Go to the source code of this file.
Classes | |
| class | SeedMea.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 | SeedMea.CloseNeuron |
| Data structure for a neuron that is close to a channel pad. More... | |
Namespaces | |
| namespace | SeedMea |
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. | |
Functions | |
| def | SeedMea.countCells |
| def | SeedMea.densityReap |
| def | SeedMea.displace |
| def | SeedMea.diSqPlasma |
| def | SeedMea.gaussConnect |
| Calculate the probability of a connection between two neurons, based on their distance apart. | |
| def | SeedMea.genPCell |
| Takes a 2-D Numpy array and fill it in with a density map. | |
| def | SeedMea.grimReap |
| def | SeedMea.nextPowTwo |
| Find the next highest power of two, to get a shape that's good for plasma fractal generation. | |
| def | SeedMea.renderMap |
| Use pygame to render an array. | |
| def | SeedMea.squarePlasma |
Variables | |
| int | SeedMea.axon_growth = 220 |
| SeedMea.axon_max = dish_width | |
| tuple | SeedMea.Ce = Connection(P, P, 'ge') |
| int | SeedMea.cell_density = 900 |
| tuple | SeedMea.cells_per_edge = int(dish_width/soma_dia) |
| tuple | SeedMea.Ci = Connection(P, P, 'gi') |
| tuple | SeedMea.close_neurons = Channel() |
| tuple | SeedMea.conn_graph = nx.DiGraph() |
| list | SeedMea.conn_list = [] |
| int | SeedMea.connectivity_rate = 55 |
| int | SeedMea.dendrite_max = 180 |
| tuple | SeedMea.density_map = np.zeros((edge_len, edge_len)) |
| int | SeedMea.dish_width = 2500 |
| tuple | SeedMea.dist = math.sqrt((neuron_x_loc - pad_x_loc)**2 + (neuron_y_loc - pad_y_loc)**2) |
| tuple | SeedMea.distance = math.sqrt((from_neuron[0][0] - to_neuron[0][0])**2 + (from_neuron[0][1] - to_neuron[0][1])**2) |
| tuple | SeedMea.edge_len = nextPowTwo(cells_per_edge) |
| string | SeedMea.eqs |
| tuple | SeedMea.excitory_count = int(neuron_count * 0.75) |
| tuple | SeedMea.expected_cells = (dish_width/1000) |
| string | SeedMea.file_date = "{0}-{1}-{2}-{3}:{4}:{5}" |
| SeedMea.ignore_corners = True | |
| SeedMea.inhib_count = neuron_count-excitory_count | |
| tuple | SeedMea.inhib_neurons = random.sample(xrange(neuron_count), inhib_count) |
| tuple | SeedMea.neuron_count = int(countCells(density_map)) |
| int | SeedMea.neuron_id = 0 |
| list | SeedMea.neuron_list = [] |
| list | SeedMea.neuron_x_loc = neuron[0] |
| list | SeedMea.neuron_y_loc = neuron[0] |
| tuple | SeedMea.now = datetime.datetime.now() |
| tuple | SeedMea.P = NeuronGroup(neuron_count, eqs, threshold=-50*mV, reset=-60*mV) |
| int | SeedMea.pad_cols = 8 |
| int | SeedMea.pad_dia = 30 |
| dictionary | SeedMea.pad_neuron_map = {} |
| int | SeedMea.pad_rows = 8 |
| int | SeedMea.pad_spacing = 200 |
| int | SeedMea.pad_threshold = 40 |
| SeedMea.pad_x_loc = pad_x*pad_spacing | |
| SeedMea.pad_y_loc = pad_y*pad_spacing | |
| int | SeedMea.soma_dia = 30 |
| int | SeedMea.survival_rate = 65 |