=Paper=
{{Paper
|id=Vol-1583/CoCoNIPS_2015_paper_8
|storemode=property
|title=Efficient Neural Computation in the Laplace Domain
|pdfUrl=https://ceur-ws.org/Vol-1583/CoCoNIPS_2015_paper_8.pdf
|volume=Vol-1583
|authors=Marc W. Howard,Karthik H. Shankar,Zoran Tiganj
|dblpUrl=https://dblp.org/rec/conf/nips/HowardST15
}}
==Efficient Neural Computation in the Laplace Domain==
https://ceur-ws.org/Vol-1583/CoCoNIPS_2015_paper_8.pdf
Efficient neural computation in the Laplace domain
Marc W. Howard, Karthik H. Shankar, and Zoran Tiganj
Department of Psychological and Brain Sciences
Boston University
{marc777,shankark,zorant}@bu.edu
Abstract
Cognitive computation ought to be fast, efficient and flexible, reusing the same
neural mechanisms to operate on many different forms of information. In order
to develop neural models for cognitive computation we need to develop neurally-
plausible implementations of fundamental operations. If the operations can be
applied across sensory modalities, this requires a common form of neural coding.
Weber-Fechner scaling is a general representational motif that is exploited by the
brain not only in vision and audition, but also for efficient representations of time,
space and numerosity. That is, for these variables, the brain appears to support
functions f (x) by placing receptors at locations xi such that xi − xi−1 ∝ xi . The
existence of a common form of neural representation suggests the possibility of
a common form of cognitive computation across information domains. Efficient
Weber-Fechner representations of time, space and number can be constructed us-
ing the Laplace transform, which can be inverted using a neurally-plausible matrix
operation. Access to the Laplace domain allows for a range of efficient compu-
tations that can be performed on Weber-Fechner scaled representations. For in-
stance, translation of a function f (x) by an amount δ to give f (x + δ) can be read-
ily accomplished in the Laplace domain. We have worked out a neurally-plausible
mapping hypothesis between translation and theta oscillations. Other operations,
such as convolution and cross-correlation are extremely efficient in the Laplace
domain, enabling the computation of addition and subtraction of neural represen-
tations. Implementation of neural circuits for these elemental computations would
allow hybrid neural-symbolic architectures that exhibit properties such as compo-
sitionality and productivity.
1 Introduction
Cognitive computation in the brain is fast, efficient and flexible. Emulating this ability would result
in extremely important technological advances. A general computational framework should be able
to operate on a wide range of content without learning each exemplar. Such a framework should
generalize across not only different specific operands but also across sensory domains, providing a
general computational language for cortical computation. Mathematical operations are an important
aspect of symbolic processing. Because of the combinatorics of these problems, learning each set
of operands and the appropriate outcome is not feasible.
This paper argues that
1. The brain represents functions of many quantities, including time, using a common form
of coding that we refer to as Weber-Fechner scaling.
2. Some of these quantities can be efficiently computed using the Laplace domain and a
neurally-plausible mechanism for approximating the inverse Laplace transform.
1
Copyright © 2015 for this paper by its authors. Copying permitted for private and academic purposes.
3. Computational operations, including translation, convolution, and an analog of cross-
correlation, can be efficiently computed in a neurally-plausible way with access to the
Laplace domain.
This suggests the hypothesis that the brain uses the Laplace domain as a common computational
currency across modalities, enabling reuse of the same neural mechanisms for flexible computations
on a range of different kinds of information.
1.1 Weber-Fechner scaling of one-dimensional functions in the brain
In this paper we restrict our attention to one-dimensional quantities defined over the positive real line
from zero (or some relatively small value) to some large (effectively unbounded) value. We argue
that the brain represents functions over variables with these properties using Weber-Fechner scaling.
If the ith receptor has a receptive fields centered at xi , then we define Weber-Fechner scaling to
mean that
1. the spacing of adjacent receptors is such that xi − xi−1 ∝ xi .
2. the width of the receptive field of the unit at xi should be proportional to xi .
∗
These two constraints imply a logarithmic scale internal scale for x, which we label x to avoid
confusion between external physical variables and internal representations. We refer to this coding
scheme as a Weber-Fechner scale because it can readily implement the behavioral Weber-Fechner
law [2].
