Active electric imaging: body-object interplay and object's "electric texture"

This article deals with the role of fish's body and object's geometry on determining the image spatial shape in pulse Gymnotiforms. This problem was explored by measuring local electric fields along a line on the skin in the presence and absence of objects. We depicted object's electric images at different regions of the electrosensory mosaic, paying particular attention to the perioral region where a fovea has been described. When sensory surface curvature increases relative to the object's curvature, the image details depending on object's shape are blurred and finally disappear. The remaining effect of the object on the stimulus profile depends on the strength of its global polarization. This depends on the length of the object's axis aligned with the field, in turn depending on fish body geometry. Thus, fish's body and self-generated electric field geometries are embodied in this "global effect" of the object. The presence of edges or local changes in impedance at the nearest surface of closely located objects adds peaks to the image profiles ("local effect" or "object's electric texture"). It is concluded that two cues for object recognition may be used by active electroreceptive animals: global effects (informing on object's dimension along the field lines, conductance, and position) and local effects (informing on object's surface). Since the field has fish's centered coordinates, and electrosensory fovea is used for exploration of surfaces, fish fine movements are essential to perform electric perception. We conclude that fish may explore adjacent objects combining active movements and electrogenesis to represent them using electrosensory information.     

Curved trajectory prediction using a self-organizing neural network

Existing neural network models are capable of tracking linear trajectories of moving visual objects. This paper describes an additional neural mechanism, disfacilitation, that enhances the ability of a visual system to track curved trajectories. The added mechanism combines information about an object's trajectory with information about changes in the object's trajectory, to improve the estimates for the object's next probable location. Computational simulations are presented that show how the neural mechanism can learn to track the speed of objects and how the network operates to predict the trajectories of accelerating and decelerating objects.     

Fast corrections of movements with a computer mouse

When we reach out for an object with our hand, we transform visual information about the object's position into muscle contractions that will bring our digits to that position. If we reach out with a tool the transformation is different, because the muscle contractions must bring the critical part of the tool to the object, rather than the digits. The difference between the motion of the hand and that of the tool can be quite large, as when moving a computer mouse across a table to bring a cursor to a position on a screen. We examined the responses to unpredictable visual perturbations during such movements. People responded about as quickly to changes in the position of the target when pointing with the mouse as when doing so with their hand. They also responded about as quickly when the cursor was displaced as when the target was displaced. We show that this is not because the visually perceived separation between target and cursor is transformed into a desired displacement of the hand. Our conclusion is that our actions are controlled by the judged positions of the end-effector and the target, even when the former is quite detached from the muscles and joints that are involved in the action.     

Color analysis method for estimating the oxygen saturation of hemoglobin using an image-input and processing system

A color analysis method which enables both qualitative and quantitative analyses of an object's color was developed. The method uses a color image-input and processing system composed of a 3-tube video camera and a digital image analyzer, which quantizes a color image into values of red, green, and blue brightness, then processes these values. We introduced a spectrophotometric principle by the Beer-Lambert law, and were able to establish a color model to analyze an object's color. In the coordinate space based on our color model, the hue of the object's color is represented by the direction from the origin, and the density by the distance from the origin. This new method was used to analyze the colors of hemoglobin solutions at various oxygen saturations and concentrations. The results agreed with the known conditions, indicating the validity of the model and its usefulness for quantitative as well as qualitative analyses of color.     

Spinal motor control system incorporates an internal model of limb dynamics

The existence and utilization of an internal representation of the controlled object is one of the most important features of the functioning of neural motor control systems. This study demonstrates that this property already exists at the level of the spinal motor control system (SMCS), which is capable of generating motor patterns for reflex rhythmic movements, such as locomotion and scratching, without the aid of the peripheral afferent feedback, but substantially modifies the generated activity in response to peripheral afferent stimuli. The SMCS is presented as an optimal control system whose optimality requires that it incorporate an internal model (IM) of the controlled object's dynamics. A novel functional mechanism for the integration of peripheral sensory signals with the corresponding predictive output from the IM, the summation of information precision (SIP) is proposed. In contrast to other models in which the correction of the internal representation of the controlled object's state is based on the calculation of a mismatch between the internal and external information sources, the SIP mechanism merges the information from these sources in order to optimize the precision of the controlled object's state estimate. It is demonstrated, based on scratching in decerebrate cats as an example of the spinal control of goal-directed movements, that the results of computer modeling agree with the experimental observations related to the SMCS's reactions to phasic and tonic peripheral afferent stimuli. It is also shown that the functional requirements imposed by the mathematical model of the SMCS comply with the current knowledge about the related properties of spinal neuronal circuitry. The crucial role of the spinal presynaptic inhibition mechanism in the neuronal implementation of SIP is elucidated. Important differences between the IM and a state predictor employed for compensating for a neural reflex time delay are discussed. Received: 8 February 2000 / Accepted: 24 March 2000     

