Algorithms of whisker-mediated touch perception Miguel Maravall 1 and Mathew E Diamond 2 Comparison of the functional organization of sensory modalities can reveal the specialized mechanisms unique to each modality as well as processing algorithms that are common across modalities. Here we examine the rodent whisker system. The whisker’s mechanical properties shape the forces transmitted to specialized receptors. The sensory and motor systems are intimately interconnected, giving rise to two forms of sensation: generative and receptive. The sensory pathway is a test bed for fundamental concepts in computation and coding: hierarchical feature detection, sparseness, adaptive representations, and population coding. The central processing of signals can be considered a sequence of filters. At the level of cortex, neurons represent object features by a coordinated population code which encompasses cells with heterogeneous properties. Addresses 1 Instituto de Neurociencias de Alicante UMH-CSIC, Campus de San Juan, Apartado 18, 03550 Sant Joan d’Alacant, Spain 2 Tactile Perception and Learning Lab, International School for Advanced Studies-SISSA, Via Bonomea 265, 34136 Trieste, Italy Corresponding author: Diamond, Mathew E ([email protected]) Current Opinion in Neurobiology 2014, 25:176–186 This review comes from a themed issue on Theoretical and computational neuroscience Edited by Adrienne Fairhall and Haim Sompolinsky 0959-4388/$ – see front matter, # 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.conb.2014.01.014 Introduction In the process that culminates in sensing and identifying an object, the starting point is the encoding of physical parameters by sensory receptors. A growing set of inves- tigations focuses on transformations along sensory path- ways as a means to understand the conversion from raw physical signals into sensations and percepts. A long- standing hypothesis is that those transformations are built up from a set of standard ‘canonical’ computations, imple- mented repeatedly [1] and combined to generate responses that are selective, specific and flexible [2]. Taking this hypothesis as a point of departure, this review aims to identify canonical computations, or algorithms, implemented in the rodent whisker system, an ‘expert’ system [3]. We focus on computations along the receptor-to-cortex ascending pathway; nevertheless a complete picture of tactile sensation will only be achieved by understanding how sensory and motor computations are woven together [4,5]. Mechanical forces in the follicle As in any sensory pathway, transduction from physical entities into action potentials constrains all later proces- sing. Input signals — fluctuations in mechanical energy at the whisker base — are shaped by the interaction be- tween the whisker’s motion, its mechanical properties (e.g., compliance) and properties of the contacted object. The whisker-follicle junction is rigid, allowing robust transmission and readout of the forces induced by whisker motion [6 ]. The form of whiskers (Fig. 1a) determines their mech- anical behavior. Bending stiffness decreases from whisker base to tip due to taper [7 ], and the concomitant increase in flexibility enables the slippage of whiskers during object exploration [8,9 ]. Additional flexibility is achieved by their hollow structure [10]. New methods for tracking whisker motion have allowed detailed analysis of how whiskers interact with objects [11,12]. The combination of whisker measurements with models of whisker deflection has begun to specify bend- ing [9 ,13] and changes in forces at the whisker base [6 ,7 ,9 ,14]. Contact-induced whisker deformations can be decomposed into a slow bending component and a transient vibrational component [15]; the relative contributions of different components depend on the specific interaction [6 ,7 ,16]. Algorithms involving comparison of components require those components to be effectively transduced by mechanoreceptors. When whiskers sweep across a textured surface (Fig. 1b), they are trapped and released by surface ridges and grains [17–19,20 ] (reviewed in [21]). These brief (2 ms) ‘stick–slip’ events cause transient, high-frequency vibrations [18]. The consequent sequence of fluctuations in mechanical energy provides a signature of texture [17,18]. Stick–slip events excite primary sensory neurons and their targets [17,18,20 ,22]. However, differences in ‘stick–slip’ events across trials are not well-correlated with trial-to-trial choices in a texture discrimination task [20 ]; other features of whisker motion may also con- tribute. Transduction of touch into neuronal signals Transduction is carried out at the terminals of neurons whose cell body resides in the trigeminal ganglion (TG; Fig. 1c). The many mechanoreceptor types, distributed Available online at www.sciencedirect.com ScienceDirect Current Opinion in Neurobiology 2014, 25:176–186 www.sciencedirect.com
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Algorithms of whisker-mediated touch perceptionMiguel Maravall1 and Mathew E Diamond2
Available online at www.sciencedirect.com
ScienceDirect
Comparison of the functional organization of sensory
modalities can reveal the specialized mechanisms unique to
each modality as well as processing algorithms that are
common across modalities. Here we examine the rodent
whisker system. The whisker’s mechanical properties shape
the forces transmitted to specialized receptors. The sensory
and motor systems are intimately interconnected, giving rise to
two forms of sensation: generative and receptive. The sensory
pathway is a test bed for fundamental concepts in computation
and coding: hierarchical feature detection, sparseness,
adaptive representations, and population coding. The central
processing of signals can be considered a sequence of filters.
