Colin J. Saldanha
Since the early 1980s, evidence suggesting that the vertebrate brain is a rich source of steroid hormones has been decisive and extensive. This evidence includes data from many vertebrate species and describes almost every enzyme necessary for the conversion of cholesterol to androgens and estrogens. In contrast, the behavioral relevance of neurosteroidogenesis is more equivocal and mysterious. Nonetheless, the presence of a limited number of steroidogenic enzymes in the brain of a few species has clearly been linked to reliable behavioral phenotype.
Gerald H. Jacobs
Color is a central feature of human perceptual experience where it functions as a critical component in the detection, identification, evaluation, placement, and appreciation of objects in the visual world. Its role is significantly enhanced by the fact that humans evolved a dimension of color vision beyond that available to most other mammals. Many fellow primates followed a similar path and in recent years the basic mechanisms that support color vision—the opsin genes, photopigments, cone signals, and central processing—have been the subjects of hundreds of investigations. Because of the tight linkage between opsin gene structure and the spectral sensitivity of cone photopigments, it is possible to trace pathways along which color vision may have evolved in primates. In turn, such information allows the development of hypotheses about the nature of color vision and its utility in nonhuman primates. These hypotheses are being critically evaluated in field studies where primates solve visual problems in the presence of the full panoply of photic cues. The intent of this research is to determine which aspects of these cues are critically linked to color vision and how their presence facilitates, impedes, or fails to influence the solutions. These investigations are challenging undertakings and the emerging literature is replete with contradictory conclusions. But steady progress is being made and it appears that (a) some of the original ideas about there being a restricted number of tasks for which color vision might be optimally utilized by nonhuman primates (e. g., fruit harvest) were too simplistic and (b) depending on circumstances that can include both features of proximate visual stimuli (spectral cues, luminance cues, size cues, motion cues, overall light levels) and situational variables (social cues, developmental status, species-specific traits) the utilization of color vision by nonhuman primates is apt to be complex and varied.
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.
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.
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.
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. We demonstrate the power afforded by the unique information brought about by a comparative approach by studying transcription in a wide array of species and illustrate how it will allow 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”). We also show that 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. Finally, we outline gaps in our knowledge of reproductive plasticity and propose future directions.
Jon H. Kaas
The neocortex is a part of the forebrain of mammals that is an innovation of mammal-like “reptilian” synapsid ancestors of early mammals. This neocortex emerged from a small region of dorsal cortex that was present in earlier ancestors and is still found in the forebrain of present-day reptiles. Instead of the thick structure of six layers of cells (five layers) and fibers (one layer) of neocortex of mammals, the dorsal cortex was characterized by a single layer of pyramidal neurons and a scattering of small, largely inhibitory neurons. In reptiles, the dorsal cortex is dominated by visual inputs, with outputs that relate to behavior and memory. The thicker neocortex of six layers in early mammals was already divided into a number of functionally specialized zones called cortical areas that were predominantly sensory in function, while relating to important aspects of motor behavior via subcortical projections. These early sensorimotor areas became modified in various ways as different branches of the mammalian radiation evolved, and neocortex often increased in size and the number of cortical areas, likely by the process of specializations within areas that subdivided areas. At least some areas, perhaps most, subdivided in another way by evolving two or more alternating types of small regions of different functional specializations, now referred to as cortical modules or columns. The specializations within and across cortical areas included those in the sizes of neurons and the extents of their processes, the dendrites and axons, and thus connections with other neurons. As a result, the neocortex of present-day mammals varies greatly within and across phylogenetically related groups (clades), while retaining basic features of organization from early ancestral mammals. In a number of present-day (extant) mammals, brains are relatively small and have little neocortex, with few areas and little structural differentiation, thus resembling early mammals. Other small mammals with little neocortex have specialized some part via selective enlargement and structural modifications to promote certain sensory abilities. Other mammals have a neocortex that is moderately to greatly expanded, with more cortical areas directly related to sensory processing and cognition and memory. The human brain is extreme in this way by having more neocortex in proportion to the rest of the brain, more cortical neurons, and likely more cortical areas.