There is good evidence that Weber-Fechner scaling is obeyed in the brain in coding extrafoveal
retinal position [7, 21]. In the case of vision, Weber-Fechner scaling can be attributed to the struc-
ture of the retinal circuitry. However, Weber-Fechner scaling appears to be more general. Neural
evidence [15] suggests that Weber-Fechner scaling applies to neural representations of numerosity.
For instance, [14] observed approximately Weber-Fechner coding for numerosity in the activity of
PFC neurons during the delay period of a working memory task. Different neurons had different
preferred numerosities. The width of the tuning curves went up linearly with the cell’s preferred
numerosity.1 Weber-Fechner scaling for numerosity cannot be attributed to a property of a physical
receptor.
Behavioral [1] and theoretical work [16] suggests that this Weber-Fechner coding scheme should ex-
tend also to functions of remembered time. Indeed, a growing body of neurophysiological evidence
suggests that representations of time also obey Weber-Fechner scaling. Figure 1 shows evidence il-
lustrating evidence suggesting that the neural representation of time may obey Weber-Fechner scal-
ing. First, the observation of time cells suggests that the brain supports functions of past time [12].
During the delay of a memory task, different time cells fire sequentially at circumscribed periods of
the delay. At any moment, observation of the set of active cells provides an estimate of the time in
the past at which the delay began. Time cells have now been observed in a variety of brain regions
with similar qualitative properties that suggest Weber-Fechner coding. It is known that the width of
the receptive fields of time cells increases with delay in the hippocampus [5, 11], medial entorhinal
cortex [10], mPFC [19] and striatum [13]. Moreover, the density of preferred times decreases with
the delay [11, 10, 13, 19]. Collaborative work to quantitatively assess Weber-Fechner coding in a
large dataset of hippocampal time cells is ongoing.
If the neural representation of time obeys Weber-Fechner scaling this is a non-trivial computational
challenge. A representation of a timeline must update itself in real time. Because the spacing
between ti and ti+1 is different than the spacing between ti+1 and ti+2 , information would have to
flow at different rates for different values of t. This seems neurally implausible. We have proposed a
solution to this challenge—updating the Laplace transform of history rather than history itself—that
we argue also lends itself readily to efficient and flexible computation.
1
Although they did not assess the spacing in a quantitative way, the number of neurons did go down with
preferred numerosity.
2
n 3.6. Figures
FI = 24 a FI = 36 b FI = 48 c FI = 60
10
10
20
20 20
20 20
20 20
firing rate (z)
20 20 20
20
40
55
40 40
40 40 40
40 40 40
40 40
Cell #
60
60
60 60
60
80
60
60 60 00
60 60 100
60
224 0 0 24
24
12 −24 -36 0
−36 0024 −36 36 36 0-48
−48
120
0
36 00−48
1 2
Time [s]
3 4
48
480
5
-60 48
−60 −6000 0 60
60
60
time relative
Figure 3.1: Temporally‐modulated tothereward
activity spans (s)
time on the treadmill.
Figure 1: A neural Weber-Fechner scaling for time? In each plot, false color firing rate maps are
Each row represents the normalized firing rate of one neuron, sorted by the
location of peak firing. Neurons were included if they were active on the
shown as a function of time for a number of extracellularly-recorded neurons. The neurons are
treadmill and had temporal information ≥ 0.2 bits/spike.
ordered according to their time of peak firing. If the neurons showed linear spacing, these plots
would appear as a straight “ridge” of constant width. To the extent the ridges are curved, that implies
a decreasing number density of neurons with preferred time of firing, consistent with Property 1. To
the extent that the ridges get wider during the delay, this implies an increase in receptive field with
preferred time of firing, consistent with Property 2. a. Neurons in medial entorhinal cortex during
running on a treadmill [10]. The duration of the delay was on average 16 s (see [10] for details). b.
