Sensory Systems/Somatosensory System


Anatomy of the Somatosensory SystemEdit

Our somatosensory system consists of sensors in the skin and sensors in our muscles, tendons, and joints. The receptors in the skin, the so called cutaneous receptors, tell us about temperature (thermoreceptors), pressure and surface texture (mechano receptors), and pain (nociceptors). The receptors in muscles and joints provide information about muscle length, muscle tension, and joint angles. (The following description is based on lecture notes from Laszlo Zaborszky, from Rutgers University.)

Cutaneous receptorsEdit


Receptors in the human skin: Mechanoreceptors can be free receptors or encapsulated. Examples for free receptors are the hair receptors at the roots of hairs. Encapsulated receptors are the Pacinian corpuscles and the receptors in the glabrous (hairless) skin: Meissner corpuscles, Ruffini corpuscles and Merkel’s disks.

Sensory information from Meissner corpuscles and rapidly adapting afferents leads to adjustment of grip force when objects are lifted. These afferents respond with a brief burst of action potentials when objects move a small distance during the early stages of lifting. In response to rapidly adapting afferent activity, muscle force increases reflexively until the gripped object no longer moves. Such a rapid response to a tactile stimulus is a clear indication of the role played by somatosensory neurons in motor activity.

The slowly adapting Merkel's receptors are responsible for form and texture perception. As would be expected for receptors mediating form perception, Merkel‘s receptors are present at high density in the digits and around the mouth (50/mm2 of skin surface), at lower density in other glabrous surfaces, and at very low density in hairy skin. This innervations density shrinks progressively with the passage of time so that by the age of 50, the density in human digits is reduced to 10/mm2. Unlike rapidly adapting axons, slowly adapting fibers respond not only to the initial indentation of skin, but also to sustained indentation up to several seconds in duration.

Activation of the rapidly adapting Pacinian corpuscles gives a feeling of vibration, while the slowly adapting Ruffini corpuscles respond to the lataral movement or stretching of skin.

Rapidly adapting Slowly adapting
Surface receptor / small receptive field Hair receptor, Meissner's corpuscle: Detect an insect or a very fine vibration. Used for recognizing texture. Merkel's receptor: Used for spatial details, e.g. a round surface edge or "an X" in brail.
Deep receptor / large receptive field Pacinian corpuscle: "A diffuse vibration" e.g. tapping with a pencil. Ruffini's corpuscle: "A skin stretch". Used for joint position in fingers.


Nociceptors have free nerve endings. Functionally, skin nociceptors are either high-threshold mechanoreceptors or polymodal receptors. Polymodal receptors respond not only to intense mechanical stimuli, but also to heat and to noxious chemicals. These receptors respond to minute punctures of the epithelium, with a response magnitude that depends on the degree of tissue deformation. They also respond to temperatures in the range of 40-60oC, and change their response rates as a linear function of warming (in contrast with the saturating responses displayed by non-noxious thermoreceptors at high temperatures).

Pain signals can be separated into individual components, corresponding to different types of nerve fibers used for transmitting these signals. The rapidly transmitted signal, which often has high spatial resolution, is called first pain or cutaneous pricking pain. It is well localized and easily tolerated. The much slower, highly affective component is called second pain or burning pain; it is poorly localized and poorly tolerated. The third or deep pain, arising from viscera, musculature and joints, is also poorly localized, can be chronic and is often associated with referred pain.


The thermoreceptors have free nerve endings. Interestingly, we have only two types of thermoreceptors that signal innocuous warmth and cooling respectively in our skin (however, some nociceptors are also sensitive to temperature, but capable of unamibiously signaling only noxious temperatures). The warm receptors show a maximum sensitivity at ~ 45°C, signal temperatures between 30 and 45°C, and cannot unambiguously signal temperatures higher than 45°C , and are unmyelinated. The cold receptors have their maximum sensitivity at ~ 27°C, signal temperatures above 17°C, and some consist of lightly myelinated fibers, while others are unmyelinated. Our sense of temperature comes from the comparison of the signals from the warm and cold receptors. Thermoreceptors are poor indicators of absolute temperature but are very sensitive to changes in skin temperature.


