algorithms of whisker-mediated touch perception -...

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

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

IntroductionIn 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 follicleAs 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 signalsTransduction is carried out at the terminals of neurons

whose cell body resides in the trigeminal ganglion (TG;

Fig. 1c). The many mechanoreceptor types, distributed

www.sciencedirect.com

[email protected]

http://dx.doi.org/10.1016/j.conb.2014.01.014

http://www.sciencedirect.com/science/journal/09594388

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


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


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


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


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].



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


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��].

www.sciencedirect.com Current Opinion in Neurobiology 2014, 25:176–186


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)


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


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-

cisely timed cortical responses [18]. Mechanisms under-

lying sparsification are being examined [77].

Response heterogeneityA striking finding is that neurons responsive to the same

whisker diverge markedly in their responses to a given

peripheral event. Neurons within the same column can



participate strongly, or not at all, in the encoding of

objects contacted by the corresponding whisker

[18,35,68��,69�,78��]. This heterogeneity occurs from

TG to all layers of BC, with two important properties.

First, neurons within each circuit are tuned to diverse

stimulus properties. Second, neurons have diverse

activity levels.

Diverse tuning properties

Tuning to stimulus features is heterogeneous at every

stage where it has been assessed (Section ‘Feature selec-

tivity’) [30��,59,61��] (Fig. 3). Since the preferred

stimulus features of neurons in VPM are similar to those

in TG, heterogeneity is partly inherited from stage to

stage [30��,59]. Higher-level features in cortex are also

heterogeneous across neurons, providing a rich repres-

entation of stimuli [61��]. Neurons in animals performing

sensory discrimination tasks also display diverse selectiv-

ity for sensory features, as do fibers projecting into BC

from motor cortex [41�,69�,79] (S.A. Hires et al., abstract

in Soc Neurosci Abstr 2012, 677.18). Neurons with different

feature selectivity are spatially intermingled within each

barrel column [80–82].

Diverse activity levels

Although mean activity levels differ systematically from

stage to stage (Section ‘Sparsification’), within each stage

there is a wide variability in activity and responsiveness

among individual neurons [72] (Fig. 4a). For example, in

the supragranular layers, where activity is sparsest, studies

under a range of conditions have nevertheless found a

subset of highly active neurons numbering �20–30% of

the population [68��,69�,81,82,83�,84].

The fraction of responsive neurons in a particular con-

dition could reflect selectivity to the stimuli presented. If

so, sparse, heterogeneous responses would reflect the

failure of stimuli to engage most neurons. However,

the finding that many neurons exhibit low firing rate

under a variety of behaviors and forms of stimulation

[18,68��,69�,84,85], even when stimulation systematically

explores a large region of parameter space [61��], suggests

that sparse firing is more a property of neurons than an

outcome of inadequate stimulus sets.

Responsiveness correlates with spontaneous activity

levels [84,85]. Further, the relative activity level of

strongly responding BC neurons is stable [84,85], despite

changes in tuning properties over time [84]. Thus, some

neurons appear intrinsically more active irrespective of

their specific tuning. At present it is unclear whether the

more active neurons constitute a separate category or one

extreme of a continuous distribution. In layer 2/3, more

active neurons receive more excitation and less inhibition

[83�,85]. Some of the heterogeneity in response proper-

ties correlates with differences in neuronal projection

pattern: layer 2/3 cells that project to motor cortex have


different excitability than those projecting to secondary

somatosensory cortex [79] and respond differently during

tactile tasks [69�].

Spiking precision, timing-basedrepresentations, and synchronyAs described above, TG responses display reliable and

precise timing. Timing precision remains high across the

pathway: typical response ‘jitter’ (spike time standard

deviation across trials) is �0.2 ms in TG, �0.4 ms in

VPM and �1–2 ms in BC [17,30��,86].

The pressure driving temporal precision is unlikely to be

a need to limit variation in absolute timing, because jitter

is orders of magnitude smaller than the trial-to-trial

variability observed in naturalistic exploratory behavior

[3,17,20��]. The requirement, instead, is for sharp relativetiming, enabling precise temporal patterning of spikes

across neurons — either to permit sensory responses to be

referred to the animal’s own whisking motion, or to

facilitate activity propagation [87].

A sensory whisking signal is found in at least a subset of

TG neurons (Section ‘Transduction of touch into

neuronal signals’). Information about both whisking

amplitude and midpoint is relayed to BC

[40,41�,88,89]. BC neurons can show a small modulation

of membrane potential by whisking phase [66,79,83�,90],

allowing contact responses to vary with phase. The

whisking phase signal combined with the amplitude

and midpoint references could enable reconstruction of

whisker position and of the location of objects relative to

the animal’s face [39,67,91].

Sensory codes based on temporally patterned spikes can

convey more information than spike count-based codes

[92,93]. In BC, latency differences across populations can

code for whisker identity [94,95]. Spike patterns both in

TG and in BC can discriminate texture in a manner

decodable by an ideal observer [34].

