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Sensing the Environment With Whiskers  

Mathew H. Evans, Michaela S.E. Loft, Dario Campagner, and Rasmus S. Petersen

Whiskers (vibrissae) are prominent on the snout of many mammals, both terrestrial and aquatic. The defining feature of whiskers is that they are rooted in large follicles with dense sensory innervation, surrounded by doughnut-shaped blood sinuses. Some species, including rats and mice, have elaborate muscular control of their whiskers and explore their environment by making rhythmic back-and-forth “whisking” movements. Whisking movements are purposefully modulated according to specific behavioral goals (“active sensing”). The basic whisking rhythm is controlled by a premotor complex in the intermediate reticular formation. Primary whisker neurons (PWNs), with cell bodies in the trigeminal ganglion, innervate several classes of mechanoreceptive nerve endings in the whisker follicle. Mechanotransduction involving Piezo2 ion channels establishes the fundamental physical signals that the whiskers communicate to the brain. PWN spikes are triggered by mechanical forces associated with both the whisking motion itself and whisker-object contact. Whisking is associated with inertial and muscle contraction forces that drive PWN activity. Whisker-object contact causes whiskers to bend, and PWN activity is driven primarily by the associated rotatory force (“bending moment”). Sensory signals from the PWNs are routed to many parts of the hindbrain, midbrain, and forebrain. Parallel ascending pathways transmit information about whisker forces to sensorimotor cortex. At each brainstem, thalamic, and cortical level of these pathways, there are one or more maps of the whisker array, consisting of cell clusters (“barrels” in the primary somatosensory cortex) whose spatial arrangement precisely mirrors that of the whiskers on the snout. However, the overall architecture of the whisker-responsive regions of the brain system is best characterized by multilevel sensory-motor feedback loops. Its intriguing biology, in combination with advantageous properties as a model sensory system, has made the whisker system the platform for seminal insights into brain function.


The Sensory World of the Naked Mole-Rat  

Thomas J. Park

Naked mole-rats are subterranean mammals that are native to equatorial east Africa including Ethiopia, Somalia, and Kenya. They are unusual among subterranean mammals in that they live in very large colonies where many respiring animals deplete oxygen and overproduce carbon dioxide. Some of their sensory traits, such as poor vision and hearing, are considered typical of subterranean mammals. However, naked mole-rats display three sensory traits that are unusual even among subterranean mammals. First, they possess a sensitive sensory array of body vibrissae on their otherwise furless bodies. Second, they have a greatly reduced sense of inflammatory and chemical pain, but express acute mechanical and thermal pain. Third, naked mole-rats, and likely other African mole-rat species, are the only rodents known that show no postbirth growth of the vomeronasal organ, an organ associated with sensing pheromones. These sensory traits, along with extreme tolerance to hypoxia and resistance to cancer, make the naked mole-rat an important animal model for studying sensory systems as well as in multiple other scientific fields.


Single Neuron Computational Modeling  

Yeonjoo Yoo and Fabrizio Gabbiani

Computational modeling is essential to understand how the complex dendritic structure and membrane properties of a neuron process input signals to generate output signals. Compartmental models describe how inputs, such as synaptic currents, affect a neuron’s membrane potential and produce outputs, such as action potentials, by converting membrane properties into the components of an electrical circuit. The simplest such model consists of a single compartment with a leakage conductance which represents a neuron having spatially uniform membrane potential and a constant conductance summarizing the combined effect of every ion flowing across the neuron’s membrane. The Hodgkin-Huxley model introduces two additional active channels; the sodium channel and the delayed rectifier potassium channel whose associated conductances change depending on the membrane potential and that are described by an additional set of three nonlinear differential equations. Since its conception in 1952, many kinds of active channels have been discovered with a variety of characteristics that can successfully be modeled within the same framework. As the membrane potential varies spatially in a neuron, the next refinement consists in describing a neuron as an electric cable to account for membrane potential attenuation and signal propagation along dendritic or axonal processes. A discrete version of the cable equation results in compartments with possibly different properties, such as different types of ion channels or spatially varying maximum conductances to model changes in channel densities. Branching neural processes such as dendrites can be modeled with the cable equation by considering the junctions of cables with different radii and electrical properties. Single neuron computational models are used to investigate a variety of topics and reveal insights that cannot be evidenced directly by experimental observation. Studies on action potential initiation and on synaptic integration provide prototypical examples illustrating why computational models are essential. Modeling action potential initiation constrains the localization and density of channels required to reproduce experimental observations, while modeling synaptic integration sheds light on the interaction between the morphological and physiological characteristics of dendrites. Finally, reduced compartmental models demonstrate how a simplified morphological structure supplemented by a small number of ion channel-related variables can provide clear explanations about complex intracellular membrane potential dynamics.