The term proprioceptive or kinesthetic sense is used to refer to the perception of joint position, joint movements, and the direction and velocity of joint movement. There are numerous mechanoreceptors in the muscles, the muscle fascia, and in the dense connective tissue of joint capsules and ligaments. There are two specialized encapsulated, low-threshold mechanoreceptors: the muscle spindle and the Golgi tendon organ. Their adequate stimulus is stretching of the tissue in which they lie. Muscle spindles, joint and skin receptors all contribute to kinesthesia. Muscle spindles appear to provide their most important contribution to kinesthesia with regard to large joints, such as the hip and knee joints, whereas joint receptors and skin receptors may provide more significant contributions with regard to finger and toe joints.

Muscle SpindlesEdit

Mammalian muscle spindle showing typical position in a muscle (left), neuronal connections in spinal cord (middle) and expanded schematic (right). The spindle is a stretch receptor with its own motor supply consisting of several intrafusal muscle fibres. The sensory endings of a primary (group Ia) afferent and a secondary (group II) afferent coil around the non-contractile central portions of the intrafusal fibres. Gamma motoneurons activate the intrafusal muscle fibres, changing the resting firing rate and stretch-sensitivity of the afferents.

Scattered throughout virtually every striated muscle in the body are long, thin, stretch receptors called muscle spindles. They are quite simple in principle, consisting of a few small muscle fibers with a capsule surrounding the middle third of the fibers. These fibers are called intrafusal fibers, in contrast to the ordinary extrafusal fibers. The ends of the intrafusal fibers are attached to extrafusal fibers, so whenever the muscle is stretched, the intrafusal fibers are also stretched. The central region of each intrafusal fiber has few myofilaments and is non-contractile, but it does have one or more sensory endings applied to it. When the muscle is stretched, the central part of the intrafusal fiber is stretched and each sensory ending fires impulses.

Numerous specializations occur in this simple basic organization, so that in fact the muscle spindle is one of the most complex receptor organs in the body. Only three of these specializations are described here; their overall effect is to make the muscle spindle adjustable and give it a dual function, part of it being particularly sensitive to the length of the muscle in a static sense and part of it being particularly sensitive to the rate at which this length changes.

  1. Intrafusal muscle fibers are of two types. All are multinucleated, and the central, non-contractile region contains the nuclei. In one type of intrafusal fiber, the nuclei are lined up single file; these are called nuclear chain fiber. In the other type, the nuclear region is broader, and the nuclei are arranged several abreast; these are called nuclear bag fibers. There are typically two or three nuclear bag fibers per spindle and about twice that many chain fibers.
  2. There are also two types of sensory endings in the muscle spindle. The first type, called the primary ending, is formed by a single Ia (A-alpha) fiber, supplying every intrafusal fiber in a given spindle. Each branch wraps around the central region of the intrafusal fiber, frequently in a spiral fashion, so these are sometimes called annulospiral endings. The second type of ending is formed by a few smaller nerve fibers (II or A-Beta) on both sides of the primary endings. These are the secondary endings, which are sometimes referred to as flower-spray endings because of their appearance. Primary endings are selectively sensitive to the onset of muscle stretch but discharge at a slower rate while the stretch is maintained. Secondary endings are less sensitive to the onset of stretch, but their discharge rate does not decline very much while the stretch is maintained. In other words, both primary and secondary endings signal the static length of the muscle (static sensitivity) whereas only the primary ending signals the length changes (movement) and their velocity (dynamic sensitivity). The change of firing frequency of group Ia and group II fibers can then be related to static muscle length (static phase) and to stretch and shortening of the muscle (dynamic phases).
  3. Muscle spindles also receive a motor innervation. The large motor neurons that supply extrafusal muscle fibers are called alpha motor neurons, while the smaller ones supplying the contractile portions of intrafusal fibers are called gamma neurons. Gamma motor neurons can regulate the sensitivity of the muscle spindle so that this sensitivity can be maintained at any given muscle length.