Decoding spike timing information requires a temporal

reference. When the system is in generative mode, the

reference can be the whisking signal, as above. A refer-

ence also present in receptive mode, or when a stimulus

appears unpredictably, is the stimulus-evoked population

activity itself, both in the form of collective activity

oscillations and of synchronous spiking across subsets

of neurons [50,92,93,96]. Which neuronal subsets become

synchronously coactive in, e.g., VPM, depends on the

particular stimulus [97,98] (M. Bale et al., abstract in SocNeurosci Abstr 2011, 704.14). Therefore, the identity of the

synchronous subset can convey robust stimulus infor-

mation [99,100]. Dynamic changes in synchrony across

subpopulations of neurons can probably be decoded

downstream, as neurons are sensitive detectors of small



changes in synchrony across an afferent population

[100,101].

Synchronous activation allows activity to propagate reliably

in the thalamocortical pathway, where individual synaptic

connections are weak on average [87]. However, full,

precise synchrony across a large part of the presynaptic

population is unlikely to be required, as some thalamocor-

tical and intracortical connections can be effective enough

to help shape postsynaptic activation [102–104]. Instead,

activity propagation may occur via dynamically changing

partial synchrony across subsets of neurons. This enables

both robust activity propagation and rich population

representations, involving combinations of neurons that

become coactive based on shared feature selectivity

[99,100]. Indeed, selective, sparse synchronous activation

of subsets of cells is a hallmark of cortical responses during

texture exploration in awake rats [18].

Spike timing information and behavior

Is the information in spike timing used during behavior? In

a radial distance discrimination task, precise (ms) timing of

contacts within the whisking cycle was not crucial, because

uncertainty in contact time introduced by whisker azi-

muthal jitter had no effect on performance [7��]. In a pole

localization task, mice detected whether the pole fell in the

target region by maintaining their whiskers approximately

within the region and waiting for the object to strike the

whisker [47]. This maximized the difference in number of

contacts and in spike counts between positive and negative

trials [68��]. Consistent with use of a spike-count but not a

timing code, optogenetic stimulation in layer 4 increased

count and fooled animals into detecting a ‘phantom pole’;

jittering stimulation timing within a whisk caused no

appreciable effect [105�].

In texture discrimination tasks, as discussed above,

sequences of ms-scale fluctuations in whisker motion

differ according to texture, and neurons can carry texture

information by sequences of spikes [18,34]. Further data

from behaving rats are required to evaluate spike timing

as a candidate code for texture [21,106]. Future work on

the relevance of spike timing may test how animals solve

whisker-mediated tasks that require a timing-based

strategy. However, even more powerful would be the

demonstration that a task potentially solvable with a non-

timing scheme is actually solved using spike timing. This

test could be accomplished, for instance, if a behavioral

deficit were induced by jittering spike timing for two

stimuli that evoke different spike counts.

Response dynamics and adaptationAdaptation, the accommodation of response levels to

ongoing or repeated stimulation, occurs across species

and sensory modalities; the consequent change in

neuronal sensitivity is an instance of a canonical compu-

tation [1,107]. Responses evolve over time because of


variation in how the sensors encounter the object and

because of dynamics inherent to neuronal responses.

In one adaptation paradigm, repeated whisker stimulation

decreases responses to successive deflections (reviewed

in [62]), sharpening tuning properties of BC neurons,

including selectivity to whisker identity [108] and direc-

tion of motion [98,109]. This adaptation enhances dis-

crimination between stimuli of different amplitudes, at

the expense of detectability [110]. An enhanced ability of

neuronal responses to discriminate different stimulus

magnitudes is also found during adaptation to sinusoidal

whisker stimulation, suggesting a common functional

effect [111,112]. Intriguingly, regular-spiking (putative

excitatory) cortical neurons adapt more weakly to stimuli

repeated at irregular intervals than at a constant rate [113],

while putative inhibitory neurons do the opposite [114].

This behavior is echoed in the finding that human sub-

jects experience an increase in the perceived intensity of

a vibration when noise is introduced [114].

In another paradigm, BC neurons modify their firing rate

and sensitivity according to the ongoing statistical distri-

bution of stimuli: changes in the overall scale (variance or

‘contrast’) of a stimulus distribution elicit compensatory

adjustments in neuronal sensitivity (gain), thus maintain-

ing the information conveyed about the stimulus [60,115].

TG and VPM neurons also display adaptation and gain

adjustment under changes in variance [57]. Individual

neurons at each subcortical stage display variable adap-

tive behavior, ranging from fixed gain to full gain rescaling

[57]. This diversity may enable subcortical populations to

preserve representation of both relative and absolute

magnitudes. Because responses to dynamic stimuli adapt

over multiple timescales, adaptation allows neurons to

encode components of whisker stimuli with different

time courses (e.g., fast, high-frequency vibrations or slow

modulations in whisking waveform) [116].

