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Deep Neural Networks in Computational Neuroscience  

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.

Article

Physiology of Color Vision in Primates  

Robert Shapley

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.

Article

Autonomic Thermoregulation  

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.

Article

Insect Navigation: Neural Basis to Behavior  

Stanley Heinze

Navigation is the ability of animals to move through their environment in a planned manner. Different from directed but reflex-driven movements, it involves the comparison of the animal’s current heading with its intended heading (i.e., the goal direction). When the two angles don’t match, a compensatory steering movement must be initiated. This basic scenario can be described as an elementary navigational decision. Many elementary decisions chained together in specific ways form a coherent navigational strategy. With respect to navigational goals, there are four main forms of navigation: explorative navigation (exploring the environment for food, mates, shelter, etc.); homing (returning to a nest); straight-line orientation (getting away from a central place in a straight line); and long-distance migration (seasonal long-range movements to a location such as an overwintering place). The homing behavior of ants and bees has been examined in the most detail. These insects use several strategies to return to their nest after foraging, including path integration, route following, and, potentially, even exploit internal maps. Independent of the strategy used, insects can use global sensory information (e.g., skylight cues), local cues (e.g., visual panorama), and idiothetic (i.e., internal, self-generated) cues to obtain information about their current and intended headings. How are these processes controlled by the insect brain? While many unanswered questions remain, much progress has been made in recent years in understanding the neural basis of insect navigation. Neural pathways encoding polarized light information (a global navigational cue) target a brain region called the central complex, which is also involved in movement control and steering. Being thus placed at the interface of sensory information processing and motor control, this region has received much attention recently and emerged as the navigational “heart” of the insect brain. It houses an ordered array of head-direction cells that use a wide range of sensory information to encode the current heading of the animal. At the same time, it receives information about the movement speed of the animal and thus is suited to compute the home vector for path integration. With the help of neurons following highly stereotypical projection patterns, the central complex theoretically can perform the comparison of current and intended heading that underlies most navigation processes. Examining the detailed neural circuits responsible for head-direction coding, intended heading representation, and steering initiation in this brain area will likely lead to a solid understanding of the neural basis of insect navigation in the years to come.

Article

Raptor Vision  

Mindaugas Mitkus, Simon Potier, Graham R. Martin, Olivier Duriez, and Almut Kelber

Diurnal raptors (birds of the orders Accipitriformes and Falconiformes), renowned for their extraordinarily sharp eyesight, have fascinated humans for centuries. The high visual acuity in some raptor species is possible due to their large eyes, both in relative and absolute terms, and a high density of cone photoreceptors. Some large raptors, such as wedge-tailed eagles and the Old World vultures, have visual acuities twice as high as humans and six times as high as ostriches—the animals with the largest terrestrial eyes. The raptor retina has rods, double cones, and four spectral types of single cones. The highest density of single cones occurs in one or two specialized retinal regions: the foveae, where, at least in some species, rods and double cones are absent. The deep central fovea allows for the highest acuity in the lateral visual field that is probably used for detecting prey from a large distance. Pursuit-hunting raptors have a second, shallower, temporal fovea that allows for sharp vision in the frontal field of view. Scavenging carrion eaters do not possess a temporal fovea that may indicate different needs in foraging behavior. Moreover, pursuit-hunting and scavenging raptors also differ in configuration of visual fields, with a more extensive field of view in scavengers. The eyes of diurnal raptors, unlike those of most other birds, are not very sensitive to ultraviolet light, which is strongly absorbed by their cornea and lens. As a result of the low density of rods, and the narrow and densely packed single cones in the central fovea, the visual performance of diurnal raptors drops dramatically as light levels decrease. These and other visual properties underpin prey detection and pursuit and show how these birds’ vision is adapted to make them successful diurnal predators.

Article

The Functional Organization of Vertebrate Retinal Circuits for Vision  

Tom Baden, Timm Schubert, Philipp Berens, and Thomas Euler

Visual processing begins in the retina—a thin, multilayered neuronal tissue lining the back of the vertebrate eye. The retina does not merely read out the constant stream of photons impinging on its dense array of photoreceptor cells. Instead it performs a first, extensive analysis of the visual scene, while constantly adapting its sensitivity range to the input statistics, such as the brightness or contrast distribution. The functional organization of the retina abides to several key organizational principles. These include overlapping and repeating instances of both divergence and convergence, constant and dynamic range-adjustments, and (perhaps most importantly) decomposition of image information into parallel channels. This is often referred to as “parallel processing.” To support this, the retina features a large diversity of neurons organized in functionally overlapping microcircuits that typically uniformly sample the retinal surface in a regular mosaic. Ultimately, each circuit drives spike trains in the retina’s output neurons, the retinal ganglion cells. Their axons form the optic nerve to convey multiple, distinctive, and often already heavily processed views of the world to higher visual centers in the brain. From an experimental point of view, the retina is a neuroscientist’s dream. While part of the central nervous system, the retina is largely self-contained, and depending on the species, it receives little feedback from downstream stages. This means that the tissue can be disconnected from the rest of the brain and studied in a dish for many hours without losing its functional integrity, all while retaining excellent experimental control over the exclusive natural network input: the visual stimulus. Once removed from the eyecup, the retina can be flattened, thus its neurons are easily accessed optically or using visually guided electrodes. Retinal tiling means that function studied at any one place can usually be considered representative for the entire tissue. At the same time, species-dependent specializations offer the opportunity to study circuits adapted to different visual tasks: for example, in case of our fovea, high-acuity vision. Taken together, today the retina is amongst the best understood complex neuronal tissues of the vertebrate brain.

Article

Annelid Vision  

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.

Article

Long-Term Potentiation and Long-Term Depression  

Arianna Maffei

Synaptic connections in the brain can change their strength in response to patterned activity. This ability of synapses is defined as synaptic plasticity. Long lasting forms of synaptic plasticity, long-term potentiation (LTP), and long-term depression (LTD), are thought to mediate the storage of information about stimuli or features of stimuli in a neural circuit. Since its discovery in the early 1970s, synaptic plasticity became a central subject of neuroscience, and many studies centered on understanding its mechanisms, as well as its functional implications.

Article

Auditory Mechanisms of Echolocation in Bats  

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.

Article

Drosophila Olfaction  

Quentin Gaudry and Jonathan Schenk

Olfactory systems are tasked with converting the chemical environment into electrical signals that the brain can use to optimize behaviors such as navigating towards resources, finding mates, or avoiding danger. Drosophila melanogaster has long served as a model system for several attributes of olfaction. Such features include sensory coding, development, and the attempt to link sensory perception to behavior. The strength of Drosophila as a model system for neurobiology lies in the myriad of genetic tools made available to the experimentalist, and equally importantly, the numerical reduction in cell numbers within the olfactory circuit. Modern techniques have recently made it possible to target nearly all cell types in the antennal lobe to directly monitor their physiological activity or to alter their expression of endogenous proteins or transgenes.