Sensory Systems/Somatosensory System
- 1 Introduction
- 2 Anatomy of the Somatosensory System
- 3 Proprioceptive Signal Processing
- 4 Modelling muscle spindles and afferent response
- 5 Internal models of limb dynamics
- 6 Somatosensory Perception of Whiskers
- 6.1 Introduction
- 6.2 Pathways carrying whisker information to Barrel Cortex
- 6.3 Whisker information processing in Barrel Cortex with specialized local microcircuit
- 6.4 Learning whisker based object discrimination & texture differentiation
- 6.5 Neural Correlates of Sensorimotor learning mechanism
- 6.6 References
Anatomy of the Somatosensory System
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.)
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.
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.
- 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.
- 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).
- 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 organ
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.
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 Processing
Modelling muscle spindles and afferent response
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) model
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 fibre is mainly responsible for detecting dynamic fusimotor input, while the 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 and chain fibre responses.
Hasan (1983) model
Another comprehensive model of muscle spindles was proposed by Hasan in 1983 . 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. The lengths of the two regions can be labelled for the sensory and for the non-sensory region. The tension 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 (equation (2) derived by Hasan).
The total length of the muscle spindle, x(t) is the sum of the length of the two regions (equation (4)).
Using this substitution and rearranging, we can derive the following expression for the length of the sensory zone (equation (5)):
Here, parameter represents the sensitivity of the tension to to velocity in the non-sensory zone, parameter and parameter 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, in Hasan's model depends on a combination of the sensory zone length and its first derivative (equation (6)), with an experimentally derived weighting.
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||Gamma - dynamic||0.1||125||-15|
|Primary||Gamma - static||100||100||-25|
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 dynamics
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:
Here each basis 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 Whiskers
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 . 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 .
Pathways carrying whisker information to Barrel Cortex
Whisker information processing in Barrel Cortex with specialized local microcircuit
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 .
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.
Learning whisker based object discrimination & texture differentiation
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  and complex learned behaviours . 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  therefore directly link somatosensation and control of movements. In one of the recent studies , mice were trained head fixed in a vibrissa-based object-detection task while imaging populations of neurons . 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 mechanism
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.
1 Feldmeyer D, Brecht M, Helmchen F, Petersen CCH, Poulet JFA, Staiger JF, Luhmann HJ, Schwarz C."Barrel cortex function" Progress in Neurobiology 2013, 103 : 3-27.
2 Lahav O, Mioduser D. "Multisensory virtual environment for supporting blind persons' acquisition of spatial cognitive mapping, orientation, and mobility skills." Academia.edu 2002.
3 Alloway KD. "Information processing streams in rodent barrel cortex: The differential functions of barrel and septal circuits." Cereb Cortex 2008, 18(5):979-989.
4 Scott SH. "Inconvenient truths about neural processing in primary motor cortex." The Journal of physiology 2008, 586(5):1217-1224.
5 Wolpert DM, Diedrichsen J, Flanagan JR. "Principles of sensorimotor learning." Nature reviews Neuroscience 2011, 12(12):739-751.
6 Wise SP, Moody SL, Blomstrom KJ, Mitz AR. "Changes in motor cortical activity during visuomotor adaptation." Experimental brain research Experimentelle Hirnforschung Experimentation cerebrale 1998, 121(3):285-299.
7 Rokni U, Richardson AG, Bizzi E, Seung HS. "Motor learning with unstable neural representations." Neuron 2007, 54(4):653-666.
8 Komiyama T, Sato TR, O'Connor DH, Zhang YX, Huber D, Hooks BM, Gabitto M, Svoboda K. "Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice." Nature 2010, 464(7292):1182-1186.
9 Hosp JA, Pekanovic A, Rioult-Pedotti MS, Luft AR. "Dopaminergic projections from midbrain to primary motor cortex mediate motor skill learning." The Journal of neuroscience : the official journal of the Society for Neuroscience 2011, 31(7):2481-2487.
10 Keller A. "Intrinsic synaptic organization of the motor cortex." Cereb Cortex 1993, 3(5):430-441.
11 Mao T, Kusefoglu D, Hooks BM, Huber D, Petreanu L, Svoboda K. "Long-range neuronal circuits underlying the interaction between sensory and motor cortex." Neuron 2011, 72(1):111-123.
12 Hooks BM, Hires SA, Zhang YX, Huber D, Petreanu L, Svoboda K, Shepherd GM. "Laminar analysis of excitatory local circuits in vibrissal motor and sensory cortical areas." PLoS biology 2011, 9(1):e1000572.
13 Anderson CT, Sheets PL, Kiritani T, Shepherd GM. "Sublayer-specific microcircuits of corticospinal and corticostriatal neurons in motor cortex." Nature neuroscience 2010, 13(6):739-744.
14 Kaneko T, Cho R, Li Y, Nomura S, Mizuno N. "Predominant information transfer from layer III pyramidal neurons to corticospinal neurons." The Journal of comparative neurology 2000, 423(1):52-65.
15 O'Connor DH, Clack NG, Huber D, Komiyama T, Myers EW, Svoboda K. "Vibrissa-based object localization in head-fixed mice." The Journal of neuroscience : the official journal of the Society for Neuroscience 2010, 30(5):1947-1967.
16 O'Connor DH, Peron SP, Huber D, Svoboda K. "Neural activity in barrel cortex underlying vibrissa-based object localization in mice." Neuron 2010, 67(6):1048-1061.
17 Shaner NC, Campbell RE, Steinbach PA, Giepmans BN, Palmer AE, Tsien RY. "Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein." Nature biotechnology 2004, 22(12):1567-1572.
18 Tian L, Hires SA, Mao T, Huber D, Chiappe ME, Chalasani SH, Petreanu L, Akerboom J, McKinney SA, Schreiter ER. "Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators." Nature methods 2009, 6(12):875-881.