Different forms of stimulation affect distinct biophysical

mechanisms, with overlapping roles. Adaptation to

repeated stimulation relies mostly on synaptic mechan-

isms, including short-term depression [117]. The under-

lying changes are complex and sensitive to specific

stimulation parameters: e.g., small-amplitude stimuli

sometimes elicit stronger adaptation than large-ampli-

tude stimuli [118]. Adaptation to noise stimulus statistics

probably recruits intrinsic membrane mechanisms

[119,120]. In summary, adaptation in the whisker system

has the effect of refining stimulus representations in a

context-dependent manner.

Coordination in BC populationsThe heterogeneity of functional properties across

neurons raises the issue of how populations collectively

represent information. Is activity coordinated? How many

neurons must an ‘observer’ (experimenter or downstream



cell) sample to extract a robust sensory message? Is it

necessary to ‘listen’ to the ‘best’ neurons? In rats perform-

ing a texture discrimination task, some neurons, taken

alone, nearly match the animal’s performance, while

others carry no readily apparent texture signal [35]. Ran-

domly sampled clusters comprised of as few as 3–7

neurons can be linearly combined to robustly discriminate

texture even in cases where no individual neuron

represents the stimuli [78��] (Fig. 4b). The collective

signals arise from synergistic interactions between hetero-

geneous neurons, confirming long-standing theoretical

proposals [121–124]. Population representations of sen-

sory and motor parameters in motor cortex during a pole

localization task saturate for comparable-sized groups

(�2–6 neurons) [40]. Thus, downstream neurons receiv-

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


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.

References and recommended readingPapers of particular interest, published within the period of review,have been highlighted as:

� of special interest�� of outstanding interest

1. Carandini M: From circuits to behavior: a bridge too far? NatNeurosci 2012, 15:507-509.

2. DiCarlo JJ, Zoccolan D, Rust NC: How does the brain solvevisual object recognition? Neuron 2012, 73:415-434.

3. Diamond ME, Arabzadeh E: Whisker sensory system — fromreceptor to decision. Prog Neurobiol 2013, 103:28-40.

4. Kleinfeld D, Ahissar E, Diamond ME: Active sensation: insightsfrom the rodent vibrissa sensorimotor system. Curr OpinNeurobiol 2006, 16:435-444.

5. Diamond ME, von Heimendahl M, Knutsen PM, Kleinfeld D,Ahissar E: ‘Where’ and ‘what’ in the whisker sensorimotorsystem. Nat Rev Neurosci 2008, 9:601-612.

6.��

Bagdasarian K, Szwed M, Knutsen PM, Deutsch D, Derdikman D,Pietr M, Simony E, Ahissar E: Pre-neuronal morphologicalprocessing of object location by individual whiskers. NatNeurosci 2013, 16:622-631.

A systematic examination of the transformation of object contact intowhisker shape. By high-speed videography and tracking, the authorsfound that angle and curvature can represent the coordinates of objectposition after contact in a reliable manner. Moreover, the follicle-whiskerjunction was found to be rigid, which enables direct readout of whiskermorphological coding by mechanoreceptors.

7.��

Pammer L, O’Connor DH, Hires SA, Clack NG, Huber D,Myers EW, Svoboda K: The mechanical variables underlyingobject localization along the axis of the whisker. J Neurosci2013, 33:6726-6741.

High-speed videography of whisker motion in head-fixed mice allowedestimation of the forces and bending moments at the base of the whiskeras a function of the radial distance of the contacted pole. Similarly to Ref.[6], the relative amplitudes of different stress variables change dramati-cally with radial object distance.

8. Williams CM, Kramer EM: The advantages of a tapered whisker.PLoS ONE 2010, 5:e8806.

9.�

Hires SA, Pammer L, Svoboda K, Golomb D: Tapered whiskersare required for active tactile sensation. Elife 2013, 2:e01350.

10. Voges D, Carl K, Klauer GJ, Uhlig R, Schilling C, Behn C, Witte H:Structural characterization of the whisker system of the rat.Sensor J IEEE 2012, 12:332-339.

11. Perkon I, Kosir A, Itskov PM, Tasic J, Diamond ME: Unsupervisedquantification of whisking and head movement in freelymoving rodents. J Neurophysiol 2011, 105:1950-1962.

12. Clack NG, O’Connor DH, Huber D, Petreanu L, Hires A, Peron S,Svoboda K, Myers EW: Automated tracking of whiskers invideos of head fixed rodents. PLoS Comput Biol 2012,8:e1002591.

13. Birdwell JA, Solomon JH, Thajchayapong M, Taylor MA, Cheely M,Towal RB, Conradt J, Hartmann MJ: Biomechanical models forradial distance determination by the rat vibrissal system. JNeurophysiol 2007, 98:2439-2455.