Neurons in medial PFC during the delay of a temporal discrimination task [19]. c. Neurons in the
striatum during109the 60 s prior to (left) and following reward in a fixed interval procedure [13]. Note
that the ordering is such that neurons with time fields earlier in the delay are at the bottom of the
0 0.2 0.4 0.6 0.8
0 0.2
psth peak latency0.4 0.6fraction0.8figure rather than the top.
as interval
response latency
(time/FI) 2 Constructing Weber-Fechner scale functions of “hidden” variables using
the Laplace transform
We have developed a formal mechanism for efficiently representing a Weber-Fechner timeline. The
key insight is that while the timeline itself cannot be evolved self-sufficiently in time, the Laplace
transform of the timeline can be [16]. The model can be understood as a two-layer feedforward
architecture (Fig 2). At each moment a single input node f (t) projects to a set of units F (s) that
store the Laplace transform up to the current moment; s indexes the different units. Through a local
set of feed forward connections (represented by an operator L-1 k ), the second layer approximately
inverts the encoded Laplace transform to represent a fuzzy reconstruction of the actual stimulus
∗
history itself, f˜(τ ). The operator L-1
k implements the Post inversion formula keeping k terms and
can be readily implemented with simple feedforward projections [16].
This simple computational scheme for representing a Weber-Fechner timeline is sufficient to ac-
count for canonical behavioral effects in a variety of learning and memory paradigms across species
[6]; the long functional time constants necessary to encode F (s) could be computed using known
neurophysiological mechanisms [18]. This mechanism can be straightforwardly generalized to rep-
resent one-dimensional spatial position, numerosity or any other variable whose time derivative is
available at each moment [5]. More precisely, by modulating the differential equations governing
the Laplace transform by α(τ ) = dx/dt we can obtain the Laplace transform with respect to x
rather than t. This mechanism is sufficient to account for a variety of neurophysiological findings
regarding place cells and time cells in the hippocampus [5] and can be generalized to numerosity.
For instance, if we initialize a representation with f (τ = 0) set to a single delta function input, then
let it evolve with α(τ ) set to the rate of change of some variable x during an interval T , then at the
∗
end of the interval f˜(x, T ) will give a scale-invariant estimate of the net quantity x accumulated
from time 0 to time T . When α(τ ) is set to zero, the estimate of f˜ stops changing so that α can also
be used as a control signal to maintain information in working memory.
As with all path integration models, this approach is subject to cumulative error. That is if
∗
α(τ ) = dx/dt + η, the estimate of f˜(x) will grow more imprecise over time. However, note that
in the absence of noise, the “blur” in the representation of time, place, and number does not reflect
stochastic variability. Rather, the blur is more analogous to a tuning curve with non-zero width.
3
a b
Figure 2: a. Schematic of the model for encoding a temporal history f (τ ). At each time step, input from a
single node provides input to a set of nodes F (s). Each node of F is indexed by a different value of s which can
∗
be identified with the real Laplace variable. Nodes in F (s) project locally to another set of nodes in f˜(τ ) via
∗
an operator Lk . The nodes in f˜ approximate the original function f (τ ). The error in f˜(τ ) is scale invariant.
-1
∗
We choose the distribution of nodes across s and thus also τ to implement Weber-Fechner spacing (not shown).
b. Nodes in f˜ behave like neural time cells. In this plot the input f (τ ) and the activity of two nodes in F (s)
∗
with different values of s and two corresponding nodes in f˜(τ ) are shown evolving in time. Note that the units
in F (s) behave like charging and discharging capacitors with different rate constants (controlled by their value
∗
of s). The units in f˜(τ ) behave like neural time cells, responding a characteristic time after the input. The time
∗
at which each unit’s activity peaks is controlled by τ = k/s.
3 Flexible computations in the Laplace domain
If time, space, and number, as well as sensory representations share a common coding scheme, then
mechanisms for computing with representations of that form could be reused across many types
of information. Here we sketch neurally implementable mechanisms for three operations in the
Laplace domain, translation, convolution, and a close analog of cross-correlation. Of these three,
translation is the most thoroughly worked out, with a detailed mapping hypothesis onto neurophys-
iological mechanisms related to theta oscillations [17]. Translation of functions of time can be used
to anticipate the future to inform decision-making in the present; translation of functions of other
variables can be used to imagine alternative states of the world to inform decision-making in the
world in its current state. Convolution and cross-correlation can be used for the addition and sub-
traction of functions, respectively (among other uses). Because the Post inversion formula is not
well-defined for −s, we describe an analog of cross-correlation that can be implemented within the
neural framework we have developed.