At the level of cortex, neurons represent object features by a
coordinated population code which encompasses cells with
heterogeneous properties.
Addresses1 Instituto de Neurociencias de Alicante UMH-CSIC, Campus de San
Juan, Apartado 18, 03550 Sant Joan d’Alacant, Spain2 Tactile Perception and Learning Lab, International School for Advanced
Studies-SISSA, Via Bonomea 265, 34136 Trieste, Italy
Algorithms of touch perception Maravall and Diamond 177
Figure 1
(a) FF
LX
C
θ
T
(b)
(c)
BC
Cortex
VPMPOm
TG
TN
brain stemfollicle
whisker
Current Opinion in Neurobiology
Input forces to the sensory system and the ascending pathway. (a) The force acting upon a whisker during contact, and thus transmitted to the
receptors in the follicle, is illustrated. The object at position X strikes a whisker of length L at a distance C from the skin and at angle u away from the
whisker’s resting angle, inducing a force F. (b) Illustration of a single large stick–slip event. One frame from a high-speed (1000 frames/s) video is
shown in gray scale. The whisker traces have been enhanced to increase their visibility. While the rat palpated the surface to judge the groove spatial
frequency, one whisker was tracked through a sequence of frames and the traces, from violet to light blue, show the whisker position over 1 ms
timesteps. The whisker tip was blocked in a groove and then sprung free as the rat retracted the whisker shaft in the posterior direction. (c) Principal
sensory pathways to the cortex are illustrated schematically. TG neurons send a peripheral branch to the skin and a central branch into the trigeminal
nuclei (TN) of the brainstem. Axons from TN cross the midline to reach the thalamus, terminating in VPM and the posterior medial nucleus, POm.
Thalamic neurons project to BC. Blue, red, and green lines represent parallel pathways that carry different sorts of tactile information, as reviewed
elsewhere. (a) Adapted personal communication from A. Hires and K. Svoboda; (b) adapted from [20��]; (c) adapted from [5].
differentially across the follicle, have diverse response
properties and are best activated by distinct forces with
particular time courses. Given the diversity of receptor
types and spatial distributions, it is not surprising that
both of the principal functional classes of primary afferent
neurons, slowly and rapidly adapting (SA and RA), in fact
comprise a rich variety of feature combinations. Thus,
each neuron displays distinct sensitivity to the location,
direction and velocity of whisker displacements evoking
lateral forces [17,23–26], to the pattern of axial forces [25],
to whisking phase [27–29] and to contact, detachment or
their combinations [24,27].
As a population, TG neurons represent the space of
dynamical features of one whisker through a high-dimen-
sional code (�200, counting each neuron as a dimension)
(see Section ‘Feature selectivity’) [30��]. This permits
rapid information encoding: specific patterns of forces
(e.g., [7��]) may engage subsets of neurons to ‘label’ the
stimulus. Whisker motion patterns are richly represented
by the TG population, allowing several population-based
decoding schemes for any task. For example, information
present across the population probably permits instan-
taneous comparison of the relative magnitudes of
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different force components (see Section ‘Mechanical
forces in the follicle’). Intriguingly, each TG neuron
projects to multiple target neurons within a column of
the principal trigeminal nucleus (‘barrelette’), and indi-
vidual barrelette neurons receive convergent inputs of
different afferent types (SA, RA): thus, TG population
signals are decoded in a ‘one-to-many’ and ‘many-to-one’
manner [31�].
A requirement for TG to implement a fast population
code based on relatively small numbers of spikes is that
spike generation be precise. Indeed, TG neurons respond
with highly reliable firing patterns [17,23,32,33] and are
among the most temporally precise neurons yet discov-
ered in the animal kingdom [17,30��,32]. The minimal
sequential pathway from receptors to cortex (Fig. 1c)
requires just three synapses — very short compared to
other sensory systems.