14. Quist BW, Hartmann MJ: Mechanical signals at the base of a ratvibrissa: the effect of intrinsic vibrissa curvature andimplications for tactile exploration. J Neurophysiol 2012,107:2298-2312.


http://refhub.elsevier.com/S0959-4388(14)00029-4/sbref0005











































15. Boubenec Y, Shulz DE, Debregeas G: Whisker encoding ofmechanical events during active tactile exploration. FrontBehav Neurosci 2012, 6:74.

16. Solomon JH, Hartmann MJ: Radial distance determination inthe rat vibrissal system and the effects of Weber’s law. PhilosTrans R Soc Lond B Biol Sci 2011, 366:3049-3057.

17. Arabzadeh E, Zorzin E, Diamond ME: Neuronal encoding oftexture in the whisker sensory pathway. PLoS Biol 2005, 3:e17.

18. Jadhav SP, Wolfe J, Feldman DE: Sparse temporal coding ofelementary tactile features during active whisker sensation.Nat Neurosci 2009, 12:792-800.

19. Morita T, Kang H, Wolfe J, Jadhav SP, Feldman DE: Psychometriccurve and behavioral strategies for whisker-based texturediscrimination in rats. PLoS ONE 2011, 6:e20437.

20.��

Zuo Y, Perkon I, Diamond ME: Whisking and whisker kinematicsduring a texture classification task. Philos Trans R Soc Lond BBiol Sci 2011, 366:3058-3069.

During judgment of spatial features on a grooved surface, distinct stick/slip events occurred during texture palpation and their frequencyincreased in relation to the spatial frequency of the grooves. The resultsallow a preliminary assessment of three candidate texture-codingmechanisms—the number of grooves encountered during each touch,the temporal difference between groove contacts and the spatial patternof groove contacts across the whiskers.

21. Jadhav SP, Feldman DE: Texture coding in the whisker system.Curr Opin Neurobiol 2010, 20:313-318.

22. Lottem E, Azouz R: Mechanisms of tactile informationtransmission through whisker vibrations. J Neurosci 2009,29:11686-11697.

23. Jones LM, Depireux DA, Simons DJ, Keller A: Robust temporalcoding in the trigeminal system. Science 2004, 304:1986-1989.

24. Szwed M, Bagdasarian K, Blumenfeld B, Barak O, Derdikman D,Ahissar E: Responses of trigeminal ganglion neurons to theradial distance of contact during active vibrissal touch. JNeurophysiol 2006, 95:791-802.

25. Stuttgen MC, Kullmann S, Schwarz C: Responses of rattrigeminal ganglion neurons to longitudinal whiskerstimulation. J Neurophysiol 2008, 100:1879-1884.

26. Bale MR, Petersen RS: Transformation in the neural code forwhisker deflection direction along the lemniscal pathway. JNeurophysiol 2009, 102:2771-2780.

27. Szwed M, Bagdasarian K, Ahissar E: Encoding of vibrissal activetouch. Neuron 2003, 40:621-630.

28. Leiser SC, Moxon KA: Responses of trigeminal ganglionneurons during natural whisking behaviors in the awake rat.Neuron 2007, 53:117-133.

29. Khatri V, Bermejo R, Brumberg JC, Keller A, Zeigler HP: Whiskingin air: encoding of kinematics by trigeminal ganglion neuronsin awake rats. J Neurophysiol 2009, 101:1836-1846.

30.��

Bale MR, Davies K, Freeman OJ, Ince RAA, Petersen RS: Low-dimensional sensory feature representation by trigeminalprimary afferents. J Neurosci 2013, 33:12003-12012.

Feature selectivity of TG neurons was analyzed using a generalized linearmodel that included linear filters acting on the stimulus and the spikehistory and could predict responses both to ‘noise’ stimuli and to replayednatural whisker motion on textures. The spectrum of TG feature selectivitydetermines the types of information provided to the rest of the whiskerpathway.

31.�

Sakurai K, Akiyama M, Cai B, Scott A, Han BX, Takatoh J,Sigrist M, Arber S, Wang F: The organization of submodality-specific touch afferent inputs in the vibrissa column. Cell Rep2013, 5:87-98.

32. Jones LM, Lee S, Trageser JC, Simons DJ, Keller A: Precisetemporal responses in whisker trigeminal neurons. JNeurophysiol 2004, 92:665-668.

33. Lottem E, Azouz R: A unifying framework underlyingmechanotransduction in the somatosensory system. JNeurosci 2011, 31:8520-8532.


34. Arabzadeh E, Panzeri S, Diamond ME: Deciphering the spiketrain of a sensory neuron: counts and temporal patterns in therat whisker pathway. J Neurosci 2006, 26:9216-9226.

35. von Heimendahl M, Itskov PM, Arabzadeh E, Diamond ME:Neuronal activity in rat barrel cortex underlying texturediscrimination. PLoS Biol 2007, 5:e305.

