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Analysis of oscillatory weight changes from online learning with filtered spiking feedback

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Prescribed Error Sensitivity (PES) is a biologically plausible supervised learning rule that is frequently used with the Neural Engineering Framework (NEF). PES modifies the connection weights between populations of spiking neurons to minimize an error signal. Continuing the work of Voelker (2015), we solve for the dynamics of PES, while filtering the error with an arbitrary linear synapse model. For the most common case of a lowpass filter, the continuous-time weight changes are characterized by a second-order bandpass filter with frequency ω = sqrt(τ^-1 κ ||a||^2) and bandwidth Q = sqrt(τ κ ||a||^2) , where τ is the exponential time constant, κ is the learning rate, and a is the activity vector. Therefore, the error converges to zero, yet oscillates if and only if τ κ ||a||^2 > 1/4. This provides a heuristic for setting κ based on the synaptic τ , and a method for engineering remarkably accurate decaying oscillators using only a single spiking leaky integrate-and-fire neuron.
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Analysis of oscillatory weight changes from online
learning with filtered spiking feedback
Aaron R. Voelker and Chris Eliasmith
Centre for Theoretical Neuroscience technical report.
October 1, 2017
Prescribed Error Sensitivity (PES) is a biologically plausible super-
vised learning rule that is frequently used with the Neural Engineering
Framework (NEF). PES modifies the connection weights between popula-
tions of spiking neurons to minimize an error signal. Continuing the work
of Voelker (2015), we solve for the dynamics of PES, while filtering the
error with an arbitrary linear synapse model. For the most common case
of a lowpass filter, the continuous-time weight changes are characterized
by a second-order bandpass filter with frequency ω=pτ1κkak2and
bandwidth Q=pτ κkak2, where τis the exponential time constant, κis
the learning rate, and ais the activity vector. Therefore, the error con-
verges to zero, yet oscillates if and only if τκkak2>1
4. This provides a
heuristic for setting κbased on the synaptic τ, and a method for engineer-
ing remarkably accurate decaying oscillators using only a single spiking
leaky integrate-and-fire neuron.
1 Introduction
The Neural Engineering Framework (NEF; Eliasmith and Anderson, 2003) is a
method for constructing biologically plausible spiking networks. To build and
simulate such models, the Centre for Theoretical Neuroscience makes extensive
use of the open-source software, Nengo (Bekolay et al., 2014). Nengo typically
learns its connection weights offline, but also supports a number of biologically
plausible supervised and unsupervised learning rules to learn its weights on-
line. By far, the most commonly used learning rule in Nengo is the Prescribed
Error Sensitivity (PES; Bekolay et al., 2013) rule, which learns a function by
minimizing a supervised error signal from external and recurrent feedback.
Previously, Voelker (2015) fully characterized the discrete-time dynamics of
PES under the restricted setting of a constant input signal, constant reference
signal, and no noise. Due to the absence of noise, no filter was required for the
error signal. However, for spiking networks considered in practice, a lowpass is
applied to the error to filter out spike-noise (e.g., DeWolf et al., 2016; Rasmussen
et al., 2017),
In this report, we relax the assumption of a constant reference signal, and
apply an arbitrary linear filter to the error signal. For simplicity, we do so for
the case of a continuous-time simulation, but our analysis can also be applied
Figure 1: Network diagram used to analyze the PES rule. A constant in-
put xis represented by a population of nspiking neurons with the activ-
ity vector aRn. A dynamic reference signal r(t) determines the error
e(t) = ((yr)h) (t) (equation 3), which in turn drives y(t) towards r(t) by
modulating the connection weights via PES (equation 2). These learned con-
nection weights decode y(t) via the decoders d(t)Rn(equation 1). A linear
filter h(t) models the postsynaptic current induced by each spike.
to the discrete-time setting via the Z-transform. To keep our analysis tractable,
we still assume a constant input signal, and briefly discuss implications for the
general dynamic setting.