Golgi tendon organEdit

Mammalian tendon organ showing typical position in a muscle (left), neuronal connections in spinal cord (middle) and expanded schematic (right). The tendon organ is a stretch receptor that signals the force developed by the muscle. The sensory endings of the Ib afferent are entwined amongst the musculotendinous strands of 10 to 20 motor units.

The Golgi tendon organ is located at the musculotendinous junction. There is no efferent innervation of the tendon organ, therefore its sensitivity cannot be controlled from the CNS. The tendon organ, in contrast to the muscle spindle, is coupled in series with the extrafusal muscle fibers. Both passive stretch and active contraction of the muscle increase the tension of the tendon and thus activate the tendon organ receptor, but active contraction produces the greatest increase. The tendon organ, consequently, can inform the CNS about the “muscle tension”. In contrast, the activity of the muscle spindle depends on the “muscle length” and not on the tension. The muscle fibers attached to one tendon organ appear to belong to several motor units. Thus the CNS is informed not only of the overall tension produced by the muscle but also of how the workload is distributed among the different motor units.

Joint receptorsEdit

The joint receptors are low-threshold mechanoreceptors and have been divided into four groups. They signal different characteristics of joint function (position, movements, direction and speed of movements). The free receptors or type 4 joint receptors are nociceptors.

Proprioceptive Signal ProcessingEdit

Feedback loops for proprioceptive signals for the perception and control of limb movements. Arrows indicate excitatory connections; filled circles inhibitory connections.

Modelling muscle spindles and afferent responseEdit

The response of the muscle spindles in mammals to muscle stretch has been thoroughly studied, and various models have been proposed. However, due to the difficulty in obtaining accurate data of the afferent and fusimotor responses during muscular movement, these models have usually been quite limited. For example, several of the earliest models account only for the afferent response, ignoring the fusimotor activity.

Mileusnic et al. (2006) modelEdit

One recent model, developed by Mileusnic et al. (2006), portrays the muscle spindle as consisting of several (typically 4 to 11) nuclear chain fibres, and two different nuclear bag fibres, connected in parallel as shown here in the figure below. The muscle fibres respond to three inputs: fascicle length, dynamic fusimotor input and static fusimotor input. The bag_1 fibre is mainly responsible for detecting dynamic fusimotor input, while the bag_2 and chain fibres are mainly responsible for detecting static fusimotor input. All fibres respond to changes in the fascicle length, and are modelled in largely the same way but with different coefficients to account for their different physiological properties. The responses of the three types of fibres are summed to generate the primary and secondary afferent activities. The primary afferent activity is affected by the response of all three types of muscle fibres, while the secondary afferent activity only depends on the bag_2 and chain fibre responses.


Hasan (1983) modelEdit

Another comprehensive model of muscle spindles was proposed by Hasan in 1983 [1]. This representation of muscle fibres and spindles is based closely on their physical properties. The muscle spindle is represented as two separate regions connected in series: sensory and non-sensory. The firing rate of the spindle afferent depends on the state of the two regions[1]. The lengths of the two regions can be labelled z(t) for the sensory and y(t) for the non-sensory region. The tension f(t) in the two regions is equal, since they are placed in series. The sensory zone can be assumed to act like a spring (equation (3)), while in the non-sensory region, tension is a non-linear function of y(t) (equation (2) derived by Hasan).

f(t) = k_1(y(t)-c)(1+[\frac{y'(t)}{a}]^{\frac{1}{3}}) \qquad \qquad \text{(2)}

f(t) = k_2z(t) \qquad \qquad \qquad \qquad \qquad \qquad \text{(3)}

The total length of the muscle spindle, x(t) is the sum of the length of the two regions (equation (4)).

x(t) = z(t) + y(t) \qquad \qquad \qquad \qquad \qquad \text{(4)}

Using this substitution and rearranging, we can derive the following expression for the length of the sensory zone (equation (5)):

z'(t) = x'(t) - a(\frac{bz(t) - x(t) + c}{x(t) - z(t) - c})^3 \qquad \text{(5)}

Here, parameter a represents the sensitivity of the tension to to velocity in the non-sensory zone, parameter b = (k_1+k_2)/k_1 and parameter c determines the zero-length tension which influences the background firing rate of the afferent. The length of the sensory zone depends not only on the current length and velocity of the spindle, but on the history of the length changes.