Somatosensory Specializations in Mammals  

Jon H. Kaas

Early mammals were small with little neocortex that included a somatosensory system with a mediolateral strip of primary somatosensory cortex and three or four adjoining somatosensory fields. As early mammals radiated out and adapted to local environments, their somatosensory systems adjusted and became specialized in many ways. Most of these specializations were most obvious as disproportionally enlarged representations of important sensory surfaces of the skin in primary somatosensory cortex. These enlarged representations included those of the bill of the duckbilled platypus, the nose of the star-nosed mole, the teeth and tongue of monkeys, the glabrous hand of raccoons, the wing of bats, and the tactile tail of some monkeys. These and other specializations enhanced the ability of these mammals to adapt to their environments and to precisely evaluate relevant sensory events and make appropriate behavioral adjustments.


Somatosensory System Organization in Mammals and Response to Spinal Injury  

Corinna Darian-Smith and Karen Fisher

Spinal cord injury (SCI) affects well over a million people in the United States alone, and its personal and societal costs are huge. This article provides a current overview of the organization of somatosensory and motor pathways, in the context of hand/paw function in nonhuman primate and rodent models of SCI. Despite decades of basic research and clinical trials, therapeutic options remain limited. This is largely due to the fact that (i) spinal cord structure and function is very complex and still poorly understood, (ii) there are many species differences which can make translation from the rodent to primate difficult, and (iii) we are still some way from determining the detailed multilevel pathway responses affecting recovery. There has also been little focus, until recently, on the sensory pathways involved in SCI and recovery, which are so critical to hand function and the recovery process. The potential for recovery in any individual depends on many factors, including the location and size of the injury, the extent of sparing of fiber tracts, and the post-injury inflammatory response. There is also a progression of change over the first weeks and months that must be taken into account when assessing recovery. There are currently no good biomarkers of recovery, and while axon terminal sprouting is frequently used in the experimental setting as an indicator of circuit remodeling and “recovery,” the correlation between sprouting and functional recovery deserves scrutiny.


Stereopsis and Depth Perception  

Andrew J. Parker

Humans and some animals can use their two eyes in cooperation to detect and discriminate parts of the visual scene based on depth. Owing to the horizontal separation of the eyes, each eye obtains a slightly different view of the scene in front of the head. These small differences are processed by the nervous system to generate a sense of binocular depth. As humans, we experience an impression of solidity that is fully three-dimensional; this impression is called stereopsis and is what we appreciate when we watch a 3D movie or look into a stereoscopic viewer. While the basic perceptual phenomena of stereoscopic vision have been known for some time, it is mainly within the last 50 years that we have gained an understanding of how the nervous system delivers this sense of depth. This period of research began with the identification of neuronal signals for binocular depth in the primary visual cortex. Building on that finding, subsequent work has traced the signaling pathways for binocular stereoscopic depth forward into extrastriate cortex and further on into cortical areas concerning with sensorimotor integration. Within these pathways, neurons acquire sensitivity to more complex, higher order aspects of stereoscopic depth. Signals relating to the relative depth of visual features can be identified in the extrastriate cortex, which is a form of selectivity not found in the primary visual cortex. Over the same time period, knowledge of the organization of binocular vision in animals that inhabit a wide diversity of ecological niches has substantially increased. The implications of these findings for developmental and adult plasticity of the visual nervous system and onset of the clinical condition of amblyopia are explored in this article. Amblyopic vision is associated with a cluster of different visual and oculomotor symptoms, but the loss of high-quality stereoscopic depth performance is one of the consistent clinical features. Understanding where and how those losses occur in the visual brain is an important goal of current research, for both scientific and clinical reasons.