We begin by formulating a mathematical description of the network in sec-
tion 2. We present our theoretical results in section 3, and prove them in
section 4. In section 5, we validate our results with numerical simulations, and
demonstrate the utility of this analysis by engineering oscillators with prede-
termined frequencies and decay rates. Finally, we conclude in section 6 by dis-
cussing some implications of this report for learning spiking dynamical networks
2 Prescribed Error Sensitivity
Consider a network in Nengo, containing a population of nspiking neurons,
encoding the constant scalar input x. Let aRnbe the average (i.e., rate)
activity of each neuron in response to this encoding.1This vector is determined
by the first principle of the NEF, and remains fixed for constant x. The decoders
d(t)Rndetermine the scalar output y(t) via the dot-product:
y(t) = aTd(t). (1)
The PES rule learns these decoders, online, according to the following dynamics:
d(t) = κe(t)a, (2)
where κ > 0 is the learning rate,2and e(t) is the chosen error signal:3
e(t) = ((yr)h) (t), (3)
1Here on, we assume that a6=0, otherwise PES will have no effect.
2κis automatically scaled by n1in Nengo, to balance the linear scaling of kak2.
3Signs are flipped in Voelker (2015); equations 2 and 3 are consistent with Nengo.
where r(t) is the reference (i.e., ideal) output, and h(t) is some arbitrary lin-
ear filter modeling the postsynaptic current (PSC) induced by a spike arriving
at the synaptic cleft. Typically, h(t) is a first-order lowpass filter with time
constant τ > 0 (i.e., modeling an exponentially decaying PSC):
h(t) = 1
τH(s) = 1
τs + 1.4(4)
The final network is summarized in Figure 1. This also naturally extends to the
case where xand yare vectors (using a population code), but we consider the
scalar case for simplicity.
Now, we aim to characterize the dynamics of e(t) in response to the control
signal r(t). Alternatively, we could characterize the dynamics of y(t) or d(t),
but the former is easier to work with, while describing the latter via equations 1
and 2 (i.e., by integrating e(t)).
3 Results
Let φ=τκkak2. For the network described in Figure 1 and section 2, we have:
e(t) = (rf)(t), (5)
F(s) = s
sH(s)1+κkak2, (6)
hence F(s) is the transfer function from R(s) to E(s). For the case of a first-
order lowpass filter (equation 4),
=F(s) = s
ωQ s+ 1
Q=pτκkak2=pφ. (9)
Thus, F(s) is a second-order Q-bandpass filter with frequency ωin radians per
second ( ω
2πis the frequency in hertz) (Zumbahlen et al., 2011, pp. 8.9–8.10).
The poles of F(s) are:
2τ. (10)
Since φ > 0, this system is exponentially stable, and, moreover, the impulse
response, f(t), is a decaying oscillator if and only if φ > 1
4 Proof
We begin by transforming equations 1–3 into the Laplace domain:
Y(s) = aTD(s)
sD(s) = κE(s)a
E(s) = (Y(s)R(s)) H(s).5
4Capital-case variables denote the Laplace transforms of their corresponding lower-case
(time-domain) variables.
Substituting the first two equations into the last, yields:
E(s) = aTD(s)R(s)H(s)
1 + κkak2s1H(s)E(s) = R(s)H(s)
E(s) = R(s)H(s)
1 + κkak2s1H(s)
Equations 5 and 6 follow from the convolution theorem. Equations 7–9 are
verified by substituting H(s)1=τs + 1 into equation 6.
The poles of the system (equation 10) are obtained by applying the quadratic
formula to the denominator polynomial from equation 7 (τs2+s+κkak2).