36. Prescott TJ, Diamond ME, Wing AM: Active touch sensing. PhilosTrans R Soc Lond B Biol Sci 2011, 366:2989-2995.

37. Lee S, Carvell GE, Simons DJ: Motor modulation of afferentsomatosensory circuits. Nat Neurosci 2008, 11:1430-1438.

38. Matyas F, Sreenivasan V, Marbach F, Wacongne C, Barsy B,Mateo C, Aronoff R, Petersen CC: Motor control by sensorycortex. Science 2010, 330:1240-1243.

39. Kleinfeld D, Deschenes M: Neuronal basis for object location inthe vibrissa scanning sensorimotor system. Neuron 2011,72:455-468.

40. Huber D, Gutnisky DA, Peron S, O’Connor DH, Wiegert JS, Tian L,Oertner TG, Looger LL, Svoboda K: Multiple dynamicrepresentations in the motor cortex during sensorimotorlearning. Nature 2012, 484:473-478.

41.�

Petreanu L, Gutnisky DA, Huber D, Xu NL, O’Connor DH, Tian L,Looger L, Svoboda K: Activity in motor-sensory projectionsreveals distributed coding in somatosensation. Nature 2012,489:299-303.

42. Grant RA, Mitchinson B, Fox CW, Prescott TJ: Active touchsensing in the rat: anticipatory and regulatory control ofwhisker movements during surface exploration. J Neurophysiol2009, 101:862-874.

43. Jenks RA, Vaziri A, Boloori AR, Stanley GB: Self-motion and theshaping of sensory signals. J Neurophysiol 2010,103:2195-2207.

44. Hutson KA, Masterton RB: The sensory contribution of a singlevibrissa’s cortical barrel. J Neurophysiol 1986, 56:1196-1223.

45. Carvell GE, Simons DJ: Biometric analyses of vibrissal tactilediscrimination in the rat. J Neurosci 1990, 10:2638-2648.

46. Knutsen PM, Pietr M, Ahissar E: Haptic object localization in thevibrissal system: behavior and performance. J Neurosci 2006,26:8451-8464.

47. O’Connor DH, Clack NG, Huber D, Komiyama T, Myers EW,Svoboda K: Vibrissa-based object localization in head-fixedmice. J Neurosci 2010, 30:1947-1967.

48. Yu C, Horev G, Rubin N, Derdikman D, Haidarliu S, Ahissar E:Coding of object location in the vibrissal thalamocorticalsystem. Cereb Cortex 2013, http://dx.doi.org/10.1093/cercor/bht241, first published online September 22, 2013.

49. Stuttgen MC, Ruter J, Schwarz C: Two psychophysical channelsof whisker deflection in rats align with two neuronal classes ofprimary afferents. J Neurosci 2006, 26:7933-7940.

50. Stuttgen MC, Schwarz C: Psychophysical and neurometricdetection performance under stimulus uncertainty. NatNeurosci 2008, 11:1091-1100.

51. Adibi M, Arabzadeh E: A comparison of neuronal and behavioraldetection and discrimination performances in rat whiskersystem. J Neurophysiol 2011, 105:356-365.

52. Adibi M, Diamond ME, Arabzadeh E: Behavioral study ofwhisker-mediated vibration sensation in rats. Proc Natl AcadSci U S A 2012, 109:971-976.

53. Mayrhofer JM, Skreb V, von der Behrens W, Musall S, Weber B,Haiss F: Novel two-alternative forced choice paradigm forbilateral vibrotactile whisker frequency discrimination inhead-fixed mice and rats. J Neurophysiol 2013, 109:273-284.

54. Miyashita T, Feldman DE: Behavioral detection of passivewhisker stimuli requires somatosensory cortex. Cereb Cortex2013, 23:1655-1660.

55. Krupa DJ, Matell MS, Brisben AJ, Oliveira LM, Nicolelis MA:Behavioral properties of the trigeminal somatosensory


































































































http://dx.doi.org/10.1093/cercor/bht241,

http://dx.doi.org/10.1093/cercor/bht241,























system in rats performing whisker-dependent tactilediscriminations. J Neurosci 2001, 21:5752-5763.

56. Sharpee TO: Computational identification of receptive fields.Annu Rev Neurosci 2013, 36:103-120.

57. Maravall M, Alenda A, Bale MR, Petersen RS: Transformation ofadaptation and gain rescaling along the whisker sensorypathway. PLoS ONE 2013, 8:e82418.

58. Theis L, Chagas AM, Arnstein D, Schwarz C, Bethge M: BeyondGLMs: a generative mixture modeling approach to neuralsystem identification. PLoS Comput Biol 2013, 9:e1003356.

59. Petersen RS, Brambilla M, Bale MR, Alenda A, Panzeri S,Montemurro MA, Maravall M: Diverse and temporally precisekinetic feature selectivity in the VPm thalamic nucleus. Neuron2008, 60:890-903.

