of synapses can also be access via `M.synapses` (and in `M.syn_matrix` as an array). <br>"
]
},
{
@@ -639,7 +646,7 @@
"id": "89c7b7cf",
"metadata": {},
"source": [
- "The following instructions will work when the `simulate` parameter is set to `True` during the creation of the MultiAreaModel object, and the `M.simulation.simulate()` method is executed."
+ "### 3.1 Test if the correct number of synapses has been created"
]
},
{
@@ -651,9 +658,6 @@
"source": [
"# Uncomment the lines in this code cell below to test if the number of synapses created by NEST matches the expected values\n",
"\n",
- "# \"\"\"\n",
- "# Test if the correct number of synapses has been created.\n",
- "# \"\"\"\n",
"# print(\"Testing synapse numbers\")\n",
"# for target_area_name in M.area_list:\n",
"# target_area = M.simulation.areas[M.simulation.areas.index(target_area_name)]\n",
@@ -674,6 +678,7 @@
"id": "57401110",
"metadata": {},
"source": [
+ "### 3.2 Extract connections information\n",
"To obtain the connections information, you can extract the lists of connected sources and targets. Moreover, you can access additional synaptic details, such as synaptic weights and delays."
]
},
@@ -721,7 +726,7 @@
"id": "8726a93d",
"metadata": {},
"source": [
- "### 1. Load spike data"
+ "### 3.3. Load spike data and compute instantaneous rate per neuron across all populations"
]
},
{
@@ -731,24 +736,7 @@
"metadata": {},
"outputs": [],
"source": [
- "data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + \"-spikes-1-0.dat\", skiprows=3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8793e033",
- "metadata": {},
- "source": [
- "### 2. Compute instantaneous rate per neuron across all populations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "9590223b",
- "metadata": {},
- "outputs": [],
- "source": [
+ "data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + \"-spikes-1-0.dat\", skiprows=3)\n",
"tsteps, spikecount = np.unique(data[:,1], return_counts=True)\n",
"rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)"
]
@@ -782,8 +770,7 @@
"id": "38ddd973",
"metadata": {},
"source": [
- "### 4.1. Instantaneous and mean rate\n",
- "avaerage over all areas"
+ "### 4.1. Instantaneous firing rate and mean firing rate <a class=\"anchor\" id=\"section_4_1\"></a>"
]
},
{
@@ -804,7 +791,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -819,9 +806,9 @@
"fig, ax = plt.subplots()\n",
"ax.plot(tsteps, rate)\n",
"ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')\n",
- "ax.set_title('instantaneous rate across all populations')\n",
+ "ax.set_title('Instantaneous firing rate across all populations')\n",
"ax.set_xlabel('time (ms)')\n",
- "ax.set_ylabel('rate (spikes / s)')\n",
+ "ax.set_ylabel('Firing rate (spikes / s)')\n",
"ax.set_xlim(0, sim_params['t_sim'])\n",
"ax.set_ylim(0, 50)\n",
"ax.legend()"
@@ -832,7 +819,7 @@
"id": "ae19bcc3",
"metadata": {},
"source": [
- "### 4.2 Raster plot of spiking activity for single area\n",
+ "### 4.2 Raster plot of spiking activity for single area <a class=\"anchor\" id=\"section_4_2\"></a>\n",
"Raster plot of spiking activity of 3% of the neurons in area V1 (A), V2 (B), and FEF (C). Blue: excitatory neurons, red: inhibitory neurons. (D-F) Spiking statistics across all 32 areas for the respective populations shown as area-averaged box plots. Crosses: medians, boxes: interquartile range (IQR), whiskers extend to the most extremeobservat ions within 1.5×IQR beyond the IQR."
]
},
@@ -875,19 +862,19 @@
"\n",
"\"\"\"\n",
"\n",
- " def init_analysis(self, ana_spec):\n",
- " assert(hasattr(self, 'simulation'))\n",
- " if 'load_areas' in ana_spec:\n",
- " load_areas = ana_spec['load_areas']\n",
- " else:\n",
- " load_areas = None\n",
- " if 'data_list' in ana_spec:\n",
- " data_list = ana_spec['data_list']\n",
- " else:\n",
- " data_list = ['spikes']\n",
- " self.analysis = Analysis(self, self.simulation,\n",
- " data_list=data_list,\n",
- " load_areas=load_areas)\n",
+ "def init_analysis(self, ana_spec):\n",
+ " assert(hasattr(self, 'simulation'))\n",
+ " if 'load_areas' in ana_spec:\n",
+ " load_areas = ana_spec['load_areas']\n",
+ " else:\n",
+ " load_areas = None\n",
+ " if 'data_list' in ana_spec:\n",
+ " data_list = ana_spec['data_list']\n",
+ " else:\n",
+ " data_list = ['spikes']\n",
+ " self.analysis = Analysis(self, self.simulation,\n",
+ " data_list=data_list,\n",
+ " load_areas=load_areas)\n",
" \n",
"area = 'V1'\n",
"frac_neurons = 0.03\n",
@@ -907,7 +894,7 @@
"id": "019d805e",
"metadata": {},
"source": [
- "### 4.3 Population-averaged firing rates"
+ "### 4.3 Population-averaged firing rate <a class=\"anchor\" id=\"section_4_3\"></a>"
]
},
{
@@ -951,7 +938,17 @@
"id": "06a595de",
"metadata": {},
"source": [
- "### 4.4 Average pairwise correlation coefficients of spiking activity"
+ "### 4.4 Average pairwise correlation coefficients of spiking activity <a class=\"anchor\" id=\"section_4_4\"></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "84d1689c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "compute_corrcoeff.py"
]
},
{
@@ -959,7 +956,18 @@
"id": "a3847e67",
"metadata": {},
"source": [
- "### 4.5 Irregularity measured by revised local variation LvR averaged across neurons"
+ "### 4.5 Irregularity of spiking activity <a class=\"anchor\" id=\"section_4_5\"></a>\n",
+ "Irregularity is measured by revised local variation LvR averaged across neurons"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d7480a9b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "create_pop_cv_isi(self, **keywords)"
]
},
{
@@ -967,7 +975,7 @@
"id": "90ae8f4c",
"metadata": {},
"source": [
- "### 4.6 Time series of population- and area-averaged firing rates\n",
+ "### 4.6 Time series of population- and area-averaged firing rates <a class=\"anchor\" id=\"section_4_6\"></a>\n",
"Area-averaged firing rates, shown as raw binned spike histograms with 1ms bin width (gray) and convolved histograms, with aGaussian kernel (black) of optimal width"
]
},