Angel Ariel Caputi
American gymnotiformes and African mormyriformes have evolved an active sensory system using a self-generated electric field as a carrier of signals. Objects polarized by the discharge of a specialized electric organ project their images on the skin where electroreceptors tuned to the time course of the self-generated field transduce local signals carrying information about impedance, shape, size, and location of objects, as well as electrocommunication messages, and encode them as primary afferents trains of spikes. This system is articulated with other cutaneous systems (passive electroreception and mechanoception) as well as proprioception informing the shape of the fish’s body. Primary afferents project on the electrosensory lobe where electrosensory signals are compared with expectation signals resulting from the integration of recent past electrosensory, other sensory, and, in the case of mormyriformes, electro- and skeleton-motor corollary discharges. This ensemble of signals converges on the apical dendrites of the principal cells where a working memory of the recent past, and therefore predictable input, is continuously built up and updated as a pattern of synaptic weights. The efferent neurons of the electrosensory lobe also project to the torus and indirectly to other brainstem nuclei that implement automatic electro- and skeleton-motor behaviors. Finally, the torus projects via the preglomerular nucleus to the telencephalon where cognitive functions, including “electroperception” of shape-, size- and impedance-related features of objects, recognition of conspecifics, perception based decisions, learning, and abstraction, are organized.
Cynthia M. Harley and Mark K. Asplen
Annelid worms are simultaneously an interesting and difficult model system for understanding the evolution of animal vision. On the one hand, a wide variety of photoreceptor cells and eye morphologies are exhibited within a single phylum; on the other, annelid phylogenetics has been substantially re-envisioned within the last decade, suggesting the possibility of considerable convergent evolution. This article reviews the comparative anatomy of annelid visual systems within the context of the specific behaviors exhibited by these animals. Each of the major classes of annelid visual systems is examined, including both simple photoreceptor cells (including leech body eyes) and photoreceptive cells with pigment (trochophore larval eyes, ocellar tubes, complex eyes); meanwhile, behaviors examined include differential mobility and feeding strategies, similarities (or differences) in larval versus adult visual behaviors within a species, visual signaling, and depth sensing. Based on our review, several major trends in the comparative morphology and ethology of annelid vision are highlighted: (1) eye complexity tends to increase with mobility and higher-order predatory behavior; (2) although they have simple sensors these can relay complex information through large numbers or multimodality; (3) polychaete larval and adult eye morphology can differ strongly in many mobile species, but not in many sedentary species; and (4) annelids exhibiting visual signaling possess even more complex visual systems than expected, suggesting the possibility that complex eyes can be simultaneously well adapted to multiple visual tasks.
Jeffrey R. Holt and Gwenaëlle S.G. Géléoc
The organs of the vertebrate inner ear respond to a variety of mechanical stimuli: semicircular canals are sensitive to angular velocity, the saccule and utricle respond to linear acceleration (including gravity), and the cochlea is sensitive to airborne vibration, or sound. The ontogenically related lateral line organs, spaced along the sides of aquatic vertebrates, sense water movement. All these organs have a common receptor cell type, which is called the hair cell, for the bundle of enlarged microvilli protruding from its apical surface. In different organs, specialized accessory structures serve to collect, filter, and then deliver these physical stimuli to the hair bundles. The proximal stimulus for all hair cells is deflection of the mechanosensitive hair bundle. Hair cells convert mechanical information contained within the temporal pattern of hair bundle deflections into electrical signals, which they transmit to the brain for interpretation.
Cynthia F. Moss
Echolocating bats have evolved an active sensing system, which supports 3D perception of objects in the surroundings and permits spatial navigation in complete darkness. Echolocating animals produce high frequency sounds and use the arrival time, intensity, and frequency content of echo returns to determine the distance, direction, and features of objects in the environment. Over 1,000 species of bats echolocate with signals produced in their larynges. They use diverse sonar signal designs, operate in habitats ranging from tropical rain forest to desert, and forage for different foods, including insects, fruit, nectar, small vertebrates, and even blood. Specializations of the mammalian auditory system, coupled with high frequency hearing, enable spatial imaging by echolocation in bats. Specifically, populations of neurons in the bat central nervous system respond selectively to the direction and delay of sonar echoes. In addition, premotor neurons in the bat brain are implicated in the production of sonar calls, along with movement of the head and ears. Audio-motor circuits, within and across brain regions, lay the neural foundation for acoustic orientation by echolocation in bats.