60. Maravall M, Petersen RS, Fairhall AL, Arabzadeh E, Diamond ME:Shifts in coding properties and maintenance of informationtransmission during adaptation in barrel cortex. PLoS Biol2007, 5:e19.

61.��

Estebanez L, El Boustani S, Destexhe A, Shulz DE: Correlatedinput reveals coexisting coding schemes in a sensory cortex.Nat Neurosci 2012, 15:1691-1699.

Systematic application of different spatiotemporal patterns of stimuli tomultiple (24) whiskers, combined with reverse correlation analysis, pro-vided a unified framework capturing multiple response properties in barrelcortex. Several functional categories of neurons emerged, grouped bytheir responses to whisker dynamics and by their selectivity to the degreeof correlated stimulation of multiple whiskers.

62. Petersen RS, Panzeri S, Maravall M: Neural coding andcontextual influences in the whisker system. Biol Cybern 2009,100:427-446.

63. Jacob V, Le Cam J, Ego-Stengel V, Shulz DE: Emergentproperties of tactile scenes selectively activate barrel cortexneurons. Neuron 2008, 60:1112-1125.

64. Ego-Stengel V, Le Cam J, Shulz DE: Coding of apparent motionin the thalamic nucleus of the rat vibrissal somatosensorysystem. J Neurosci 2012, 32:3339-3351.

65. Hirata A, Castro-Alamancos MA: Cortical transformation ofwide-field (multiwhisker) sensory responses. J Neurophysiol2008, 100:358-370.

66. Crochet S, Petersen CC: Correlating whisker behavior withmembrane potential in barrel cortex of awake mice. NatNeurosci 2006, 9:608-610.

67. Curtis JC, Kleinfeld D: Phase-to-rate transformations encodetouch in cortical neurons of a scanning sensorimotor system.Nat Neurosci 2009, 12:492-501.

68.��

O’Connor DH, Peron SP, Huber D, Svoboda K: Neural activity inbarrel cortex underlying vibrissa-based object localization inmice. Neuron 2010, 67:1048-1061.

During a pole location discrimination task in mice, a small fraction ofneurons fired most of the spikes recorded in barrel cortex: the most active10% generated approximately 50% of spikes. A neuron’s activity levelcorrelated with its ability to discriminate trial type: both activity level anddiscriminative ability were lower in layers 2/3 and 6 than in layers 4 and 5.However, all layers contained neurons that responded strongly and werehighly discriminative.

69.�

Chen JL, Carta S, Soldado-Magraner J, Schneider BL,Helmchen F: Behaviour-dependent recruitment of long-rangeprojection neurons in somatosensory cortex. Nature 2013,499:336-340.

70. de Kock CP, Bruno RM, Spors H, Sakmann B: Layer- and cell-type-specific suprathreshold stimulus representation inrat primary somatosensory cortex. J Physiol 2007,581:139-154.

71. de Kock CP, Sakmann B: Spiking in primary somatosensorycortex during natural whisking in awake head-restrained ratsis cell-type specific. Proc Natl Acad Sci U S A 2009,106:16446-16450.

72. Barth AL, Poulet JF: Experimental evidence for sparse firing inthe neocortex. Trends Neurosci 2012, 35:345-355.


73. Wolfe J, Houweling AR, Brecht M: Sparse and powerful corticalspikes. Curr Opin Neurobiol 2010, 20:306-312.

74. Ganguli S, Sompolinsky H: Compressed sensing, sparsity, anddimensionality in neuronal information processing and dataanalysis. Annu Rev Neurosci 2012, 35:485-508.

75. Harris KD, Mrsic-Flogel TD: Cortical connectivity and sensorycoding. Nature 2013, 503:51-58.

76. Quiroga RQ: Concept cells: the building blocks of declarativememory functions. Nat Rev Neurosci 2012, 13:587-597.

77. Petersen CC, Crochet S: Synaptic computation and sensoryprocessing in neocortical layer 2/3. Neuron 2013, 78:28-48.

78.��

Safaai H, von Heimendahl M, Sorando JM, Diamond ME,Maravall M: Coordinated population activity underlying texturediscrimination in rat barrel cortex. J Neurosci 2013,33:5843-5855.

This study used ideal observer analysis to determine the trial discrimina-tion performance of groups of neuronal single or multiple units recordedwhile a rat performed a texture discrimination task. The collective signalwas analyzed in terms of the net redundancy or synergy present and itsdependence on the amount of heterogeneity between neurons. Informa-tion in pooled populations saturated at �20 neurons, at which pointredundancy set in. The population size at which representations transitionfrom synergy to redundancy may depend on sensory modality, taskdifficulty, and degree of training.

79. Yamashita T, Pala A, Pedrido L, Kremer Y, Welker E, Petersen CC:Membrane potential dynamics of neocortical projectionneurons driving target-specific signals. Neuron 2013,80:1477-1480.