Quantitative phase imaging with scanning holographic microscopy: an experimental assesment

This paper demonstrates experimentally how quantitative phase information can be obtained in scanning holographic microscopy. Scanning holography can operate in both coherent and incoherent modes, simultaneously if desired, with different detector geometries. A spatially integrating detector provides an incoherent hologram of the object's intensity distribution (absorption and/or fluorescence, for example), while a point detector in a conjugate plane of the pupil provides a coherent hologram of the object's complex amplitude, from which a quantitative measure of its phase distribution can be extracted. The possibility of capturing simultaneously holograms of three-dimensional specimens, leading to three-dimensional reconstructions with absorption contrast, reflectance contrast, fluorescence contrast, as was previously demonstrated, and quantitative phase contrast, as shown here for the first time, opens up new avenues for multimodal imaging in biological studies.     

Vision: getting to grips with the Ebbinghaus illusion

It is well known that visual illusions can have a dramatic effect upon our visual perception of such properties as an object's size. It remains the subject of much debate, however, whether visual illusions have a similar influence on visually guided actions. Recent studies have thrown new light on this debate.     

Motion processing: how low can you go?

Neurons at early stages in the visual system can only 'view' small parts of the visual world, impeding their ability to determine correctly an object's motion direction. New studies suggest that this 'aperture problem' is already solved by special neurons at the first stage of motion detection in primary visual cortex.     

A computational basis to object?

To use an object, we must be able to perceive the spatial relationship between the object's parts. The accepted view of how the brain coherently encodes an object is that some neurons in the frontal cortex employ an object-centered coordinate frame. A new computational model challenges this view, using the rich conceptual framework of neural basis functions.     

Symmetry cues for matching mirrored objects

The study explores cues for matching pairs of objects. These objects were arranged into mirror-symmetrical displays and the task was to judge whether a pre-specified 180 degree rotation around the X, Y or Z axis carries one object into the mirror object. For some rotations, the object's mirror symmetry (M) and, for other rotations, the object's point-symmetry (P) could serve as a cue. The matching results suggest that M is a better cue than P, say M > P. Various attempts are made to explain this effect. The most promising one focuses on simplest structural object representations as these capture M and not P. It is furthermore plausible that M captured by reference frames at high hierarchical representation levels, say M1, serves as a better cue than M captured at low levels, say M2. The prediction M1 > M2 > P merely applies to the open surface objects in the experiment. For the closed solid objects in the experiment the expectation is M1 = M2 = P. Both predictions roughly agree with the accuracy and reaction-time data. The results suggest the perceptual relevance of representation cues and, aditionally, that cues stemming from reference frames at higher hierarchical representation levels are more effective than those from lower levels.     

Obtaining information by dynamic (effortful) touching

Dynamic touching is effortful touching. It entails deformation of muscles and fascia and activation of the embedded mechanoreceptors, as when an object is supported and moved by the body. It is realized as exploratory activities that can vary widely in spatial and temporal extents (a momentary heft, an extended walk). Research has revealed the potential of dynamic touching for obtaining non-visual information about the body (e.g. limb orientation), attachments to the body (e.g. an object's height and width) and the relation of the body both to attachments (e.g. hand's location on a grasped object) and surrounding surfaces (e.g. places and their distances). Invariants over the exploratory activity (e.g. moments of a wielded object's mass distribution) seem to ground this 'information about'. The conception of a haptic medium as a nested tensegrity structure has been proposed to express the obtained information realized by myofascia deformation, by its invariants and transformations. The tensegrity proposal rationalizes the relative indifference of dynamic touch to the site of mechanical contact (hand, foot, torso or probe) and the overtness of exploratory activity. It also provides a framework for dynamic touching's fractal nature, and the finding that its degree of fractality may matter to its accomplishments.     

