Parallel coding schemes of whisker velocity in the rat's somatosensory system

Enhanced Release Probability without Changes in Synaptic Delay during Analogue-Digital Facilitation

Neuronal timing with millisecond precision is critical for many brain functions such as sensory perception, learning and memory formation. At the level of the chemical synapse, the synaptic delay is determined by the presynaptic release probability (Pr) and the waveform of the presynaptic action potential (AP). For instance, paired-pulse facilitation or presynaptic long-term potentiation are associated with reductions in the synaptic delay, whereas paired-pulse depression or presynaptic long-term depression are associated with an increased synaptic delay. Parallelly, the AP broadening that results from the inactivation of voltage gated potassium (Kv) channels responsible for the repolarization phase of the AP delays the synaptic response, and the inactivation of sodium (Nav) channels by voltage reduces the synaptic latency. However, whether synaptic delay is modulated during depolarization-induced analogue-digital facilitation (d-ADF), a form of context-dependent synaptic facilitation induced by prolonged depolarization of the presynaptic neuron and mediated by the voltage-inactivation of presynaptic Kv1 channels, remains unclear. We show here that despite Pr being elevated during d-ADF at pyramidal L5-L5 cell synapses, the synaptic delay is surprisingly unchanged. This finding suggests that both Pr- and AP-dependent changes in synaptic delay compensate for each other during d-ADF. We conclude that, in contrast to other short- or long-term modulations of presynaptic release, synaptic timing is not affected during d-ADF because of the opposite interaction of Pr- and AP-dependent modulations of synaptic delay.

Reducing Merkel cell activity in the whisker follicle disrupts cortical encoding of whisker movement amplitude and velocity

Merkel cells (MCs) and associated primary sensory afferents of the whisker follicle-sinus complex, accurately code whisker self-movement, angle, and whisk phase during whisking. However, little is known about their roles played in cortical encoding of whisker movement. To this end, the spiking activity of primary somatosensory barrel cortex (wS1) neurons was measured in response to varying the whisker deflection amplitude and velocity in transgenic mice with previously established reduced mechanoelectrical coupling at MC-associated afferents. Under reduced MC activity, wS1 neurons exhibited increased sensitivity to whisker deflection. This appeared to arise from a lack of variation in response magnitude to varying the whisker deflection amplitude and velocity. This latter effect was further indicated by weaker variation in the temporal profile of the evoked spiking activity when either whisker deflection amplitude or velocity was varied. Nevertheless, under reduced MC activity, wS1 neurons retained the ability to differentiate stimulus features based on the timing of their first post-stimulus spike. Collectively, results from this study suggest that MCs contribute to cortical encoding of both whisker amplitude and velocity, predominantly by tuning wS1 response magnitude, and by patterning the evoked spiking activity, rather than by tuning wS1 response latency.

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The role of Merkel cells (MCs) in cortical encoding of whisker deflection amplitude and velocity was investigated.

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Reducing MC synaptic activity increased barrel cortex neurons response sensitivity to whisker deflection.

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This effect occurred from a lack of variation in response magnitude to varying whisker deflection amplitude and velocity.

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However, stimuli differentiation through changes in cortical response latency was preserved.

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MCs are thus suggested to play a predominant role in tuning the cortical response magnitude over the response latency.

Sensory Adaptation in the Whisker-Mediated Tactile System: Physiology, Theory, and Function

In the natural environment, organisms are constantly exposed to a continuous stream of sensory input. The dynamics of sensory input changes with organism's behaviour and environmental context. The contextual variations may induce >100-fold change in the parameters of the stimulation that an animal experiences. Thus, it is vital for the organism to adapt to the new diet of stimulation. The response properties of neurons, in turn, dynamically adjust to the prevailing properties of sensory stimulation, a process known as "neuronal adaptation." Neuronal adaptation is a ubiquitous phenomenon across all sensory modalities and occurs at different stages of processing from periphery to cortex. In spite of the wealth of research on contextual modulation and neuronal adaptation in visual and auditory systems, the neuronal and computational basis of sensory adaptation in somatosensory system is less understood. Here, we summarise the recent finding and views about the neuronal adaptation in the rodent whisker-mediated tactile system and further summarise the functional effect of neuronal adaptation on the response dynamics and encoding efficiency of neurons at single cell and population levels along the whisker-mediated touch system in rodents. Based on direct and indirect pieces of evidence presented here, we suggest sensory adaptation provides context-dependent functional mechanisms for noise reduction in sensory processing, salience processing and deviant stimulus detection, shift between integration and coincidence detection, band-pass frequency filtering, adjusting neuronal receptive fields, enhancing neural coding and improving discriminability around adapting stimuli, energy conservation, and disambiguating encoding of principal features of tactile stimuli.