3.1 A neural mechanism for translation via hippocampal theta oscillations.
Access to the Laplace domain facilitates flexible translation of one-dimensional representations. A
function f (x) can be translated to obtain f (x + δ) in the Laplace domain via a simple point-wise
multiplication with the function exp(−sδ) where s is the Laplace domain variable. This can under-
stood in the context of the two layer network as modulation of the synaptic weights in L-1
k between F
and f˜ [22]. Consideration of the computational requirements for translation in the Laplace domain
coupled with the hypothesis that hippocampal theta phase precession implements translation leads
to several results [17].
The resulting neural model accomplishes translation across scales and at the same time explains and
organizes a broad variety of neurophysiological findings related to hippocampal theta oscillations.
The hypothesis is that theta oscillations implement translation from zero to some large value within
each theta cycle. This successive translation of the present into the past enables prediction of the fu-
4
a b
Figure 3: A neurophysiological mechanism for translation of one-dimensional functions exploiting theta os-
cillations. a. A generalized circuit for computing and translating scale-invariant functions of one-dimensional
variables. α(τ ) enables the same circuit to represent functions of time or any other one-dimensional quantity
for which the time derivative is available. Thus, if f (τ ) can be rewritten as f (x (τ )) and α(τ ) = dx/dτ ,
∗
then the reconstruction is with respect to x rather than τ and we write x. δ provides a mechanism to translate
the function. b. Theta phase precession shows properties resembling translation to different future points of
the trajectory within a theta cycle. Top: neurophysiological data from [9]. Place cells from different positions
along the dorsoventral axis of the hippocampus have place cells of different size. However, cells at all scales
still precess over the same range of phases. Bottom: model predictions show the same qualitative patterns [17].
ture at successively more distant points. This model accounts for the finding that all scales (different
values of s) phase precess through the same range of local theta phases (Fig. 3). Moreover, coherent
translation requires that both past time (controlled by the values of s) and future time (controlled by
the rate at which δ changes within a theta cycle) obey Weber-Fechner scaling. Finally, the model
predicts that cells coding for predicted events should ramp up their firing from the time at which
the prediction becomes available to the time at which the predicted stimulus is obtained, phase pre-
cessing through at most one theta cycle. This prediction is analogous to findings for neurons in the
ventral striatum [20].
We found good evidence aligning translation of functions of space and time from 0 to some large
value of δ to neurophysiological findings during hippocampal theta oscillations. However, trans-
lations with other properties could be implemented during other neurophysiological events. For
instance, translation by negative values would correspond to search through memory for the past;
translation to a single non-zero value of δ (rather than sweeping through a range of values) would
facilitate retrieval of a memories at a specific range of past times [4]. In two spatial dimensions, one
can imagine a series of translations tracing out an imagined path in a navigation task [8]. In the vi-
sual modality, translation could be used to simulate planned (or imagined) eye movements or motion
of objects in the world. Although these translations could have somewhat different neurophysiolog-
ical signatures, they are all computationally related to one another. And in all cases, the translation
facilitates decision-making and behavior in the present by enabling examination of imagined states
of the world.
3.2 Arithmetic operations on functions through parallel computations
Access to the Laplace domain facilitates operations other than translation. In the same way that
point-wise multiplications in the Laplace domain can be achieved in a parallel fashion to implement
translation of any function, it is also possible to perform addition and subtraction operations on any
two functions by point-wise parallel computations with similar efficiency in the Laplace domain. For
this, we start with a definition of the operations addition and subtraction on numbers represented by
distribution functions.
Let f (x) and g(x) be functions representing two distributions of possible values for the number x
in the range 0 to xmax . Outside this range, the functions are assumed to vanish. We shall define the
operation of ‘addition’ of these two distributions to be [f + g](x) to be the convolution of the two
5
functions. Z ∞
[f + g](x) ≡ f (x0 )g(x − x0 ) dx0
0
The justification for this definition is rather straightforward. By considering the two functions to be
Dirac delta functions at two different positions, x1 and x2 , note that [f + g] is a Dirac delta function
at x1 + x2 . Moreover, the addition operation is bilinear with respect to the two functions, and hence
the above generalized definition for addition is justified. Importantly, since we have access to the
Laplace transform of the functions, namely F (s) and G(s), the addition operation can be performed
in the Laplace domain. The Laplace transform of [f + g] is simply the point wise multiplication
of F (s) and G(s), which can be computed in a parallel fashion, independently for each s value.