The firing rate, g(t) in Hasan's model depends on a combination of the sensory zone length and its first derivative (equation (6)), with an experimentally derived weighting.

g(t) = z(t) + 0.1z'(t) \qquad \qquad \qquad \qquad \text{(6)}

Model parametersEdit

Approximate values for the model parameters a, b and c were suggested by Hasan (1983), and differ for voluntary and passive movements. A summary of these values is presented in the table below. Type of ending Condition A (mm/s) B C (mm)

Type of ending Condition A (mm/s) B C (mm)
Primary Passive 0.3 250 -15
Primary Gamma - dynamic 0.1 125 -15
Primary Gamma - static 100 100 -25
Secondary Passive 50 50 -20

In the model, these values are assumed to be static for the duration of a movement, however this is not believed to be the case.

Internal models of limb dynamicsEdit

In addition to modelling the response of muscle spindle afferents to muscle stretch, several groups have worked on modelling the signals which are sent from the brain to the spindle efferents in order for muscles to complete specific movements. The complexity here lies in the fact that the brain must be able to adapt to unexpected changes in the dynamics of planned movements, using feedback from the spindle afferents.

Studies in this area suggest that humans achieve this using internal models, which are built through an “error-feedback-learning” process, and transform planned muscle states into the motor commands required to achieve them. To generate the motor commands for a particular reaching movement, the brain performs calculations based on the expected dynamics of the planned movement. However, any unexpected changes in these dynamics while the movement is being executed (e.g. external strain placed on the muscle) will lead to errors in expected muscle length (Gottlieb 1994, Shadmehr and Muss-Ivaldi 1994). These errors are communicated to the brain through the muscle spindle afferents, which experience a different sensory state to what is expected. The brain then reacts to these error signals with short and long latency responses, which work to minimise the error, but cannot eliminate it completely due to the delay in the system.

Studies suggest that the error can be eliminated in a subsequent attempt at the movement under the same dynamics, and this is where the “error-feedback-learning” idea comes from (Thoroughman and Shadmehr 1999). The corrections which are generated by the brain form an internal model, which maps a desired action (in kinematic coordinates) to the necessary motor commands (as torques). This internal model can be represented as a weighted combination of basis elements:

torque = \sum w_i  g_i(\theta,\theta',\theta''...)

Here each basis g_i represents some characteristic of the muscle's sensory state, and the motor command is a “population code”. Population coding is a method of representing stimuli as the combined activity of many neurons (in contrast to rate coding). In order to use such a model, we need to know how the bases represent particular limb or muscle positions, and the neuronal firing rates associated with them. The bases can, in principle, represent every aspect of the state: position, velocity, acceleration and even higher derivatives. However, this high dimensionality makes it very difficult to derive relationships experimentally between each dimension of the bases and the firing rates.

Somatosensory Perception of WhiskersEdit


Figure 1A. Overview of the whisker system in rats
Figure 1B. System level description of the ascending pathways from whiskers to barrel cortex.

The barrel Cortex is a specialized region in somatosensory cortex responsible for processing the tactile information from whiskers. As every other cortical region, the barrel cortex also preserves the columnar organization which plays a crucial role in information processing. Information from each whisker is represented in separate, discrete columns analogous to “barrels”, hence the name barrel cortex. Rodents use whiskers constantly to acquire sensory information from the environment. Given their nocturnal nature, tactile information carried by whisker forms the primary sensory signals to build a perceptual map of the environment. The whiskers on the snouts of mice and rats serve as arrays of highly sensitive detectors for acquiring tactile information as shown in Figure 1 A and B. By using their whiskers, rodents can build spatial representations of their environment, locate objects, and perform fine-grain texture discrimination. Somatosensory whisker-related processing is highly organized into stereotypical maps, which occupy a large portion of the rodent brain. During exploration and palpation of objects, the whiskers are under motor control, often executing rapid large-amplitude rhythmic sweeping movements, and this sensory system is therefore an attractive model for investigating active sensory processing and sensory-motor integration. In these animals, a large part of the neocortex is dedicated to the processing of information from the whiskers. Since rodents are nocturnal, visual information is relatively poor and they rely heavily on the tactile information from whiskers. Perhaps the most remarkable specialization of this sensory system is the primary somatosensory ‘‘barrel’’ cortex, where each whisker is represented by a discrete and well-defined structure in layer 4.

