human
I am working on the file "Converting non-spiking neurons to spiking neurons.ipynb".
The first task is:
So that's the basic behaviour we want to emulate with a spiking neuron.  The general approach is just to round the output!  We take whatever the neuron model is doing already, but we take the output and add it into an accumulator.  Then we check if the accumulator is above 1.  If it is, we emit a spike and subtract 1 from the accumulator.  If it isn't we don't emit a spike.  Here's the result:
Can you write Python code for it?

Hereever the neuron model is doing already, but we take the output and add it into an accumulator?
assistant
#Hereever the neuron model is doing already, but we take the output and add it into an accumulator.

class LeakyIntegratorSpiking:
    def __init__(self, threshold, tau_rc=20):
        self.threshold = threshold
        self.tau_rc = tau_rc
        self.v = 0
        self.output_accumulator = 0
        
    def step(self, J):
        if self.v > self.threshold:
            output = self.v - self.threshold
        else:
            output = 0
            
        dv = -self.v + output + J
        self.v += dv / self.tau_rc
        
        self.output_accumulator += output
        output = int(self.output_accumulator)
        self.output_accumulator -= output
        
        return output
    
output = np.zeros(steps)
neuron = LeakyIntegratorSpiking(threshold=0.2, tau_rc=20)

for i in range(steps):
    output[i] = neuron.step(stim[i])
    
plt.figure(figsize=(14,4))
plt.plot(stim, label='stimulus (J)')
plt.plot(output_nonspiking, label='output (non-spiking)')
plt.plot(output, label='output (spiking)')
plt.xlabel('time steps')
plt.legend()
plt.show()