from nengo.neurons import LIF, SpikingRectifiedLinear
import numpy as np
from nengo_loihi.compat import HAS_TF, tf
def discretize_tau_rc(tau_rc, dt):
"""Discretize tau_rc as per discretize_compartment.
Parameters
----------
tau_rc : float
The neuron membrane time constant.
dt : float
The simulator time step.
"""
lib = tf if HAS_TF and isinstance(tau_rc, tf.Tensor) else np
decay_rc = -lib.expm1(-dt / tau_rc)
decay_rc = lib.round(decay_rc * (2**12 - 1)) / (2**12 - 1)
return -dt / lib.log1p(-decay_rc)
def discretize_tau_ref(tau_ref, dt):
"""Discretize tau_ref as per Compartment.configure_lif.
Parameters
----------
tau_rc : float
The neuron membrane time constant.
dt : float
The simulator time step.
"""
lib = tf if HAS_TF and isinstance(tau_ref, tf.Tensor) else np
return dt * lib.round(tau_ref / dt)
def loihi_lif_rates(neuron_type, x, gain, bias, dt):
tau_ref = discretize_tau_ref(neuron_type.tau_ref, dt)
tau_rc = discretize_tau_rc(neuron_type.tau_rc, dt)
j = neuron_type.current(x, gain, bias) - 1
out = np.zeros_like(j)
period = tau_ref + tau_rc * np.log1p(1. / j[j > 0])
out[j > 0] = (neuron_type.amplitude / dt) / np.ceil(period / dt)
return out
def loihi_spikingrectifiedlinear_rates(neuron_type, x, gain, bias, dt):
j = neuron_type.current(x, gain, bias)
out = np.zeros_like(j)
period = 1. / j[j > 0]
out[j > 0] = (neuron_type.amplitude / dt) / np.ceil(period / dt)
return out
def _broadcast_rates_inputs(x, gain, bias):
x = np.array(x, dtype=float, copy=False, ndmin=1)
gain = np.array(gain, dtype=float, copy=False, ndmin=1)
bias = np.array(bias, dtype=float, copy=False, ndmin=1)
if x.ndim == 1:
x = x[:, np.newaxis] * np.ones(gain.shape[-1])
return x, gain, bias
def loihi_rates(neuron_type, x, gain, bias, dt):
x, gain, bias = _broadcast_rates_inputs(x, gain, bias)
for cls in type(neuron_type).__mro__:
if cls in loihi_rate_functions:
return loihi_rate_functions[cls](neuron_type, x, gain, bias, dt)
return neuron_type.rates(x, gain, bias)
def nengo_rates(neuron_type, x, gain, bias):
"""Call NeuronType.rates with Nengo 3.0 broadcasting rules"""
x, gain, bias = _broadcast_rates_inputs(x, gain, bias)
return neuron_type.rates(x, gain, bias)
loihi_rate_functions = {
LIF: loihi_lif_rates,
SpikingRectifiedLinear: loihi_spikingrectifiedlinear_rates,
}
[docs]class LoihiLIF(LIF):
"""Simulate LIF neurons as done by Loihi.
On Loihi, the inter-spike interval has to be an integer. This causes
aliasing the firing rates where a wide variety of inputs can produce the
same output firing rate. This class reproduces this effect, as well as
the discretization of some of the neuron parameters. It can be used in
e.g. ``nengo`` or ``nengo_dl`` to reproduce these unique Loihi effects.
Parameters
----------
nengo_dl_noise : NeuronOutputNoise
Noise added to the rate-neuron output when training with this neuron
type in ``nengo_dl``.