Tim C. Kietzmann, Patrick McClure, and Nikolaus Kriegeskorte
The goal of computational neuroscience is to find mechanistic explanations of how the nervous system processes information to give rise to cognitive function and behavior. At the heart of the field are its models, that is, mathematical and computational descriptions of the system being studied, which map sensory stimuli to neural responses and/or neural to behavioral responses. These models range from simple to complex. Recently, deep neural networks (DNNs) have come to dominate several domains of artificial intelligence (AI). As the term “neural network” suggests, these models are inspired by biological brains. However, current DNNs neglect many details of biological neural networks. These simplifications contribute to their computational efficiency, enabling them to perform complex feats of intelligence, ranging from perceptual (e.g., visual object and auditory speech recognition) to cognitive tasks (e.g., machine translation), and on to motor control (e.g., playing computer games or controlling a robot arm). In addition to their ability to model complex intelligent behaviors, DNNs excel at predicting neural responses to novel sensory stimuli with accuracies well beyond any other currently available model type. DNNs can have millions of parameters, which are required to capture the domain knowledge needed for successful task performance. Contrary to the intuition that this renders them into impenetrable black boxes, the computational properties of the network units are the result of four directly manipulable elements: input statistics, network structure, functional objective, and learning algorithm. With full access to the activity and connectivity of all units, advanced visualization techniques, and analytic tools to map network representations to neural data, DNNs represent a powerful framework for building task-performing models and will drive substantial insights in computational neuroscience.
Color perception in macaque monkeys and humans depends on the visually evoked activity in three cone photoreceptors and on neuronal post-processing of cone signals. Neuronal post-processing of cone signals occurs in two stages in the pathway from retina to the primary visual cortex. The first stage, in in P (midget) ganglion cells in the retina, is a single-opponent subtractive comparison of the cone signals. The single-opponent computation is then sent to neurons in the Parvocellular layers of the Lateral Geniculate Nucleus (LGN), the main visual nucleus of the thalamus. The second stage of processing of color-related signals is in the primary visual cortex, V1, where multiple comparisons of the single-opponent signals are made. The diversity of neuronal interactions in V1cortex causes the cortical color cells to be subdivided into classes of single-opponent cells and double-opponent cells. Double-opponent cells have visual properties that can be used to explain most of the phenomenology of color perception of surface colors; they respond best to color edges and spatial patterns of color. Single opponent cells, in retina, LGN, and V1, respond to color modulation over their receptive fields and respond best to color modulation over a large area in the visual field.
Thad E. Wilson and Kristen Metzler-Wilson
Thermoregulation is a key physiologic homeostatic process and is subdivided into autonomic, behavioral, and adaptive divisions. Autonomic thermoregulation is a neural process related to the sympathetic and parasympathetic nervous systems. Autonomic thermoregulation is controlled at the subcortical level to alter physiologic processes of heat production and loss to maintain internal temperature. Mammalian, including human, autonomic responses to acute heat or cold stresses are dependent on environmental conditions and species genotype and phenotype, but many similarities exist. Responses to an acute heat stress begin with the sensation of heat, leading to central processing of the information and sympathetic responses via end organs, which can include sweat glands, vasculature, and airway and cardiac tissues. Responses to an acute cold stress begin with the sensation of cold, which leads to central processing of the information and sympathetic responses via end organs, which can include skeletal and piloerector muscles, brown adipose tissue, vasculature, and cardiac tissue. These autonomic responses allow homeostasis of internal temperature to be maintained across a wide range of external temperatures for most mammals, including humans. At times, uncompensable thermal challenges occur that can be maintained for only limited periods of time before leading to pathophysiologic states of hyperthermia or hypothermia.