Exponential stability is implied by both poles being strictly in the left half-
plane. Lastly, f(t) oscillates if and only if the poles are complex, if and only if
the discriminant (1 4φ) is negative, if and only if φ > 1
5 Validation
We construct the network from Figure 1 using Nengo 2.5.0 (Bekolay et al., 2013),
n= 1 spiking leaky integrate-and-fire neurons (mean firing rate of 262 Hz),6
τ= 0.1 s (equation 4), x= 0, and κsuch that φ > 1
4. We construct the transfer
function from equation 7 using nengolib 0.4.0 (Voelker, 2017):
im po rt nengolib
from ne ng ol ib. s ig na l import s
H = nengo li b.L ow pass( tau )
F = s / (s /H + ka pp aa. do t( a ))
where tau τis the time constant of the synapse, kappa κis the learning-
rate supplied to Nengo (divided by n), and aais the NumPy array for
the population’s activity. We evaluate (rf)(t) using F.filt(r, dt=dt), and
compare this to the e(t) obtained numerically in simulation. The filt method
automatically discretizes F(s) according to the simulation time-step (dt = 1 ms)
using zero-order hold (ZOH).7
In Figure 2, we confirm that (rf)(t) approximates the numerical e(t) given
white noise r(t). In Figure 3-Top, we exploit our knowledge of the impulse
response, f(t), to engineer a number of decaying oscillators by controlling r(t).
In Figure 3-Bottom, we evaluate np.abs(F.evaluate(freqs)) at a variety of
frequencies (freqs) to visualize the bandpass behaviour of each filter.
5This has nice form since ais a constant – otherwise multiplication of two time-varying
signals becomes a complex integral in the Laplace domain.
6Spikes are used in place of ain equations 1 and 2.
7Technically, for a discrete-time simulation, the problem and results should have been
formulated in the discrete-time domain using the Z-transform to begin with, as opposed to
discretizing at the end, but the difference is quite subtle.
Figure 2: Comparison of the analytical error (equation 7) to the numerical e(t)
obtained by simulating the network from Figure 1 (κ= 103). The control
signal r(t) is randomly sampled white noise with a cutoff frequency of 10 Hz.
The normalized root-mean-square error is approximately 3.7%.
Figure 3: Harnessing the dynamics of PES with various κto engineer decaying
oscillators with predetermined frequencies (ω) and bandwidths (Q). The time
constant of the first-order lowpass filter is fixed at τ= 0.1 s, while κis set
to achieve the desired ωvia equation 8. (Top) Once every second, r(t) is set
to a unit-area impulse. Consequently, e(t) oscillates according to the impulse
response, f(t). (Bottom) Visualizing the ideal frequency response of F(s) (equa-
tion 7). Dashed lines at ω(equation 8) align with the peak of each bandpass
filter, or equivalently the frequency of each oscillation. The width of each filter
is proportional to the decay rate Q1(equation 9).
6 Discussion
Since φ=τκkak2>1
4if and only if the weights oscillate, this motivates a simple
heuristic for setting the learning rate to prevent oscillatory weight changes: set
4τkak2, where kak2is maximal over all possible activity vectors. In this
case, equation 7 factors into a (differentiated) double-exponential:
F(s) = τ1τ2s
τ(τ1s+ 1)(τ2s+ 1) =τ1τ2τ1s1
τ1s+ 11
τ2s+ 1,
that is, two first-order lowpass filters chained together, where:
(τ1, τ2) = 2τ
by equation 10. In other words, the non-oscillatory regime (0 < φ 1
4) of
PES is characterized by the dynamics of a double-exponential. We remark that
τ1=τ2= 2τ(i.e., an alpha filter) directly on the point of bifurcation from
double-exponential to oscillatory behaviour (φ=1
4; see Figure 4).
In all applications involving online learning (that we are aware of) oscillatory
weight changes are viewed as problematic, and so the relevant constants (τ,κ,
and kak2) are tweaked until the issue disappears. In contrast, we have shown
that not only can the relationship between these constants and the oscillations
be fully understood, but they can be harnessed to engineer bandpass filters
(with respect to the transformation r(t)7→ e(t)) with specific frequencies (ω)
and bandwidths (Q). More generally, the PES learning rule can be used to
construct dynamical systems whose transfer function (equation 6) depends on
H(s), κ, and kak2. As we used only a single spiking neuron, the accuracy of
these systems rely solely on the accuracy of the PES implementation, the model
of H(s), and the constancy of (ah)(t) in practice (i.e., given spiking activity).
