def generate(ref='Example6_PyNN', add_inputs=True): ################################################################################ ### Build new network net = Network(id=ref, notes='Another network for PyNN - work in progress...') net.parameters = { 'N_scaling': 0.005, 'layer_height': 400, 'width': 100, 'depth': 100, 'input_weight': 0.1 } cell = Cell(id='CorticalCell', pynn_cell='IF_curr_exp') cell.parameters = { 'cm': 0.25, # nF 'i_offset': 0.0, # nA 'tau_m': 10.0, # ms 'tau_refrac': 2.0, # ms 'v_reset': -65.0, # mV 'v_rest': -65.0, # mV 'v_thresh': -50.0 # mV } net.cells.append(cell) if add_inputs: input_cell = Cell(id='InputCell', pynn_cell='SpikeSourcePoisson') input_cell.parameters = { 'start': 0, 'duration': 10000000000, 'rate': 150 } net.cells.append(input_cell) e_syn = Synapse(id='ampa', pynn_receptor_type='excitatory', pynn_synapse_type='curr_exp', parameters={'tau_syn': 0.5}) net.synapses.append(e_syn) i_syn = Synapse(id='gaba', pynn_receptor_type='inhibitory', pynn_synapse_type='curr_exp', parameters={'tau_syn': 0.5}) net.synapses.append(i_syn) N_full = { 'L23': { 'E': 20683, 'I': 5834 }, 'L4': { 'E': 21915, 'I': 5479 }, 'L5': { 'E': 4850, 'I': 1065 }, 'L6': { 'E': 14395, 'I': 2948 } } scale = 0.1 pops = [] input_pops = [] pop_dict = {} layers = ['L23'] layers = ['L23', 'L4', 'L5', 'L6'] for l in layers: i = 3 - layers.index(l) r = RectangularRegion(id=l, x=0, y=i * net.parameters['layer_height'], z=0, width=net.parameters['width'], height=net.parameters['layer_height'], depth=net.parameters['depth']) net.regions.append(r) for t in ['E', 'I']: try: import opencortex.utils.color as occ if l == 'L23': if t == 'E': color = occ.L23_PRINCIPAL_CELL if t == 'I': color = occ.L23_INTERNEURON if l == 'L4': if t == 'E': color = occ.L4_PRINCIPAL_CELL if t == 'I': color = occ.L4_INTERNEURON if l == 'L5': if t == 'E': color = occ.L5_PRINCIPAL_CELL if t == 'I': color = occ.L5_INTERNEURON if l == 'L6': if t == 'E': color = occ.L6_PRINCIPAL_CELL if t == 'I': color = occ.L6_INTERNEURON except: color = '.8 0 0' if t == 'E' else '0 0 1' pop_id = '%s_%s' % (l, t) pops.append(pop_id) ref = 'l%s%s' % (l[1:], t.lower()) exec( ref + " = Population(id=pop_id, size='int(%s*N_scaling)'%N_full[l][t], component=cell.id, properties={'color':color, 'type':t})" ) exec("%s.random_layout = RandomLayout(region = r.id)" % ref) exec("net.populations.append(%s)" % ref) exec("pop_dict['%s'] = %s" % (pop_id, ref)) if add_inputs: color = '.8 .8 .8' input_id = '%s_%s_input' % (l, t) input_pops.append(input_id) input_ref = 'l%s%s_i' % (l[1:], t.lower()) exec( input_ref + " = Population(id=input_id, size='int(%s*N_scaling)'%N_full[l][t], component=input_cell.id, properties={'color':color})" ) exec("%s.random_layout = RandomLayout(region = r.id)" % input_ref) exec("net.populations.append(%s)" % input_ref) #l23i = Population(id='L23_I', size=int(100*scale), component=cell.id, properties={'color':}) #l23ei = Population(id='L23_E_input', size=int(100*scale), component=input_cell.id) #l23ii = Population(id='L23_I_input', size=int(100*scale), component=input_cell.id) #net.populations.append(l23e) #net.populations.append(l23ei) #net.populations.append(l23i) #net.populations.append(l23ii) conn_probs = [ [0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0., 0.0076, 0.], [0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0., 0.0042, 0.], [0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.], [0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0., 0.1057, 0.], [0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.], [0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.], [0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252], [0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443] ] if add_inputs: for p in pops: proj = Projection(id='proj_input_%s' % p, presynaptic='%s_input' % p, postsynaptic=p, synapse=e_syn.id, delay=2, weight='input_weight') proj.one_to_one_connector = OneToOneConnector() net.projections.append(proj) for pre_i in range(len(pops)): for post_i in range(len(pops)): pre = pops[pre_i] post = pops[post_i] prob = conn_probs[post_i][pre_i] ####### TODO: check!!!! weight = 1 syn = e_syn if prob > 0: if 'I' in pre: weight = -1 syn = i_syn proj = Projection(id='proj_%s_%s' % (pre, post), presynaptic=pre, postsynaptic=post, synapse=syn.id, delay=1, weight=weight) proj.random_connectivity = RandomConnectivity(probability=prob) net.projections.append(proj) print(net.to_json()) new_file = net.to_json_file('%s.json' % net.id) ################################################################################ ### Build Simulation object & save as JSON recordTraces = {} recordSpikes = {} from neuromllite.utils import evaluate for p in pops: forecast_size = evaluate(pop_dict[p].size, net.parameters) recordTraces[p] = list(range(min(2, forecast_size))) recordSpikes[p] = '*' for ip in input_pops: recordSpikes[ip] = '*' sim = Simulation(id='Sim%s' % net.id, network=new_file, duration='100', dt='0.025', seed=1234, recordTraces=recordTraces, recordSpikes=recordSpikes) sim.to_json_file() return sim, net