To summarize, TG neurons convey information pack-
aged in a high-temporal precision code where different
neurons encode diverse physical properties. The speed of
peripheral encoding is reflected in the finding that cortical
neurons carry texture information within 20 ms after
Current Opinion in Neurobiology 2014, 25:176–186
178 Theoretical and computational neuroscience
Box 1 Self-generated motion and modes of sensing.
Touch entails putting sensors into contact with an object to
determine its identity, properties or location. Accordingly, the
intricate loops connecting ‘sensory’ and ‘motor’ circuits can be
understood in terms of the need to meld sensory and motor
information — motor output generates sensory input, sensory input
modulates motor output [5,37,38,39,40,41�].
Whisker-mediated sensation occurs through two modes of opera-
tion. In the generative mode (Fig. 2a), the animal moves its whiskers
to actively seek contact with objects and palpate them: the animal
causes the percept by its own motion [42]. Tasks involving the
generative mode include wall following [43], gap measurement [44],
texture discrimination [19,20��,21,35,45], and object localization
[46,47]. In the generative mode, whisker-mediated perception has
been put forward as a process where sensory and motor systems
dynamically converge, through repeated contacts, until both
neuronal representations reach a stable state [46,48].
In the receptive mode (Fig. 2b), the animal places its whiskers on an
object and is frequently observed to immobilize its whiskers to
optimize signal collection. Tasks include detection and discrimina-
tion of vibrations applied to whiskers [44,49–54] and discrimination of
the width of an aperture [55].
whisker contact [34,35]. In the rest of this review, we
highlight emerging themes concerning the transform-
ation of signals from TG to cortex.
Active sensingActive sensing systems are purposive and information-
seeking [36]: active sensing entails control of the sensor
apparatus in whatever manner best suits the task.
Although the concept of sensor apparatus control applies
to all modalities, it is perhaps most evident in the
modality of touch. Recent evidence (Box 1) indicates
Figure 2
Generative Sensing (a)
object motionless
head andwhiskersin motion
Two modes in which rats collect tactile information. Both panels are single
been enhanced to increase their visibility. (a) During generative sensing, the
the whisker tips and the object. This mode of sensing is critical when the obj
motor system. The image is taken as the rat judges the spatial frequency of
provides mechanical energy. Since the rat’s percept could be confounded b
image is taken as the rat perceives the vibration of the flat plate. (a) Adapte
Current Opinion in Neurobiology 2014, 25:176–186
that rats and mice select motor programs to interact with
objects according to the type of information to be
acquired — rodents can either generate sensations or
receive sensations.
Feature selectivityNeurons early in sensory processing are sensitive to the
presence of particular features in a stimulus. Feature
selectivity can be approximated by treating the neuron
as a device that (1) linearly filters its input and (2)
responds by applying a nonlinear threshold function to
the filtered stimulus [56]. Linear filtering is thus a candi-
date canonical algorithm [1].
Variants and generalizations of linear-nonlinear
models predict trains of action potentials in TG
[17,23,30��,57,58], the ventral posterior medial thalamic
nucleus (VPM) [57,59] and the barrel cortex (BC) [60,61��].Neurons are selective to temporal features of whisker
motion (Fig 3). For subcortical neurons, filtering is typically
simple in that a single, short-duration feature (e.g., instan-
taneous velocity) predicts a neuron’s response [30��,59].
These features are conserved from TG [30��] to VPM [59]
(Fig. 3b and c), though VPM processing differs in other
important aspects (discussed below). In contrast, BC
neurons are sensitive to temporal features (Fig. 3d and
e), but have more complex, high-dimensional selectivity:
multiple features, sometimes in combination, are necess-
ary to explain a neuron’s firing [60,61��]. Cortical neurons
respond to nonlinear dynamical features such as speed (the
absolute value of velocity) [17,60,61��] and are sensitive to
correlated motion between whiskers [61��] (Fig. 3f). Sub-
cortical tuning curves indicate faithful representation of
filtered stimulus magnitude, while cortical curves indicate
Receptive Sensing (b)
object inmotion
head and whiskersmotionless
Current Opinion in Neurobiology
frames from high-speed (1000 frames/s) video; the whisker traces have
rat moves its head and whiskers to create dynamic interaction between
ect is immobile and the mechanical energy must originate in the animal’s
grooves on the object surface. (b) During receptive sensing, the object
y its own motor output, the head and whiskers remain motionless. The
d from [20��]; (b) adapted from [129].