Although we have analyzed the continuous-time setting, the same proof tech-
nique can be applied to the discrete-time domain by use of the Z-transform.
Likewise, although we have assumed xis a constant, we can apply a “separa-
tion of timescales” argument (i.e., assuming x(t) changes on a slower timescale
than f(t)) to carry this same analysis over to dynamic x(t). By equation 10, this
analysis holds approximately for x(t) with frequencies  − 1
4πτ Hz, by applying
a time-varying filter to d(t) that depends on the current activity vector.
In conclusion, we have extended our previous analysis of PES to include
linearly filtered feedback and a dynamic reference signal. This fully characterizes
the rule in the context of NEF networks representing a constant value, as a
transfer function from the reference signal to the error signal. This transfer
function may then be readily analyzed and exploited using linear systems theory.
This demonstrates a more general principle of recurrently coupling available
dynamical primitives in biological models (here, a PSC that is integrated by an
online learning rule) to improve network-level computations.
We thank Terrence C. Stewart for inspiring this work, in part by providing his
perspective on the PES rule applied to adaptive control in Nengo (DeWolf et al.,
2016), at the 2017 Telluride Neuromorphic Cognition Engineering Workshop.
Figure 4: Visualizing the poles of F(s) (equation 10) by sweeping κ > 0 (while
kak2and τ= 0.1 s remain fixed). Arrows follow the direction of increasing κ.
When φ1
4, the dynamics of PES are a double-exponential. The learning rule
becomes an alpha filter when the two poles collide: φ=1
(marked by a solid circle). When φ > 1
4, the weight changes become oscillatory
(due to complex poles). As κincreases, the oscillatory frequency, ω, scales as
O(κ). As κdecreases, the first pole converges to s=1
τ(marked by a solid
x) while the second pole cancels the zero at s= 0.
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Technical Report
Full-text available
Prescribed Error Sensitivity (PES) is a biologically plausible supervised learning rule that is frequently used with the Neural Engineering Framework (NEF). PES modifies the connection weights between populations of neurons to minimize an external error signal. We solve the discrete dynamical system for the case of constant inputs and no noise, to show that the decoding vectors given by the NEF have a simple closed-form expression in terms of the number of simulation timesteps. Moreover, with γ = (1 − κ||a||^2) < 1, where κ is the learning rate and a is the vector of firing rates, the error at timestep k is the initial error times γ^k. Thus, γ > −1 implies exponential convergence to a unique stable solution, γ < 0 results in oscillatory weight changes, and γ ≤ −1 implies instability.
Full-text available
Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has not yet gathered significant empirical support, partly due to the technical challenge of building and simulating large-scale models with the NEF. Nengo is a software tool that can be used to build and simulate large-scale models based on the NEF; currently, it is the primary resource for both teaching how the NEF is used, and for doing research that generates specific NEF models to explain experimental data. Nengo 1.4, which was implemented in Java, was used to create Spaun, the world's largest functional brain model (Eliasmith et al., 2012). Simulating Spaun highlighted limitations in Nengo 1.4's ability to support model construction with simple syntax, to simulate large models quickly, and to collect large amounts of data for subsequent analysis. This paper describes Nengo 2.0, which is implemented in Python and overcomes these limitations. It uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results.
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Nengolib – Additional extensions and tools for modelling dynamical systems in Nengo.
  • R Aaron
  • Voelker
Aaron R. Voelker. Nengolib – Additional extensions and tools for modelling dynamical systems in Nengo., 2017. Accessed: 2017-08-12.
Nengolib -Additional extensions and tools for modelling dynamical systems in Nengo
  • Aaron R Voelker
Aaron R. Voelker. Nengolib -Additional extensions and tools for modelling dynamical systems in Nengo., 2017. Accessed: 2017-08-12.