80. Andermann ML, Moore CI: A somatotopic map of vibrissamotion direction within a barrel column. Nat Neurosci 2006,9:543-551.

81. Kerr JN, de Kock CP, Greenberg DS, Bruno RM, Sakmann B,Helmchen F: Spatial organization of neuronal populationresponses in layer 2/3 of rat barrel cortex. J Neurosci 2007,27:13316-13328.

82. Sato TR, Gray NW, Mainen ZF, Svoboda K: The functionalmicroarchitecture of the mouse barrel cortex. PLoS Biol 2007,5:e189.

83.�

Crochet S, Poulet JF, Kremer Y, Petersen CC: Synapticmechanisms underlying sparse coding of active touch. Neuron2011, 69:1160-1175.

84. Margolis DJ, Lutcke H, Schulz K, Haiss F, Weber B, Kugler S,Hasan MT, Helmchen F: Reorganization of cortical populationactivity imaged throughout long-term sensory deprivation. NatNeurosci 2012, 15:1539-1546.

85. Yassin L, Benedetti BL, Jouhanneau JS, Wen JA, Poulet JF,Barth AL: An embedded subnetwork of highly active neurons inthe neocortex. Neuron 2010, 68:1043-1050.

86. Montemurro MA, Panzeri S, Maravall M, Alenda A, Bale MR,Brambilla M, Petersen RS: Role of precise spike timing in codingof dynamic vibrissa stimuli in somatosensory thalamus. JNeurophysiol 2007, 98:1871-1882.

87. Bruno RM, Sakmann B: Cortex is driven by weak butsynchronously active thalamocortical synapses. Science 2006,312:1622-1627.

88. Fee MS, Mitra PP, Kleinfeld D: Central versus peripheraldeterminants of patterned spike activity in rat vibrissa cortexduring whisking. J Neurophysiol 1997, 78:1144-1149.

89. Hill DN, Curtis JC, Moore JD, Kleinfeld D: Primary motor cortexreports efferent control of vibrissa motion on multipletimescales. Neuron 2011, 72:344-356.

90. Gentet LJ, Avermann M, Matyas F, Staiger JF, Petersen CC:Membrane potential dynamics of GABAergic neurons in thebarrel cortex of behaving mice. Neuron 2010, 65:422-435.

91. Knutsen PM, Ahissar E: Orthogonal coding of object location.Trends Neurosci 2009, 32:101-109.

92. Panzeri S, Diamond ME: Information carried by populationspike times in the whisker sensory cortex can be decoded






















































































































without knowledge of stimulus time. Front Synaptic Neurosci2010, 2:17.

93. Panzeri S, Ince RAA, Diamond ME, Kayser C: Reading spike timingwithout a clock: intrinsic decoding of spike trains. Phil Trans R SocB 2014, 369 20120467. http://dx.doi.org/10.1098/rstb.2012.0467.

94. Panzeri S, Petersen RS, Schultz SR, Lebedev M, Diamond ME:The role of spike timing in the coding of stimulus location in ratsomatosensory cortex. Neuron 2001, 29:769-777.

95. Petersen RS, Panzeri S, Diamond ME: Population coding ofstimulus location in rat somatosensory cortex. Neuron 2001,32:503-514.

96. Storchi R, Bale MR, Biella GE, Petersen RS: Comparison oflatency and rate coding for the direction of whisker deflectionin the subcortical somatosensory pathway. J Neurophysiol2012, 108:1810-1820.

97. Temereanca S, Brown EN, Simons DJ: Rapid changes inthalamic firing synchrony during repetitive whiskerstimulation. J Neurosci 2008, 28:11153-11164.

98. Khatri V, Bruno RM, Simons DJ: Stimulus-specific and stimulus-nonspecific firing synchrony and its modulation by sensoryadaptation in the whisker-to-barrel pathway. J Neurophysiol2009, 101:2328-2338.

99. Stanley GB, Jin J, Wang Y, Desbordes G, Wang Q, Black MJ,Alonso JM: Visual orientation and directional selectivitythrough thalamic synchrony. J Neurosci 2012, 32:9073-9088.

100. Brette R: Computing with neural synchrony. PLoS Comput Biol2012, 8:e1002561.

101. Rossant C, Leijon S, Magnusson AK, Brette R: Sensitivity of noisyneurons to coincident inputs. J Neurosci 2011, 31:17193-17206.

102. Song S, Sjostrom PJ, Reigl M, Nelson S, Chklovskii DB: Highlynonrandom features of synaptic connectivity in local corticalcircuits. PLoS Biol 2005, 3:e68.

103. Lefort S, Tomm C, Floyd Sarria JC, Petersen CC: The excitatoryneuronal network of the C2 barrel column in mouse primarysomatosensory cortex. Neuron 2009, 61:301-316.

104. Diaz-Quesada M, Martini FJ, Ferrati G, Bureau I, Maravall M:Diverse thalamocortical short-term plasticity elicited byongoing stimulation. J Neurosci 2014, 34:515-526.

