Good morning,
I’m starting to use nengo and I would like to implement a new neuron, and in particular the FitzHugh - Nagumo (FHN) model. I’ve seen and used as guideline the rectified linear example and this is very helpful, but I have some problems.
As first, I have implemented the neuron class.
class FHN(nengo.neurons.NeuronType):
#FitzHugh - Nagumo neuron model.
probeable = ('voltage', 'recovery')
alpha = NumberParam('alpha')
gamma = NumberParam('gamma')
epsilon = NumberParam('epsilon')
def __init__(self, alpha=-0.1, gamma=0.008,
epsilon=0.01):
super(FHN, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.epsilon = epsilon
@property
def _argreprs(self):
args = []
def add(attr, default):
if getattr(self, attr) != default:
args.append("%s=%s" % (attr, getattr(self, attr)))
add("alpha", -0.1)
add("gamma", 0.008)
add("epsilon", 0.01)
return args
def rates(self, x, gain, bias):
"""Estimates steady-state firing rate given gain and bias.
Uses the `.settled_firingrate` helper function.
"""
J = self.current(x, gain, bias)
voltage = np.zeros_like(J)
recovery = np.zeros_like(J)
return settled_firingrate(self.step_math, J, [voltage, recovery],
settle_time=0.001, sim_time=1.0)
def step_math(self, dt, J, voltage, recovery):
dV = (voltage * (voltage - self.alpha) * (1 - voltage) - recovery + J)
voltage[:] += dV * dt
dw = (self.epsilon * (voltage - self.gamma * recovery))
recovery[:] += dw * dt
Here I have two questions:
- due to the fact that the FHN is a normalized model, should I normalize also the voltage input
J? (I have understood, maybe in the wrong way, thatJis the voltage input in mV). - when I compile it, an error occurs in the
ratesmethod.
If I use thesettled_firingrate:
~\Anaconda3\lib\site-packages\nengo\utils\neurons.py in settled_firingrate(step_math, J, states, dt, settle_time, sim_time)
164 steps = int(settle_time / dt)
165 for _ in range(steps):
--> 166 step_math(dt, J, out, *states)
167 # Simulate for sim time, and keep track
168 steps = int(sim_time / dt)
TypeError: step_math() takes 5 positional arguments but 6 were given
I don’t understand why this happens; meanwhile, if I use the default rates method I have this error:
~\Anaconda3\lib\site-packages\nengo\neurons.py in rates(self, x, gain, bias)
166 J = self.current(x, gain, bias)
167 out = np.zeros_like(J)
--> 168 self.step_math(dt=1., J=J, output=out)
169 return out
170
TypeError: step_math() got an unexpected keyword argument 'output'
Maybe there is a mistake in the implementation of the step_math method or I don’t use properly the rates method.
The second step is to implement an Operator subclass: I’ve thought to use the default SimNeurons Operator in neurons\builder\neurons.py that is used for all the neurons’ models.
Finally, the third step is to implement a build function:
@Builder.register(FHN)
def build_fhn(model, FHN, neurons):
model.sig[neurons]['voltage'] = Signal(
np.ones(neurons.size_in) * 0.01, name="%s.voltage" % neurons) #Voltage initial condition = 0.01
model.sig[neurons]['recovery'] = Signal(
np.zeros(neurons.size_in), name="%s.recovery" % neurons) # Recovery initial condition = 0
model.add_op(SimNeurons(neurons=FHN,
J=model.sig[neurons]['in'],
output=model.sig[neurons]['out'],
states=[model.sig[neurons]['voltage'],
model.sig[neurons]['recovery']]))
If it is possible, can you help me? I can’t understand where I’m wrong.
Thank you in advance,
Emanuele