Synaptic Properties of Sensory Thalamus  

Martha E. Bickford

Detailed studies of thalamic circuits have revealed many features that are shared across nuclei. For example, glutamatergic inputs to the thalamus can be placed into three categories based on the size of the synaptic terminals they form, their synaptic arrangements, and the postsynaptic responses they elicit. Remarkably, these three categories can be identified in most sensory nuclei of the dorsal thalamus. Likewise, in most sensory thalamic nuclei, circuits that release the neurotransmitter gamma aminobutyric acid (GABA) can be placed into two general categories based on their dendritic or axonal origins. Finally, similar cholinergic circuits have been identified across thalamic nuclei. The ultimate goal of examining the shared versus diverse features of thalamic circuits is to identify fundamental modules, mechanisms, and/or conceptual frameworks, in order to decipher thalamic function.


Synesthesia and Sensory Processing  

Louisa J. Rinaldi

Synesthesia is a neurodevelopmental condition that causes 4.4% of the population to experience the world differently. For these individuals certain stimuli (e.g., letters of the alphabet) trigger a secondary experience (e.g., color perception). This process is automatic and remains consistent over time. Tests for measuring synesthesia have successfully built on this principle of synesthetic associations being consistent over time, and using this method a number of studies have investigated the heritability of the condition, cognitive differences that synesthetes have compared with non-synesthetes, and the neurological architecture of synesthete brains. These measures have largely focused on adult synesthetes for whom the condition is already fully developed. Since 2009 researchers have begun to also investigate childhood synesthesia, which has helped to advance our understanding of how this condition emerges. Drawing on both adult and child studies, we can better understand the neurological and cognitive implications of a lifetime of experiencing synesthetic associations.


Taste Buds and Gustatory Transduction: A Functional Perspective  

Alan C. Spector and Susan P. Travers

Everything a person swallows must pass a final chemical analysis by the sensory systems of the mouth; of these, the gustatory system is cardinal. Gustation can be heuristically divided into three basic domains of function: sensory-discriminative (quality and intensity), motivational/affective (promote or deter ingestion), and physiological (e.g., salivation and insulin release). The signals from the taste buds, transmitted to the brain through the sensory branches of cranial nerves VII (facial), IX (glossopharyngeal), and X (vagal), subserve these primary functions. Taste buds are collections of 50–100 cells that are distributed in various fields in the tongue, soft palate, and throat. There are three types of cells that have been identified in taste buds based on their morphological and cytochemical expression profiles. Type II cells express specialized G-protein-coupled receptors (GPCR or GPR) on their apical membranes, which protrude through a break in the oral epithelial lining called the taste pore, that are responsible for the sensing of sweeteners (via the taste type 1 receptor (T1R) 2 + T1R3), amino acids (via the T1R1+T1R3), and bitter ligands (via the taste type 2 receptors (T2Rs)). Type III cells are critical for the sensing of acids via the otopetrin-1 (Otop-1) ion channel. The sensing of sodium, in at least rodents, occurs through the epithelial sodium channel (ENaC), but the exact composition of this channel and the type of taste cell type in which the functional version resides remains unclear. It is controversial whether Type I cells, which have been characterized as glial-like, are involved in sodium transduction or play any taste signaling role. For the most part, receptors for different stimulus classes (e.g., sugars vs. bitter ligands) are not co-expressed, providing significant early functionally related segregation of signals. There remains a persistent search for yet to be identified receptors that may contribute to some functions associated with stimuli representing the so-called basic taste qualities—sweet, salty, sour, bitter, and umami—as well as unconventional stimuli such as fatty acids (in addition to cluster of differentiation-36 (CD-36), GPR40, and GPR120) and maltodextrins. The primary neurotransmitter in taste receptor cells is ATP, which is released through a voltage-gated heteromeric channel consisting of the calcium homeostasis modulator 1 and 3 (CALHM1/3) and binds with P2X2/X3 receptors on apposed afferent fibers. Serotonin released from Type III cells has been implicated as an additional neurotransmitter, binding with HT3a receptors, and possibly playing a role in acid taste (which is sour to humans). Taste bud cells undergo complete turnover about every two weeks. Although there remains much to be understood about the operations of the taste bud, perhaps the one very clear principle that emerges is that the organization of signals transmitted to the brain is not random and arbitrary to be decoded by complex algorithms in the circuits of the central gustatory system. Rather, the transmission of taste information from the periphery is highly ordered.