Finally, the L-1
k operator can be employed to invert the Laplace transform of [f + g] and obtain a
fuzzy addition operation.
It is easy to convince oneself that subtraction operation can similarly be defined to be2
Z ∞
[f − g](x) ≡ f (x0 )g(x0 + x) dx0
0
By defining a reflected function gr (x) = g(xmax − x), it can be seen that the Laplace transform of
[f − g] is simply the point wise multiplication of the Laplace transform of f (x) and gr (x). A point
of subtlety here is that for the subtraction operation, we have to consider both positive and negative
values of x although the two functions are assumed to be non vanishing only for x > 0. However,
noting that [f − g](x) = [g − f ](−x) for positive x, we can perform the subtraction operation
for negative x values also. In this entire process, only positive values of s are utilized, and hence
the inverse Laplace operator L-1 k is always well defined and the entire process can be performed in
parallel.
We have not yet carefully considered the neurophysiological substrates that could support these
arithmetic operations. However, the computational efficiency of performing these operations in the
Laplace domain is considerable. Given these considerations, it may be reasonable for the brain to
encode the Laplace transform even for variables that are provided along a Weber-Fechner scale due
to the property of the sensory receptors.
4 Discussion
We suggest that the brain uses a common form of coding, Weber-Fechner scaling, to represent
unbounded one-dimensional quantities. It can be shown that Weber-Fechner scaling is an optimal
response to signals that are long-range correlated, which are found throughout the natural world
[16]. Weber-Fechner scaling allows for representation of exponential scales with linear resources.
Representation of variables such as time, space and numerosity is greatly facilitated by access to the
Laplace transform. Many computations can be efficiently performed in the Laplace domain. For
instance, translation of representations of space and time toward the past can be used to estimate
the future. Recent work has developed a detailed mapping between a translation operator and hip-
pocampal theta oscillations. We sketched implementable operations for addition and subtraction of
functions on a Weber-Fechner scale. These operations could be used for combining functions, or for
comparing one function to another. Because the outcome of a specific operation does not need be
learned, but can be computed on-line, the existence of these operations provides an important step
towards endowing neural systems with the properties of productivity and compositionality that are
taken to be essential aspects of symbolic computation and cognition more broadly [3]. For instance,
it is clear that arithmetic obeys the properties of compositionality and productivity (modulo edge
effects). If the result of an addition operation is a function with the same neural code as the addends,
then one can in principle represent an effectively infinite number of possible problems. For instance,
given only two input functions f and g one could compute f + g, or (f + g) + g, or (f + f ) + g, etc.
There are several design considerations that are important in developing this into a general frame-
work for cognitive computation. The first consideration is whether computation for different infor-
mation should be performed in a central location, as in a von Neumann architecture or performed
2
The challenge of this approach is that the Post inversion formula does not work when the transform is
growing exponentially as with −s. If that were not the case, cross-correlation would suffice to implement
subtraction.
6
locally. The answer may depend on the form of operation. Consider Fig. 3a. Different settings for
α(τ ) and different settings for f (τ ) can give rise to a very broad range of representations, corre-
sponding to a broad taxonomy of cells in the hippocampus and related structures [5]. All of these
representations can be translated by modulating the same weights used to construct the representa-
tion (modulation by δ). Here the control signal for translation is a scalar per representation and the
output of the computation can be written to the same cells that are used to hold the representation
itself.3 This means that the cost of local implementation of translation is small per translatable func-
tion. In contrast, addition and subtraction operators require additional resources to hold the output
of the computation. The storage cost of implementing this operation locally would be relatively
substantial. Moreover, because there are many pairwise combinations of representations that might
need to be combined, there is in addition a considerable wiring cost associated with local processing.
For these reasons addition and subtraction of functions ought not to be performed locally.
Acknowledgments
We acknowledge helpful discussions with Eric Schwartz, Haim Sompolinsky, Kamal Sen, Xuexin
Wei, and Michele Rucci. This work was supported by BU’s Initiative for the Physics and Mathe-
matics of Neural Systems and AFOSR FA9550-12-1-0369.
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