Age-related hearing loss affects over half of the elderly population, yet it remains poorly understood. Natural aging can cause the input to the brain from the cochlea to be progressively compromised in most individuals, but in many cases the cochlea has relatively normal sensitivity and yet people have an increasingly difficult time processing complex auditory stimuli. The two main deficits are in sound localization and temporal processing, which lead to poor speech perception. Animal models have shown that there are multiple changes in the brainstem, midbrain, and thalamic auditory areas as a function of age, giving rise to an alteration in the excitatory/inhibitory balance of these neurons. This alteration is manifest in the cerebral cortex as higher spontaneous and driven firing rates, as well as broader spatial and temporal tuning. These alterations in cortical responses could underlie the hearing and speech processing deficits that are common in the aged population.
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.
Paul E. Nachtigall
Toothed whales and dolphins, odontocete cetaceans, produce very loud biosonar sounds in order to navigate and to locate and catch their prey of fish and squid. Underwater biosonar was not discovered until after 1950, but the initial experiments demonstrated a unique sensory modality that could find small targets far away and distinguish between objects buried in mud that differed only by the metal from which they were made. Dolphins determine the distance to their prey by evaluating very small time differences between the outgoing signal and the echo return. The type of outgoing signal varies greatly from low frequency, explosively loud sperm whale clicks, to frequency modulated mid-frequency beaked whale sounds, to very high frequency (over 100 kHz) harbor porpoise signals. All appear to be made by specialized pneumatic phonic lips closely connected to sound projecting fatty melons that focus sound before sending out narrow echolocation sound beams. The frequency of most hearing is matched to echolocation, with the areas of best hearing of the animals being the areas of principal outgoing signal frequency. The sensation levels of hearing are under the animal’s control with “automatic gain control” operating to assure the best hearing of the echo returns. Angular localization of the bottlenose dolphins, for discriminating the minimum audible angles of clicks, is less than one degree in both the horizontal and vertical directions. This remarkable localization performance has yet to be fully explained, but new hypotheses of gular pathways, shaded receiver models, and internal pinnae may provide some explanations as a theory of auditory localization in the odontocetes develops.
Douglas K. Reilly and Jagan Srinivasan
To survive, animals must properly sense their surrounding environment. The types of sensation that allow for detecting these changes can be categorized as tactile, thermal, aural, or olfactory. Olfaction is one of the most primitive senses, involving the detection of environmental chemical cues. Organisms must sense and discriminate between abiotic and biogenic cues, necessitating a system that can react and respond to changes quickly. The nematode, Caenorhabditis elegans, offers a unique set of tools for studying the biology of olfactory sensation.
The olfactory system in C. elegans is comprised of 14 pairs of amphid neurons in the head and two pairs of phasmid neurons in the tail. The male nervous system contains an additional 89 neurons, many of which are exposed to the environment and contribute to olfaction. The cues sensed by these olfactory neurons initiate a multitude of responses, ranging from developmental changes to behavioral responses. Environmental cues might initiate entry into or exit from a long-lived alternative larval developmental stage (dauer), or pheromonal stimuli may attract sexually mature mates, or repel conspecifics in crowded environments. C. elegans are also capable of sensing abiotic stimuli, exhibiting attraction and repulsion to diverse classes of chemicals. Unlike canonical mammalian olfactory neurons, C. elegans chemosensory neurons express more than one receptor per cell. This enables detection of hundreds of chemical structures and concentrations by a chemosensory nervous system with few cells. However, each neuron detects certain classes of olfactory cues, and, combined with their synaptic pathways, elicit similar responses (i.e., aversive behaviors). The functional architecture of this chemosensory system is capable of supporting the development and behavior of nematodes in a manner efficient enough to allow for the genus to have a cosmopolitan distribution.
Yaniv Cohen, Emmanuelle Courtiol, Regina M. Sullivan, and Donald A. Wilson
Odorants, inhaled through the nose or exhaled from the mouth through the nose, bind to receptors on olfactory sensory neurons. Olfactory sensory neurons project in a highly stereotyped fashion into the forebrain to a structure called the olfactory bulb, where odorant-specific spatial patterns of neural activity are evoked. These patterns appear to reflect the molecular features of the inhaled stimulus. The olfactory bulb, in turn, projects to the olfactory cortex, which is composed of multiple sub-units including the anterior olfactory nucleus, the olfactory tubercle, the cortical nucleus of the amygdala, the anterior and posterior piriform cortex, and the lateral entorhinal cortex. Due to differences in olfactory bulb inputs, local circuitry and other factors, each of these cortical sub-regions appears to contribute to different aspects of the overall odor percept. For example, there appears to be some spatial organization of olfactory bulb inputs to the cortical nucleus of the amygdala, and this region may be involved in the expression of innate odor hedonic preferences. In contrast, the olfactory bulb projection to the piriform cortex is highly distributed and not spatially organized, allowing the piriform to function as a combinatorial, associative array, producing the emergence of experience-dependent odor-objects (e.g., strawberry) from the molecular features extracted in the periphery. Thus, the full perceptual experience of an odor requires involvement of a large, highly dynamic cortical network.
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.