Thalamocortical Interactions for Sensory Processing  

Jose M. Alonso and Harvey A. Swadlow

The thalamocortical pathway is the main route of sensory information to the cerebral cortex. Vision, touch, hearing, taste, and balance all depend on the integrity of this pathway that connects the thalamic structures receiving sensory input with the cortical areas specialized in each sensory modality. Only the ancient sense of smell is independent of the thalamus, gaining access to cortex through more anterior routes. While the thalamocortical pathway targets different layers of the cerebral cortex, its main stream projects to the middle layers and has axon terminals that are dense, spatially restricted, and highly specific in their connections. The remarkable specificity of these thalamocortical connections allows for a precise reconstruction of the sensory dimensions that need to be most finely sampled, such as spatial acuity in vision and sound frequency in hearing. The thalamic axon terminals also segregate topographically according to their stimulus preferences, providing a simple principle to build cortical sensory maps: neighboring values in sensory space are represented by neighboring points within the cortex. Thalamocortical processing is not static. It is continuously modulated by the brain stem and corticothalamic feedback based on the level of attention and alertness, and during sleep or general anesthesia. When alert, visual thalamic responses become stronger, more reliable, more sustained, more effective at sampling fast changes in the scene, and more linearly related to the stimulus. The high firing rates of the alert state make thalamocortical synapses chronically depressed and excitatory synaptic potentials less dependent on temporal history, improving even further the linear relation between stimulus and response. In turn, when alertness wanes, the thalamus reduces its firing rate, and starts generating spike bursts that drive large postsynaptic responses and keep the cortex responsive to sudden stimulus changes.


Transcriptomic Architecture of Reproductive Plasticity  

Susan C. P. Renn and Nadia Aubin-Horth

Several species show diversity in reproductive patterns that result from phenotypic plasticity. This reproductive plasticity is found for example in mate choice, parental care, reproduction suppression, reproductive tactics, sex role, and sex reversal. Studying the genome-wide changes in transcription that are associated with these plastic phenotypes will help answer several questions, including those regarding which genes are expressed and where they are expressed when an individual is faced with a reproductive choice, as well as those regarding whether males and females have the same brain genomic signature when they express the same behaviors, or if they activate sex-specific molecular pathways to output similar behavioral responses. The comparative approach of studying transcription in a wide array of species allows us to uncover genes, pathways, and biological functions that are repeatedly co-opted (“genetic toolkit”) as well as those that are unique to a particular system (“genomic signature”). Additionally, by quantifying the transcriptome, a labile trait, using time series has the potential to uncover the causes and consequences of expressing one plastic phenotype or another. There are of course gaps in our knowledge of reproductive plasticity, but no shortage of possibilities for future directions.


Understanding How Humans Learn and Adapt to Changing Environments  

Daphne Bavelier and Aaron Cochrane

Compared to other animals or to artificial agents, humans are unique in the extent of their abilities to learn and adapt to changing environments. When focusing on skill learning and model-based approaches, learning can be conceived as a progression of increasing, then decreasing, dimensions of representing knowledge. First, initial learning demands exploration of the learning space and the identification of the relevant dimensions for the novel task at hand. Second, intermediate learning requires a refinement of these relevant dimensions of knowledge and behavior to continue improving performance while increasing efficiency. Such improvements utilize chunking or other forms of dimensionality reduction to diminish task complexity. Finally, late learning ensures automatization of behavior through habit formation and expertise development, thereby reducing the need to effortfully control behavior. While automatization greatly increases efficiency, there is also a trade-off with the ability to generalize, with late learning tending to be highly specific to the learned features and contexts. In each of these phases a variety of interacting factors are relevant: Declarative instructions, prior knowledge, attentional deployment, and cognitive fitness have unique roles to play. Neural contributions to processes involved also shift from earlier to later points in learning as effortfulness initially increases and then gives way to automaticity. Interestingly, video games excel at providing uniquely supportive environments to guide the learner through each of these learning stages. This fact makes video games a useful tool for both studying learning, due to their engaging nature and dynamic range of complexity, as well as engendering learning in domains such as education or cognitive training.