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Algorithms of touch perception Maravall and Diamond 179
Figure 3
(a) Ideal filters
position
Time (ms)
-40 10 10 10
Time (ms)
-40
Time (ms)
-40
Time (ms)
Neuron 1 Neuron 2 Neuron 3
-40 10 10 10
Time (ms)
-40
Time (ms)
-40
Time (ms)
Neuron 1 Neuron 2
Neuron 1
Filter 1 Filter 1
Pha
se (
rad)
Filter 2 Filter 2
Common input
Independent inputs “local”neurons
“global” neurons
Response to uncorrelated stimulation (z score)
Cel
l cou
nt
Neuron 2
Neuron 3
-40 10 10 10
Time (ms)
-40
Time (ms)
-40 10 10
0
-
2
π
π
πTime (ms)
-40
Time (ms)
-40 10 10
0
Time (ms)
Time (ms)
-40
40
1
2
24
c1-c + ×
20
01 10 100
-40
Time (ms)
-40
velocity acceleration
(b) Trigeminal ganglion
(c) VPM thalamic nucleus
(d) Barrel cortex (e) Cortical population filters
(f) Cortical selectivity for correlated vs uncorrelated stimulation
2π
Current Opinion in Neurobiology
Feature selectivity. (a) ‘Ideal’ filters for extraction of position, velocity and acceleration values. Note similarity between these waveforms and those in
the remaining panels. (b) Examples of filters for TG neurons. Each neuron extracts a distinct, rapid feature. (c) Examples for VPM neurons. (d) BC
neurons are sensitive to multiple features in combination. Features are of longer duration than those comprising subcortical filters, signifying longer
temporal integration. (e) Dataset of linear filters for barrel cortex neurons sensitive to single-whisker stimulation, sorted by the relative phase of the filter
waveform. (f) Distribution of barrel cortex sensitivity to stimuli with different degrees of interwhisker correlation. (a) and (c) Adapted from [59]; (b) from
[30��]; (d–f), from [61��].
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180 Theoretical and computational neuroscience
Figure 4
(a) “L6”“L5”“L4”“L2/3”
60100
10-1
10-2
0.05
10-3
10-4
50
40
30
Ove
rall
spik
e ra
te (
Hz)
20
10
00 1 2 3 4 5 6 7 8 9418 588
Depth (μm) Session ranked by discrimination
Dis
crim
inat
ion
p va
lue
890 1154
(b)
Current Opinion in Neurobiology
Firing rate heterogeneity and population coding in BC. (a) Spike rates during pole localization in head-fixed mice. Each circle is one neuron and colored
bars indicate laminar boundaries. Median activity level differs across layers, yet within each layer there is marked variability. (b) Single-neuron and
population discrimination performance for 9 experimental sessions of a texture discrimination task in freely moving rats. Gray squares correspond to
individual neuronal clusters (single-unit or multi-unit); black circles, to the population of simultaneously recorded clusters (3–7 clusters per population).
p value is the probability that the discrimination performance of a neuron or population arose by chance; horizontal gray line, p = 0.05. Individual
neuronal clusters are variable and often perform below chance ( p > 0.05). However, every population performs well above chance. (a) Adapted from
[68��]; (b) from [78��].
the detection of events that exceed background by some
proportion [57,59,60]. Until there is evidence that can be
explained only by alternative models, our view is that the
high-dimensional selectivity of cortical neurons is, to a first
approximation, the result of convergence of neurons with
simpler properties.
Early studies described nonlinear integration of the
motion of multiple whiskers in cortex (reviewed in
[62]); such findings were given a new framework by
recent research on multiwhisker correlations [61��]. Mul-
tiwhisker selectivity allows neurons to construct ‘appar-
ent motion’ from sequential deflection of adjacent
whiskers [63]. Interestingly, a neuron’s directional sensi-
tivity to apparent motion is not predicted by its direc-
tional sensitivity to motion of its principal whisker.
Neurons in VPM can also display selectivity to apparent
global motion [64], although more weakly than in the
cortex [64,65].
