This is a ROS node that is used by itself to generate network connectivity pickle files for the brian_recv.py and brian_to_csv.py nodes. More...
Classes | |
| class | Channel |
| Data structure for a MEA channel pad neurons: list of neurons within range of the pad total_weight: combined weights of all the neurons. More... | |
| class | CloseNeuron |
| Data structure for a neuron that is close to a channel pad. More... | |
Functions | |
| def | countCells |
| def | densityReap |
| def | displace |
| def | diSqPlasma |
| def | gaussConnect |
| Calculate the probability of a connection between two neurons, based on their distance apart. | |
| def | genPCell |
| Takes a 2-D Numpy array and fill it in with a density map. | |
| def | grimReap |
| def | nextPowTwo |
| Find the next highest power of two, to get a shape that's good for plasma fractal generation. | |
| def | renderMap |
| Use pygame to render an array. | |
| def | squarePlasma |
Variables | |
| int | axon_growth = 220 |
| axon_max = dish_width | |
| tuple | Ce = Connection(P, P, 'ge') |
| int | cell_density = 900 |
| tuple | cells_per_edge = int(dish_width/soma_dia) |
| tuple | Ci = Connection(P, P, 'gi') |
| tuple | close_neurons = Channel() |
| tuple | conn_graph = nx.DiGraph() |
| list | conn_list = [] |
| int | connectivity_rate = 55 |
| int | dendrite_max = 180 |
| tuple | density_map = np.zeros((edge_len, edge_len)) |
| int | dish_width = 2500 |
| tuple | dist = math.sqrt((neuron_x_loc - pad_x_loc)**2 + (neuron_y_loc - pad_y_loc)**2) |
| tuple | distance = math.sqrt((from_neuron[0][0] - to_neuron[0][0])**2 + (from_neuron[0][1] - to_neuron[0][1])**2) |
| tuple | edge_len = nextPowTwo(cells_per_edge) |
| string | eqs |
| tuple | excitory_count = int(neuron_count * 0.75) |
| tuple | expected_cells = (dish_width/1000) |
| string | file_date = "{0}-{1}-{2}-{3}:{4}:{5}" |
| ignore_corners = True | |
| inhib_count = neuron_count-excitory_count | |
| tuple | inhib_neurons = random.sample(xrange(neuron_count), inhib_count) |
| tuple | neuron_count = int(countCells(density_map)) |
| int | neuron_id = 0 |
| list | neuron_list = [] |
| list | neuron_x_loc = neuron[0] |
| list | neuron_y_loc = neuron[0] |
| tuple | now = datetime.datetime.now() |
| tuple | P = NeuronGroup(neuron_count, eqs, threshold=-50*mV, reset=-60*mV) |
| int | pad_cols = 8 |
| int | pad_dia = 30 |
| dictionary | pad_neuron_map = {} |
| int | pad_rows = 8 |
| int | pad_spacing = 200 |
| int | pad_threshold = 40 |
| pad_x_loc = pad_x*pad_spacing | |
| pad_y_loc = pad_y*pad_spacing | |
| int | soma_dia = 30 |
| int | survival_rate = 65 |
This is a ROS node that is used by itself to generate network connectivity pickle files for the brian_recv.py and brian_to_csv.py nodes.
This node creates the connectivity maps, and those nodes run the actual simulation. You only need to run this node if you do not yet have pickle files or you want to generate new ones.
More info:
Generates a connectivity map by simulating growth of seeded neuron cells.
The growth simulation is a three-stage process: 1. Determine the cell density for each region of the MEA. 2. Assign cells to locations based on density. 3. Connect the cells based on a connectivity function.
Cell density is assigned based on random midpoint displacement fractals, aka "plasma fractals". This approach was chosen because it provides a stochastic approach that is computationally efficient. If it is not a good model of the actual distribution of neurons, a different algorithm can be used.
Cells are probabilistically assigned to locations based on the cell density (actually cell probability) at that point. Cells are modeled as point locations.
For each pair of cells, the probability of their connection is determined based on the distance between the cells and the cells are connected accordingly. Note that this results in N(N-1) comparisons, so roughly n^2 connections. Put that in your NP pipe and smoke it.
| def SeedMea.countCells | ( | density_map | ) |
Definition at line 217 of file SeedMea.py.
| def SeedMea.densityReap | ( | density_map, | |
| expected_cells | |||
| ) |
Definition at line 196 of file SeedMea.py.
| def SeedMea.displace | ( | size, | |
| originalSize | |||
| ) |
Definition at line 109 of file SeedMea.py.
| def SeedMea.diSqPlasma | ( | array | ) |
Definition at line 114 of file SeedMea.py.
| def SeedMea.gaussConnect | ( | distance, | |
| center | |||
| ) |
Calculate the probability of a connection between two neurons, based on their distance apart.
Generates a value from 0 to 1, centered at
Definition at line 159 of file SeedMea.py.
| def SeedMea.genPCell | ( | density_map | ) |
Takes a 2-D Numpy array and fill it in with a density map.
The map values are in the range 0-1 and describe the probability of there being a cell soma located at that location. 1 is a cell, 0 is no cell.