def generate(ref="Example6_PyNN", add_inputs=True): ################################################################################ ### Build new network net = Network(id=ref, notes="Another network for PyNN - work in progress...") net.parameters = { "N_scaling": 0.005, "layer_height": 400, "width": 100, "depth": 100, "input_weight": 0.1, } cell = Cell(id="CorticalCell", pynn_cell="IF_curr_exp") cell.parameters = { "cm": 0.25, # nF "i_offset": 0.0, # nA "tau_m": 10.0, # ms "tau_refrac": 2.0, # ms "v_reset": -65.0, # mV "v_rest": -65.0, # mV "v_thresh": -50.0, # mV } net.cells.append(cell) if add_inputs: input_cell = Cell(id="InputCell", pynn_cell="SpikeSourcePoisson") input_cell.parameters = {"start": 0, "duration": 10000000000, "rate": 150} net.cells.append(input_cell) e_syn = Synapse( id="ampa", pynn_receptor_type="excitatory", pynn_synapse_type="curr_exp", parameters={"tau_syn": 0.5}, ) net.synapses.append(e_syn) i_syn = Synapse( id="gaba", pynn_receptor_type="inhibitory", pynn_synapse_type="curr_exp", parameters={"tau_syn": 0.5}, ) net.synapses.append(i_syn) N_full = { "L23": {"E": 20683, "I": 5834}, "L4": {"E": 21915, "I": 5479}, "L5": {"E": 4850, "I": 1065}, "L6": {"E": 14395, "I": 2948}, } scale = 0.1 pops = [] input_pops = [] pop_dict = {} layers = ["L23"] layers = ["L23", "L4", "L5", "L6"] for l in layers: i = 3 - layers.index(l) r = RectangularRegion( id=l, x=0, y=i * net.parameters["layer_height"], z=0, width=net.parameters["width"], height=net.parameters["layer_height"], depth=net.parameters["depth"], ) net.regions.append(r) for t in ["E", "I"]: try: import opencortex.utils.color as occ if l == "L23": if t == "E": color = occ.L23_PRINCIPAL_CELL if t == "I": color = occ.L23_INTERNEURON if l == "L4": if t == "E": color = occ.L4_PRINCIPAL_CELL if t == "I": color = occ.L4_INTERNEURON if l == "L5": if t == "E": color = occ.L5_PRINCIPAL_CELL if t == "I": color = occ.L5_INTERNEURON if l == "L6": if t == "E": color = occ.L6_PRINCIPAL_CELL if t == "I": color = occ.L6_INTERNEURON except: color = ".8 0 0" if t == "E" else "0 0 1" pop_id = "%s_%s" % (l, t) pops.append(pop_id) ref = "l%s%s" % (l[1:], t.lower()) exec( ref + " = Population(id=pop_id, size='int(%s*N_scaling)'%N_full[l][t], component=cell.id, properties={'color':color, 'type':t})" ) exec("%s.random_layout = RandomLayout(region = r.id)" % ref) exec("net.populations.append(%s)" % ref) exec("pop_dict['%s'] = %s" % (pop_id, ref)) if add_inputs: color = ".8 .8 .8" input_id = "%s_%s_input" % (l, t) input_pops.append(input_id) input_ref = "l%s%s_i" % (l[1:], t.lower()) exec( input_ref + " = Population(id=input_id, size='int(%s*N_scaling)'%N_full[l][t], component=input_cell.id, properties={'color':color})" ) exec("%s.random_layout = RandomLayout(region = r.id)" % input_ref) exec("net.populations.append(%s)" % input_ref) # l23i = Population(id='L23_I', size=int(100*scale), component=cell.id, properties={'color':}) # l23ei = Population(id='L23_E_input', size=int(100*scale), component=input_cell.id) # l23ii = Population(id='L23_I_input', size=int(100*scale), component=input_cell.id) # net.populations.append(l23e) # net.populations.append(l23ei) # net.populations.append(l23i) # net.populations.append(l23ii) conn_probs = [ [0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0.0, 0.0076, 0.0], [0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0.0, 0.0042, 0.0], [0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.0], [0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0.0, 0.1057, 0.0], [0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.0], [0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.0], [0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252], [0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443], ] if add_inputs: for p in pops: proj = Projection( id="proj_input_%s" % p, presynaptic="%s_input" % p, postsynaptic=p, synapse=e_syn.id, delay=2, weight="input_weight", ) proj.one_to_one_connector = OneToOneConnector() net.projections.append(proj) for pre_i in range(len(pops)): for post_i in range(len(pops)): pre = pops[pre_i] post = pops[post_i] prob = conn_probs[post_i][pre_i] ####### TODO: check!!!! weight = 1 syn = e_syn if prob > 0: if "I" in pre: weight = -1 syn = i_syn proj = Projection( id="proj_%s_%s" % (pre, post), presynaptic=pre, postsynaptic=post, synapse=syn.id, delay=1, weight=weight, ) proj.random_connectivity = RandomConnectivity(probability=prob) net.projections.append(proj) print(net.to_json()) new_file = net.to_json_file("%s.json" % net.id) ################################################################################ ### Build Simulation object & save as JSON record_traces = {} record_spikes = {} from neuromllite.utils import evaluate for p in pops: forecast_size = evaluate(pop_dict[p].size, net.parameters) record_traces[p] = list(range(min(2, forecast_size))) record_spikes[p] = "*" for ip in input_pops: record_spikes[ip] = "*" sim = Simulation( id="Sim%s" % net.id, network=new_file, duration="100", dt="0.025", seed=1234, record_traces=record_traces, record_spikes=record_spikes, ) sim.to_json_file() return sim, net