Continuous, multidimensional coding of 3D complex tactile stimuli by primary sensory neurons of the vibrissal system

Across all sensory modalities, first-stage sensory neurons are an information bottleneck: they must convey all information available for an animal to perceive and act in its environment. Our understanding of coding properties of primary sensory neurons in the auditory and visual systems has been aided by the use of increasingly complex, naturalistic stimulus sets. By comparison, encoding properties of primary somatosensory afferents are poorly understood. Here, we use the rodent whisker system to examine how tactile information is represented in primary sensory neurons of the trigeminal ganglion (Vg). Vg neurons have long been thought to segregate into functional classes associated with separate streams of information processing. However, this view is based on Vg responses to restricted stimulus sets which potentially underreport the coding capabilities of these neurons. In contrast, the current study records Vg responses to complex three-dimensional (3D) stimulation while quantifying the complete 3D whisker shape and mechanics, thereby beginning to reveal their full representational capabilities. The results show that individual Vg neurons simultaneously represent multiple mechanical features of a stimulus, do not preferentially encode principal components of the stimuli, and represent continuous and tiled variations of all available mechanical information. These results directly contrast with proposed codes in which subpopulations of Vg neurons encode select stimulus features. Instead, individual Vg neurons likely overcome the information bottleneck by encoding large regions of a complex sensory space. This proposed tiled and multidimensional representation at the Vg directly constrains the computations performed by more central neurons of the vibrissotrigeminal pathway.

Laminar-specific encoding of texture elements in rat barrel cortex

KEY POINTS

For rats texture discrimination is signalled by the large face whiskers by stick-slip events. Neural encoding of repetitive stick-slip events will be influenced by intrinsic properties of adaptation. We show that texture coding in the barrel cortex is laminar specific and follows a power function. Our results also show layer 2 codes for novel feature elements via robust firing rates and temporal fidelity. We conclude that texture coding relies on a subtle neural ensemble to provide important object information.

ABSTRACT

Texture discrimination by rats is exquisitely guided by fine-grain mechanical stick-slip motions of the face whiskers as they encounter, stick to and slip past successive texture-defining surface features such as bumps and grooves. Neural encoding of successive stick-slip texture events will be shaped by adaptation, common to all sensory systems, whereby receptor and neural responses to a stimulus are affected by responses to preceding stimuli, allowing resetting to signal novel information. Additionally, when a whisker is actively moved to contact and brush over surfaces, that motion itself generates neural responses that could cause adaptation of responses to subsequent stick-slip events. Nothing is known about encoding in the rat whisker system of stick-slip events defining textures of different grain or the influence of adaptation from whisker protraction or successive texture-defining stick-slip events. Here we recorded responses from halothane-anaesthetized rats in response to texture-defining stimuli applied to passive whiskers. We demonstrate that: across the columnar network of the whisker-recipient barrel cortex, adaptation in response to repetitive stick-slip events is strongest in uppermost layers and equally lower thereafter; neither whisker protraction speed nor stick-slip frequency impede encoding of stick-slip events at rates up to 34.08 Hz; and layer 2 normalizes responses to whisker protraction to resist effects on texture signalling. Thus, within laminar-specific response patterns, barrel cortex reliably encodes texture-defining elements even to high frequencies.

Fractional-order leaky integrate-and-fire model with long-term memory and power law dynamics

Pyramidal neurons produce different spiking patterns to process information, communicate with each other and transform information. These spiking patterns have complex and multiple time scale dynamics that have been described with the fractional-order leaky integrate-and-Fire (FLIF) model. Models with fractional (non-integer) order differentiation that generalize power law dynamics can be used to describe complex temporal voltage dynamics. The main characteristic of FLIF model is that it depends on all past values of the voltage that causes long-term memory. The model produces spikes with high interspike interval variability and displays several spiking properties such as upward spike-frequency adaptation and long spike latency in response to a constant stimulus. We show that the subthreshold voltage and the firing rate of the fractional-order model make transitions from exponential to power law dynamics when the fractional order α decreases from 1 to smaller values. The firing rate displays different types of spike timing adaptation caused by changes on initial values. We also show that the voltage-memory trace and fractional coefficient are the causes of these different types of spiking properties. The voltage-memory trace that represents the long-term memory has a feedback regulatory mechanism and affects spiking activity. The results suggest that fractional-order models might be appropriate for understanding multiple time scale neuronal dynamics. Overall, a neuron with fractional dynamics displays history dependent activities that might be very useful and powerful for effective information processing.

Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems

Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences of chaos for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggesting that small changes in stimuli will be obscured by intrinsically generated variability. On the other hand, recent work shows that the type of chaos that occurs in spiking networks can have a surprisingly low-dimensional structure, suggesting that there may be room for fine stimulus features to be precisely resolved. Here we show that strongly chaotic networks produce patterned spikes that reliably encode time-dependent stimuli: using a decoder sensitive to spike times on timescales of 10’s of ms, one can easily distinguish responses to very similar inputs. Moreover, recurrence serves to distribute signals throughout chaotic networks so that small groups of cells can encode substantial information about signals arriving elsewhere. A conclusion is that the presence of strong chaos in recurrent networks need not exclude precise encoding of temporal stimuli via spike patterns.