Vision and Art  

Bevil R. Conway

The premise of the field of vision and art is that studies of visual processing can inform an understanding of visual art and artistic practice, and a close reading of art, art history, and art practice can help generate hypotheses about how vision works. Paraphrasing David Hubel, visual neurobiology can enhance art just as knowledge of bones and muscles has for centuries informed artistic representations of the body. The umbrella of visual art encompasses a bewildering diversity of works. A focus on 2-dimensional artworks provides an introduction to the field. For each of the steps taken by the visual brain to turn retinal images into perception, one can ask how the biology informs one’s understanding of visual art, how visual artists have exploited aspects of how the brain processes visual information, and what the strategies deployed by visual artists reveal about neural mechanisms of vision.


Visual Attention  

Sabine Kastner and Timothy J. Buschman

Natural scenes are cluttered and contain many objects that cannot all be processed simultaneously. Due to this limited processing capacity, neural mechanisms are needed to selectively enhance the information that is most relevant to one’s current behavior and to filter unwanted information. We refer to these mechanisms as “selective attention.” Attention has been studied extensively at the behavioral level in a variety of paradigms, most notably, Treisman’s visual search and Posner’s paradigm. These paradigms have also provided the basis for studies directed at understanding the neural mechanisms underlying attentional selection, both in the form of neuroimaging studies in humans and intracranial electrophysiology in non-human primates. The selection of behaviorally relevant information is mediated by a large-scale network that includes regions in all major lobes as well as subcortical structures. Attending to a visual stimulus modulates processing across the visual processing hierarchy with stronger effects in higher-order areas. Current research is aimed at characterizing the functions of the different network nodes as well as the dynamics of their functional connectivity.


Visual Perception in the Human Brain: How the Brain Perceives and Understands Real-World Scenes  

Clemens G. Bartnik and Iris I. A. Groen

How humans perceive and understand real-world scenes is a long-standing question in neuroscience, cognitive psychology, and artificial intelligence. Initially, it was thought that scenes are constructed and represented by their component objects. An alternative view proposed that scene perception starts by extracting global features (e.g., spatial layout) first and individual objects in later stages. A third framework focuses on how the brain not only represents objects and layout but how this information combines to allow determining possibilities for (inter)action that the environment offers us. The discovery of scene-selective regions in the human visual system sparked interest in how scenes are represented in the brain. Experiments using functional magnetic resonance imaging show that multiple types of information are encoded in the scene-selective regions, while electroencephalography and magnetoencephalography measurements demonstrate links between the rapid extraction of different scene features and scene perception behavior. Computational models such as deep neural networks offer further insight by how training networks on different scene recognition tasks results in the computation of diagnostic features that can then be tested for their ability to predict activity in human brains when perceiving a scene. Collectively, these findings suggest that the brain flexibly and rapidly extracts a variety of information from scenes using a distributed network of brain regions.


Visual Shape and Object Perception  

Anitha Pasupathy, Yasmine El-Shamayleh, and Dina V. Popovkina

Humans and other primates rely on vision. Our visual system endows us with the ability to perceive, recognize, and manipulate objects, to avoid obstacles and dangers, to choose foods appropriate for consumption, to read text, and to interpret facial expressions in social interactions. To support these visual functions, the primate brain captures a high-resolution image of the world in the retina and, through a series of intricate operations in the cerebral cortex, transforms this representation into a percept that reflects the physical characteristics of objects and surfaces in the environment. To construct a reliable and informative percept, the visual system discounts the influence of extraneous factors such as illumination, occlusions, and viewing conditions. This perceptual “invariance” can be thought of as the brain’s solution to an inverse inference problem in which the physical factors that gave rise to the retinal image are estimated. While the processes of perception and recognition seem fast and effortless, it is a challenging computational problem that involves a substantial proportion of the primate brain.