Definition at line 95 of file SeedMea.py.
| def SeedMea.grimReap | ( | density_map, | |
| survival_rate | |||
| ) |
Definition at line 188 of file SeedMea.py.
| def SeedMea.nextPowTwo | ( | start | ) |
Find the next highest power of two, to get a shape that's good for plasma fractal generation.
Definition at line 152 of file SeedMea.py.
| def SeedMea.renderMap | ( | densityData, | |
| title | |||
| ) |
Use pygame to render an array.
The result is an image of the same dimensionality as the array, with each pixel set to 255*the value of the corresponding array location.
Definition at line 164 of file SeedMea.py.
| def SeedMea.squarePlasma | ( | array | ) |
Definition at line 117 of file SeedMea.py.
| int SeedMea::axon_growth = 220 |
Definition at line 234 of file SeedMea.py.
Definition at line 236 of file SeedMea.py.
| tuple SeedMea::Ce = Connection(P, P, 'ge') |
Definition at line 355 of file SeedMea.py.
| int SeedMea::cell_density = 900 |
Definition at line 242 of file SeedMea.py.
| tuple SeedMea::cells_per_edge = int(dish_width/soma_dia) |
Definition at line 246 of file SeedMea.py.
| tuple SeedMea::Ci = Connection(P, P, 'gi') |
Definition at line 356 of file SeedMea.py.
| tuple SeedMea::close_neurons = Channel() |
Definition at line 396 of file SeedMea.py.
| tuple SeedMea::conn_graph = nx.DiGraph() |
Definition at line 315 of file SeedMea.py.
| list SeedMea::conn_list = [] |
Definition at line 297 of file SeedMea.py.
| int SeedMea::connectivity_rate = 55 |
Definition at line 240 of file SeedMea.py.
| int SeedMea::dendrite_max = 180 |
Definition at line 232 of file SeedMea.py.
| tuple SeedMea::density_map = np.zeros((edge_len, edge_len)) |
Definition at line 252 of file SeedMea.py.
| int SeedMea::dish_width = 2500 |
Definition at line 231 of file SeedMea.py.
| tuple SeedMea::dist = math.sqrt((neuron_x_loc - pad_x_loc)**2 + (neuron_y_loc - pad_y_loc)**2) |
Definition at line 402 of file SeedMea.py.
| tuple SeedMea::distance = math.sqrt((from_neuron[0][0] - to_neuron[0][0])**2 + (from_neuron[0][1] - to_neuron[0][1])**2) |
Definition at line 301 of file SeedMea.py.
| tuple SeedMea::edge_len = nextPowTwo(cells_per_edge) |
Definition at line 249 of file SeedMea.py.
| string SeedMea::eqs |
00001 ''' 00002 dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt 00003 dge/dt = -ge/(5*ms) : volt 00004 dgi/dt = -gi/(10*ms) : volt 00005 '''
Definition at line 334 of file SeedMea.py.
| tuple SeedMea::excitory_count = int(neuron_count * 0.75) |
Definition at line 346 of file SeedMea.py.
| tuple SeedMea::expected_cells = (dish_width/1000) |
Definition at line 269 of file SeedMea.py.
| string SeedMea::file_date = "{0}-{1}-{2}-{3}:{4}:{5}" |
Definition at line 312 of file SeedMea.py.
| SeedMea::ignore_corners = True |
Definition at line 370 of file SeedMea.py.
Definition at line 347 of file SeedMea.py.
| tuple SeedMea::inhib_neurons = random.sample(xrange(neuron_count), inhib_count) |
Definition at line 354 of file SeedMea.py.
| tuple SeedMea::neuron_count = int(countCells(density_map)) |
Definition at line 341 of file SeedMea.py.
| int SeedMea::neuron_id = 0 |
Definition at line 287 of file SeedMea.py.
| list SeedMea::neuron_list = [] |
Definition at line 286 of file SeedMea.py.
| list SeedMea::neuron_x_loc = neuron[0] |
Definition at line 399 of file SeedMea.py.
| list SeedMea::neuron_y_loc = neuron[0] |
Definition at line 400 of file SeedMea.py.
| tuple SeedMea::now = datetime.datetime.now() |
Definition at line 311 of file SeedMea.py.
| tuple SeedMea::P = NeuronGroup(neuron_count, eqs, threshold=-50*mV, reset=-60*mV) |
Definition at line 342 of file SeedMea.py.
| int SeedMea::pad_cols = 8 |
Definition at line 369 of file SeedMea.py.
| int SeedMea::pad_dia = 30 |
Definition at line 371 of file SeedMea.py.
| dictionary SeedMea::pad_neuron_map = {} |
Definition at line 378 of file SeedMea.py.
| int SeedMea::pad_rows = 8 |
Definition at line 368 of file SeedMea.py.
| int SeedMea::pad_spacing = 200 |
Definition at line 372 of file SeedMea.py.
| int SeedMea::pad_threshold = 40 |
Definition at line 373 of file SeedMea.py.
| SeedMea::pad_x_loc = pad_x*pad_spacing |
Definition at line 392 of file SeedMea.py.
| SeedMea::pad_y_loc = pad_y*pad_spacing |
Definition at line 393 of file SeedMea.py.
| int SeedMea::soma_dia = 30 |
Definition at line 230 of file SeedMea.py.
| int SeedMea::survival_rate = 65 |
Definition at line 238 of file SeedMea.py.