These layer 4 barrels are somatotopically arranged in an almost identical fashion to the layout of the whiskers on the snout i.e. bordering whiskers are represented in adjacent cortical areas [1]. Sensorimotor integration of whisker related activity leads to pattern discrimination and enables rodents to have a reliable map of the environment. This is an interesting model to study because rodents use whisker to “see” and this cross modality sensory information processing could help us to improve the life of humans, who are deprived of one sensory modality. Specifically, blind people can be trained to use somatosensory information to build a spatial map of the environment [2].

Pathways carrying whisker information to Barrel CortexEdit

Pathways carrying whisker information to Barrel Cortex
Figure 2. Schematic demonstrating the ascending pathway of rodent whisker-related sensorimotor system.

The tactile information from the whiskers on the snouts is carried through the trigeminal nerves, which terminate at the trigeminal nucleus as shown in Figure 2. The ascending pathway starts with the primary afferents in the trigeminal ganglion (TG) transducing whisker vibrations into neuronal signals, and projecting to the trigeminal brainstem complex (TN). The TN consists of the principal nucleus (PrV), and the spinal sub-nuclei (interpolarisSpVi; caudalisSpVc; the detailed connectivity of the oralis sub-nucleus is unknown and is omitted in the figure). The SpVi falls into a caudal and rostral part (SpVic and SpVir). The classical mono-whisker lemniscal pathway (lemniscal 1) originates in PrV barrelettes, and projects via VPM barreloid cores to primary somatosensory cortex (S1) barrel columns. A second lemniscal pathway originating from PrV has been recently discovered which carries multi-whisker signals via barreloid heads to septa (and dysgranular zone) of S1. The extra-lemniscal pathway originates in SPVic and carries multi-whisker signals via barreloid tails in VPM to the secondary somatosensory area. Finally, the parelemniscal pathway originates in SpVir and carries multi-whisker signals via POm to S1, S2, and primary motor area (M1). The different colours of connections indicate three principal pathways through which associative coupling between the sensorimotor cortical areas may be realized. Black indicates direct cortico-cotical connections. Blue shows cortico-thalamic cascades. Brown represents cortico-sub-cortical loops. Projections of S1 and S2 may open or close the lemniscal gate (i.e. gate signal flow through PrV) by modulating intrinsic TN circuitry.

Figure 3.Processing of whisker-related sensory information in barrel cortex. System level description of the pathways involved in the propagation of information from whiskers to cortex & columnar organization of the barrel cortex which receives information from single whisker.

The sensory neurons make excitatory glutamatergic synapses in the trigeminal nuclei of the brain stem. Trigemino-thalamic neurons in the principal trigeminal nucleus are organized into somatotopically arranged ‘‘barrelettes,’’ each receiving strong input from a single whisker as shown in (Figure 3). The principal trigeminal neurons project to the ventral posterior medial (VPM) nucleus of the thalamus, which is also somatotopically laid out into anatomical units termed ‘‘barreloids’’ VPM neurons respond rapidly and precisely to whisker deflection, with one ‘‘principal’’ whisker evoking stronger responses than all others. The axons of VPM neurons within individual barreloids project to the primary somatosensory neocortex forming discrete clusters in layer 4, which form the basis of the ‘‘barrel’’ map as shown in Figure 3.

