Example of using derived classes in Brian
Using a class derived from one of Brian’s classes can be a useful way of organising code in complicated simulations. A class such as a NeuronGroup can itself create further NeuronGroup, Connection and NetworkOperation objects. In order to have these objects included in the simulation, the derived class has to include them in its contained_objects list (this tells Brian to add these to the Network when the derived class object is added to the network).
from brian import *
class PoissonDrivenGroup(NeuronGroup):
'''
This class is a group of leaky integrate-and-fire neurons driven by
external Poisson inputs. The class creates the Poisson inputs and
connects them to itself.
'''
def __init__(self, N, rate, weight):
tau = 10 * ms
eqs = '''
dV/dt = -V/tau : 1
'''
# It's essential to call the initialiser of the base class
super(PoissonDrivenGroup, self).__init__(N, eqs, reset=0, threshold=1)
self.poisson_group = PoissonGroup(N, rate)
self.conn = Connection(self.poisson_group, self, 'V')
self.conn.connect_one_to_one(weight=weight)
self.contained_objects += [self.poisson_group,
self.conn]
G = PoissonDrivenGroup(100, 100 * Hz, .3)
M = SpikeMonitor(G)
M_pg = SpikeMonitor(G.poisson_group)
trace = StateMonitor(G, 'V', record=0)
run(1 * second)
subplot(311)
raster_plot(M_pg)
title('Input spikes')
subplot(312)
raster_plot(M)
title('Output spikes')
subplot(313)
plot(trace.times, trace[0])
title('Sample trace')
show()