105�

O’Connor DH, Hires SA, Guo ZV, Li N, Yu J, Sun QQ, Huber D,Svoboda K: Neural coding during active somatosensationrevealed using illusory touch. Nat Neurosci 2013, 16:958-965.

106. Diamond ME, von Heimendahl M, Arabzadeh E: Whisker-mediated texture discrimination. PLoS Biol 2008, 6:e220.

107. Maravall M: Adaptation and sensory coding. In Principles ofneural coding. Edited by Quian Quiroga R, Panzeri S. Boca Raton:Taylor & Francis/CRC Press; 2013:357-378.

108. Katz Y, Heiss JE, Lampl I: Cross-whisker adaptation of neuronsin the rat barrel cortex. J Neurosci 2006, 26:13363-13372.

109. Khatri V, Simons DJ: Angularly nonspecific responsesuppression in rat barrel cortex. Cereb Cortex 2007,17:599-609.

110. Wang Q, Webber RM, Stanley GB: Thalamic synchrony and theadaptive gating of information flow to cortex. Nat Neurosci2010, 13:1534-1541.

111. Adibi M, McDonald JS, Clifford CW, Arabzadeh E: Adaptationimproves neural coding efficiency despite increasingcorrelations in variability. J Neurosci 2013, 33:2108-2120.


112. Adibi M, Clifford CW, Arabzadeh E: Informational basis ofsensory adaptation: entropy and single-spike efficiency in ratbarrel cortex. J Neurosci 2013, 33:14921-14926.

113. Lak A, Arabzadeh E, Diamond ME: Enhanced response ofneurons in rat somatosensory cortex to stimuli containingtemporal noise. Cereb Cortex 2008, 18:1085-1093.

114. Lak A, Arabzadeh E, Harris JA, Diamond ME: Correlatedphysiological and perceptual effects of noise in a tactilestimulus. Proc Natl Acad Sci U S A 2010, 107:7981-7986.

115. Garcia-Lazaro JA, Ho SS, Nair A, Schnupp JW: Shifting andscaling adaptation to dynamic stimuli in somatosensorycortex. Eur J Neurosci 2007, 26:2359-2368.

116. Lundstrom BN, Fairhall AL, Maravall M: Multiple timescaleencoding of slowly varying whisker stimulus envelope incortical and thalamic neurons in vivo. J Neurosci 2010,30:5071-5077.

117. Chung S, Li X, Nelson SB: Short-term depression atthalamocortical synapses contributes to rapid adaptation ofcortical sensory responses in vivo. Neuron 2002, 34:437-446.

118. Ganmor E, Katz Y, Lampl I: Intensity-dependent adaptation ofcortical and thalamic neurons is controlled by brainstemcircuits of the sensory pathway. Neuron 2010, 66:273-286.

119. Diaz-Quesada M, Maravall M: Intrinsic mechanisms for adaptivegain rescaling in barrel cortex. J Neurosci 2008, 28:696-710.

120. Mease RA, Famulare M, Gjorgjieva J, Moody WJ, Fairhall AL:Emergence of adaptive computation by single neurons in thedeveloping cortex. J Neurosci 2013, 33:12154-12170.

121. Abbott LF, Dayan P: The effect of correlated variability on theaccuracy of a population code. Neural Comput 1999, 11:91-101.

122. Sompolinsky H, Yoon H, Kang K, Shamir M: Population coding inneuronal systems with correlated noise. Phys Rev E Stat NonlinSoft Matter Phys 2001, 64:051904.

123. Shamir M, Sompolinsky H: Implications of neuronal diversity onpopulation coding. Neural Comput 2006, 18:1951-1986.

124. Chelaru MI, Dragoi V: Efficient coding in heterogeneousneuronal populations. Proc Natl Acad Sci U S A 2008,105:16344-16349.

125. Hung CP, Kreiman G, Poggio T, DiCarlo JJ: Fast readout ofobject identity from macaque inferior temporal cortex. Science2005, 310:863-866.

126. Meyers EM, Freedman DJ, Kreiman G, Miller EK, Poggio T:Dynamic population coding of category information in inferiortemporal and prefrontal cortex. J Neurophysiol 2008, 100:1407-1419.

127. Nikolic D, Hausler S, Singer W, Maass W: Distributed fadingmemory for stimulus properties in the primary visual cortex.PLoS Biol 2009, 7:e1000260.

128�

Guo ZV, Li N, Huber D, Ophir E, Gutnisky D, Ting JT, Feng G,Svoboda K: Flow of cortical activity underlying a tactiledecision in mice. Neuron 2014, 81:179-194.

129��

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.




http://dx.doi.org/10.1098/rstb.2012.0467








































































































http://dx.doi.org/10.1073/pnas.1315171111

algorithms of whisker-mediated touch perception -...

Documents