Recurrently connected populations of excitatory and inhibitory neurons found in cortex are known to produce rich and irregular spiking activity, with complex trial-to-trial variability in response to input stimuli. Many theoretical studies found this firing regime to be associated with chaos, where tiny perturbations explode to impact subsequent neural activity. As a result, the precise spiking patterns produced by such networks would be expected to be too fragile to carry any valuable information about stimuli, since inevitable sources of noise such as synaptic failure or ion channel fluctuations would be amplified by chaotic dynamics on repeated trials. In this article we revisit the implications of chaos in input-driven networks and directly measure its impact on evoked population spike patterns. We find that chaotic network dynamics can, in fact, produce highly patterned spiking activity which can be used by a simple decoder to perform input-classification tasks. This can be explained by the presence of low-dimensional, input-specific chaotic attractors, leading to a form of trial-to-trial variability that is intermittent, rather than uniformly random. We propose that chaos is a manageable by-product of recurrent connectivity, which serves to efficiently distribute information about stimuli throughout a network.

Whisking Kinematics Enables Object Localization in Head-Centered Coordinates Based on Tactile Information from a Single Vibrissa

During active tactile exploration with their whiskers (vibrissae), rodents can rapidly orient to an object even though there are very few proprioceptors in the whisker muscles. Thus a long-standing question in the study of the vibrissal system is how the rat can localize an object in head-centered coordinates without muscle-based proprioception. We used a three-dimensional model of whisker bending to simulate whisking motions against a peg to investigate the possibility that the 3D mechanics of contact from a single whisker are sufficient for localization in head-centered coordinates. Results show that for nearly all whiskers in the array, purely tactile signals at the whisker base - as would be measured by mechanoreceptors, in whisker-centered coordinates - could be used to determine the location of a vertical peg in head-centered coordinates. Both the "roll" and the "elevation" components of whisking kinematics contribute to the uniqueness and resolution of the localization. These results offer an explanation for a behavioral study showing that rats can more accurately determine the horizontal angle of an object if one column, rather than one row, of whiskers is spared.

Decoupling kinematics and mechanics reveals coding properties of trigeminal ganglion neurons in the rat vibrissal system

Tactile information available to the rat vibrissal system begins as external forces that cause whisker deformations, which in turn excite mechanoreceptors in the follicle. Despite the fundamental mechanical origin of tactile information, primary sensory neurons in the trigeminal ganglion (Vg) have often been described as encoding the kinematics (geometry) of object contact. Here we aimed to determine the extent to which Vg neurons encode the kinematics vs. mechanics of contact. We used models of whisker bending to quantify mechanical signals (forces and moments) at the whisker base while simultaneously monitoring whisker kinematics and recording single Vg units in both anesthetized rats and awake, body restrained rats. We employed a novel manual stimulation technique to deflect whiskers in a way that decouples kinematics from mechanics, and used Generalized Linear Models (GLMs) to show that Vg neurons more directly encode mechanical signals when the whisker is deflected in this decoupled stimulus space.

DOI:http://dx.doi.org/10.7554/eLife.13969.001

Animals must gather sensory information from the world around them and act on that information. Specialized sensory cells convert physical information from the environment into electrical signals that the brain can interpret. In the case of hearing, this physical information consists of changes in air pressure, and for vision, it is patterns of light bouncing off of objects.

Rodents rely heavily on touch information from their whiskers to explore their world. When a whisker touches an object, it deforms and bends. The first neurons to respond to whisker touch – so called primary sensory neurons – represent contact between the whisker and the object in the form of electrical signals, but exactly how they do this is unclear.

One possibility is that primary sensory neurons encode the movement of the whisker itself. Whenever a whisker touches an object, the whisker is deflected in a particular direction by a particular amount and at a particular speed. These movement-related features are referred to as the “kinematic” properties of whisker-object contact. Alternatively, these whisker sensory neurons might be more concerned with the forces at the base of the whisker caused by object contact. These forces are the “mechanical” properties of whisker-object contact.

Bush, Schroeder et al. set out to determine whether the electrical response of these whisker sensory neurons mainly encode kinematic or mechanical information. However, these two types of information are often closely related to each other: put simply, small whisker movements tend to accompany small forces and vice versa. Bush, Schroeder et al. therefore devised a method to deliver touch stimuli to the whiskers in a way that separates kinematic from mechanical information. Mathematical models were then developed to compare how well the neurons represent each type of information. The models showed that whisker sensory neurons generally encode mechanical signals more directly than kinematic ones.

This information adds to our understanding of how animals learn about the world through their senses. However, the analysis of Bush, Schroeder et al. relies on the long-standing simplification that whisker motion is two-dimensional, whereas in reality whiskers move in three dimensions. Therefore, a future challenge is to examine how sensory neurons represent information about touch, such as the location or shape of an object, during three-dimensional whisker-object contact.

DOI:http://dx.doi.org/10.7554/eLife.13969.002