Whisker information processing in Barrel Cortex with specialized local microcircuitEdit

The deflection of a whisker is thought to open mechano-gated ion channels in nerve endings of sensory neurons innervating the hair follicle (although the molecular signalling machinery remains to be identified). The resulting depolarization evokes action potential firing in the sensory neurons of the infraorbital branch of the trigeminal nerve. The transduction through mechanical deformation is similar to the hair cells in the inner ear; in this case the contact of whiskers with the objects causes the mechano-gated ion channels to open. Cation-permeable ion channels let positively charged ions into the cells and causes depolarization, eventually leading to generation of action potentials. A single sensory neuron only fires action potentials to deflection of one specific whisker. The innervation of the hair follicle shows a diversity of nerve endings, which may be specialized for detecting different types of sensory input [3].

The layer 4 barrel map is arranged almost identically to the layout of the whiskers on the snout of the rodent. There are several recurrent connections in layer 4 and it sends axons to layer 2/3 neurons, which integrates information from other cortical regions like primary motor cortex. These intra-cortical and inter-cortical connections enable the rodents to achieve stimulus discrimination capabilities and to extract optimal information from the incoming tactile stimulus. Also, these projections play a crucial role in integrating somatosensory information with motor output. Information from whiskers is processed in the barrel cortex with specialized local microcircuits formed to extract optimal information about the environment. These cortical microcircuits are composed of excitatory and inhibitory neurons as shown in Figure 4.

Figure 4.Local Microcircuit in Barrel cortex. Left: schematic representation of the cortical layers (barrels within L4 in cyan ) with examples of typical dendritic morphologies of excitatory cortical neurons (in red , an L2 neuron; in violet , a spiny stellate L4 cell; in green , an L5B pyramidal neuron). Right: schematic representation of the main excitatory connections between cortical layers within a barrel column (black).

Learning whisker based object discrimination & texture differentiationEdit

Rodents move their sensors to collect information, and these movements are guided by sensory input. When action sequences are required to achieve success in novel tasks, interactions between movement and sensation underlie motor control [4] and complex learned behaviours [5]. The motor cortex has important roles in learning motor skills [6-9], but its function in learning sensorimotor associations is unknown. The neural circuits underlying sensorimotor integration are beginning to be mapped. Different motor cortex layers harbour excitatory neurons with distinct inputs and projections [10-12]. Outputs to motor centres in the brain stem and spinal cord arise from pyramidal tract-type neurons in layer 5B (L5B). Within motor cortex, excitation descends from L2/3 to L5 [13, 14]. Input from somatosensory cortex impinges preferentially onto L2/3 neurons. L2/3 neurons [10] therefore directly link somatosensation and control of movements. In one of the recent studies [15], mice were trained head fixed in a vibrissa-based object-detection task while imaging populations of neurons [16]. Following a sound, a pole was moved to one of several target positions within reach of the whiskers (the ‘go’ stimulus) or to an out-of-reach position (the ‘no-go’ stimulus). Target and out-of-reach locations were arranged along the anterior–posterior axis; the out-of reach position was most anterior. Mice searched for the pole with one whisker row, the C row, and reported the pole as ‘present’ by licking, or ‘not present’ by withholding licking. Licking on go trials (hit) was rewarded with water, whereas licking on no-go trials (false alarm) was punished with a time-out during which the trial was stopped for 2 seconds. Trials without licking (no-go, correct rejection, go, and miss) were not rewarded or punished. All mice showed learning within the first two or three sessions. Performance reached expert levels after three to six training sessions. Learning the behavioural task was directly dependent on the motor related behaviour. Naive mice whisked occasionally in a manner unrelated to trail structure. Thus, object detection relies on a sequence of actions, linked by sensory cues. An auditory cue triggers whisking during the sampling period. Contact between whisker and object causes licking for a water reward during a response period. Silencing vM1 indicates that this task requires the motor cortex; with vM1 silenced, task-dependent whisking persisted, but was reduced in amplitude and repeatability, and task performance dropped.

Neural Correlates of Sensorimotor learning mechanismEdit

Coding of touch in the motor cortex is consistent with direct input from vS1 to the imaged neurons. A model based on population coding of individual behavioural features also predicted motor behaviours. Accurate decoding of whisking amplitude, whisking set-point and lick rate suggests that vM1 controls these slowly varying motor parameters, as expected from previous motor cortex and neurophysiological experiments.


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Last modified on 28 August 2013, at 10:42