"""
def __init__(
self,
tau_rc=0.02,
tau_ref=0.002,
min_voltage=0,
amplitude=1,
nengo_dl_noise=None,
):
super(LoihiLIF, self).__init__(
tau_rc=tau_rc,
tau_ref=tau_ref,
min_voltage=min_voltage,
amplitude=amplitude,
)
self.nengo_dl_noise = nengo_dl_noise
@property
def _argreprs(self):
args = super(LoihiLIF, self)._argreprs
if self.nengo_dl_noise is not None:
args.append("nengo_dl_noise=%s" % self.nengo_dl_noise)
return args
[docs] def rates(self, x, gain, bias, dt=0.001):
return loihi_lif_rates(self, x, gain, bias, dt)
[docs] def step_math(self, dt, J, spiked, voltage, refractory_time):
tau_ref = discretize_tau_ref(self.tau_ref, dt)
refractory_time -= dt
delta_t = (dt - refractory_time).clip(0, dt)
voltage -= (J - voltage) * np.expm1(-delta_t / self.tau_rc)
spiked_mask = voltage > 1
spiked[:] = spiked_mask * (self.amplitude / dt)
voltage[voltage < self.min_voltage] = self.min_voltage
voltage[spiked_mask] = 0
refractory_time[spiked_mask] = tau_ref + dt
[docs]class LoihiSpikingRectifiedLinear(SpikingRectifiedLinear):
"""Simulate spiking rectified linear neurons as done by Loihi.
On Loihi, the inter-spike interval has to be an integer. This causes
aliasing in the firing rates such that a wide variety of inputs produce the
same output firing rate. This class reproduces this effect. It can be used
in e.g. ``nengo`` or ``nengo_dl`` to reproduce these unique Loihi effects.
"""
[docs] def rates(self, x, gain, bias, dt=0.001):
return loihi_spikingrectifiedlinear_rates(self, x, gain, bias, dt)
[docs] def step_math(self, dt, J, spiked, voltage):
voltage += J * dt
spiked_mask = voltage > 1
spiked[:] = spiked_mask * (self.amplitude / dt)
voltage[voltage < 0] = 0
voltage[spiked_mask] = 0
[docs]class NeuronOutputNoise:
"""Noise added to the output of a rate neuron.
Often used when training deep networks with rate neurons for final
implementation in spiking neurons to simulate the variability
caused by spiking.
"""
pass
[docs]class LowpassRCNoise(NeuronOutputNoise):
"""Noise model combining Lowpass synapse and neuron membrane filters.
Samples "noise" (i.e. variability) from a regular spike train filtered
by the following transfer function, where :math:`\\tau_{rc}` is the
membrane time constant and :math:`\\tau_s` is the synapse time constant:
.. math::
H(s) = [(\\tau_s s + 1) (\\tau_{rc} s + 1)]^{-1}
See [1]_ for background and derivations.
Attributes
----------
tau_s : float
Time constant for Lowpass synaptic filter.
References
----------
.. [1] E. Hunsberger (2018) "Spiking Deep Neural Networks: Engineered and
Biological Approaches to Object Recognition." PhD thesis. pp. 106--113.
(http://compneuro.uwaterloo.ca/publications/hunsberger2018.html)
"""
def __init__(self, tau_s):
self.tau_s = tau_s
def __repr__(self):
return "%s(tau_s=%s)" % (type(self).__name__, self.tau_s)
[docs]class AlphaRCNoise(NeuronOutputNoise):
"""Noise model combining Alpha synapse and neuron membrane filters.
Samples "noise" (i.e. variability) from a regular spike train filtered
by the following transfer function, where :math:`\\tau_{rc}` is the
membrane time constant and :math:`\\tau_s` is the synapse time constant:
.. math::
H(s) = [(\\tau_s s + 1)^2 (\\tau_{rc} s + 1)]^{-1}
See [1]_ for background and derivations.
Attributes
----------
tau_s : float
Time constant for Alpha synaptic filter.
References
----------
.. [1] E. Hunsberger (2018) "Spiking Deep Neural Networks: Engineered and
Biological Approaches to Object Recognition." PhD thesis. pp. 106--113.
(http://compneuro.uwaterloo.ca/publications/hunsberger2018.html)
"""
def __init__(self, tau_s):
self.tau_s = tau_s
def __repr__(self):
return "%s(tau_s=%s)" % (type(self).__name__, self.tau_s)