Most studies of feature selectivity have relied on deliver-
ing controlled whisker stimulation to anesthetized
animals; as yet, no study has directly compared tuning
properties under anesthesia with response properties of
the same cells during behavior. However, feature selec-
tivity properties have strong parallels in animals perform-
ing discrimination tasks. Whisker contact modulates the
firing rate of BC neurons in behaving animals
[18,35,66,67,68��,69�] (S.A. Hires et al., abstract in SocNeurosci Abstr 2012, 677.18): responses correlate with
Current Opinion in Neurobiology 2014, 25:176–186
features including force magnitude, frequency of stick–slip events and maximum curvature.
SparsificationIn the whisker pathway, activity levels change system-
atically from stage to stage, a change that can be quanti-
fied as ‘sparseness’ — the fraction of neurons that are
active and encoding information at any moment
[68��,70,71,72] (Fig. 4a). The computational advantages
of sparse activity have been reviewed elsewhere [72–75].
Sparseness can reduce overlap (correlation) between
activity patterns, facilitate learning, discrimination and
categorization, and limit energy expenditure. A repres-
entation where individual neurons are selectively sensi-
tive to high-level features (Section ‘Feature selectivity’)
is a step toward the ‘concept’ representations found at the
final stage of cortical processing [2,76].
In whisker-mediated touch perception, sparseness
increases at more central stations of the sensory pathway,
peaking in layers 2/3 of BC (reviewed in [72]) (Fig. 4a).
For example, in behaving animals, stick–slip events
during texture exploration elicit low-probability, pre-
ing input from distinct subgroups of BC neurons can each
receive a robust message. Moreover, population repres-
entations of task parameters remain stable even when
individual neurons are plastic [40].
In sum, downstream neurons can extract texture identity
through a simple decoding scheme involving linear
synaptic weighting [78��]. The robustness of linear
decoding schemes applies across cortical areas and is a
candidate for a fundamental algorithm of cortical com-
putation [125–127].
ConclusionsThis review documents recent progress in understanding
the algorithms by which whisker shape and motion are
translated into patterns of neuronal activity distributed
across networks. We conclude by highlighting experimen-
tal paradigms that offer the opportunity to attack many
remaining questions. How does the feature detection
framework translate to the behaving sensory system?
Can sensory neurons integrate ‘local’ features over time
to generate a ‘global’ stimulus representation? Can such a
global representation be transferred to other brain regions
for storage and manipulation? In a delayed response task,
the mouse must use generative sensing to localize an
object, but delay its choice for a go cue. The flow of activity
from sensation to action has been mapped [128�]. In
another task, rats must use receptive sensing to perform
a tactile delayed comparison task [129��]. The rat receives
two stimuli, ‘base’ and ‘comparison,’ separated by a variable
delay. Each stimulus is a vibration, generated as a series of
velocity values sampled from a normal distribution. The rat
must judge which stimulus has greater velocity standard
deviation. The stimulus is exactly the sort of ‘noise’ used to
map neuronal feature selectivity through reverse corre-
lation methods: the design allows the investigator to
examine the algorithms of whisker-mediated perception
that underlie behavioral performance.
AcknowledgementsWe are grateful to Stuart Ingham for help with the artwork in Fig. 2 and toMichael Bale for comments on the manuscript. We apologize to our
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colleagues whose work could not be cited in entirety due to spaceconstraints. Funding was from the Spanish Ministry of Economy andCompetitiveness grant BFU2011-23049 (co-funded by the European Fundfor Regional Development); the Valencia Regional Government grantPROMETEO/2011/086; the Human Frontier Science Program grantNeuroscience of Knowledge (RG0015/2013); the European ResearchCouncil Advanced grant CONCEPT (294498); the European Union FETgrant CORONET (269459); the Italian Ministry for Universities andResearch grant HANDBOT, and the Compagnia San Paolo.
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Fassihi A, Akrami A, Esmaeili V, Diamond ME: Tactile perceptionand working memory in rats and humans. Proc Natl Acad Sci US A 2014 http://dx.doi.org/10.1073/pnas.1315171111. publishedahead of print January 21, 2014.
Using their whiskers to receive vibrations, rats show sensory acuity andworking memory capacities that rival those of human subjects using theirfingertips. The study will allow the study of neuronal mechanisms under-lying a new form of tactile perception in rats.