Theoretical analysis of pre-receptor image conditioning in weakly electric fish

Electroreceptive fish detect nearby objects by processing the information contained in the pattern of electric currents through the skin. The distribution of local transepidermal voltage or current density on the sensory surface of the fish's skin is the electric image of the surrounding environment. This article reports a model study of the quantitative effect of the conductance of the internal tissues and the skin on electric image generation in Gnathonemus petersii (Günther 1862). Using realistic modelling, we calculated the electric image of a metal object on a simulated fish having different combinations of internal tissues and skin conductances. An object perturbs an electric field as if it were a distribution of electric sources. The equivalent distribution of electric sources is referred to as an object's imprimence. The high conductivity of the fish body lowers the load resistance of a given object's imprimence, increasing the electric image. It also funnels the current generated by the electric organ in such a way that the field and the imprimence of objects in the vicinity of the rostral electric fovea are enhanced. Regarding skin conductance, our results show that the actual value is in the optimal range for transcutaneous voltage modulation by nearby objects. This result suggests that "voltage" is the answer to the long-standing question as to whether current or voltage is the effective stimulus for electroreceptors. Our analysis shows that the fish body should be conceived as an object that interacts with nearby objects, conditioning the electric image. The concept of imprimence can be extended to other sensory systems, facilitating the identification of features common to different perceptual systems.     

Possible use of television image analysis for measuring the volume and surface area of irregularly shaped nuclei

A use of the TV image for measuring volumes and squares of blood cell nuclei irregular in shape is considered. The linear correspondence has been detected between the volumetric-spatial structure of the object and the shape of the respective video-signal, this correspondence being mathematically proved. The measuring of parametres of video-signal may be informative of the object's shape.     

Object segmentation model: analytical results and biological implications

 A simple, biologically motivated neural network for segmentation of a moving object from a visual scene is presented. The model consists of two parts: an object selection model which employs a scaling approach for receptive field sizes, and a subsequent network implementing a spotlight by means of multiplicative synapses. The network selects one object out of several, segments the rough contour of the object, and encodes the winner object's position with high accuracy. Analytical equations for the performance level of the network, e.g., for the critical distance of two objects above which they are perceived as separate, are derived. The network preferentially chooses the object with the largest angular velocity and the largest angular width. An equation for the velocity and width preferences is presented. Additionally it is shown that for certain neurons of the model, flat receptive fields are more favourable than Gaussian ones. The network exhibits performances similar to those known from amphibians. Various electrophysiological and behavioral results – e.g., the distribution of the diameters of the receptive fields of tectal neurons, of the tongue-projecting salamander Hydromantes italicus and the range of optimal prey velocities for prey catching – can be understood on the basis of the model. Received: 7 December 2000 / Accepted: 13 February 2001     

Bayesian modeling of dynamic motion integration

The quality of the representation of an object's motion is limited by the noise in the sensory input as well as by an intrinsic ambiguity due to the spatial limitation of the visual motion analyzers (aperture problem). Perceptual and oculomotor data demonstrate that motion processing of extended objects is initially dominated by the local 1D motion cues, related to the object's edges and orthogonal to them, whereas 2D information, related to terminators (or edge-endings), takes progressively over and leads to the final correct representation of global motion. A Bayesian framework accounting for the sensory noise and general expectancies for object velocities has proven successful in explaining several experimental findings concerning early motion processing [Weiss, Y., Adelson, E., 1998. Slow and smooth: a Bayesian theory for the combination of local motion signals in human vision. MIT Technical report, A.I. Memo 1624]. In particular, these models provide a qualitative account for the initial bias induced by the 1D motion cue. However, a complete functional model, encompassing the dynamical evolution of object motion perception, including the integration of different motion cues, is still lacking. Here we outline several experimental observations concerning human smooth pursuit of moving objects and more particularly the time course of its initiation phase, which reflects the ongoing motion integration process. In addition, we propose a recursive extension of the Bayesian model, motivated and constrained by our oculomotor data, to describe the dynamical integration of 1D and 2D motion information. We compare the model predictions for object motion tracking with human oculomotor recordings.     

The 'when' parietal pathway explored by lesion studies

The perception of events in space and time is at the root of our interactions with the environment. The precision with which we perceive visual events in time enables us to act upon objects with great accuracy and the loss of such functions due to brain lesions can be catastrophic. We outline a visual timing mechanism that deals with the trajectory of an object's existence across time, a crucial function when keeping track of multiple objects that temporally overlap or occur sequentially. Recent evidence suggests these functions are served by an extended network of areas, which we call the 'when' pathway. Here we show that the when pathway is distinct from and interacts with the well-established 'where' and 'what' pathways.     

Teaching electron diffraction and imaging of macromolecules.

Electron microscopic analysis can be used to determine the three-dimensional structures of macromolecules at resolutions ranging between 3 and 30 A. It differs from nuclear magnetic resonance spectroscopy or x-ray crystallography in that it allows an object's Coulomb potential functions to be determined directly from images and can be used to study relatively complex macromolecular assemblies in a crystalline or noncrystalline state. Electron imaging already has provided valuable structural information about various biological systems, including membrane proteins, protein-nucleic acid complexes, contractile and motile protein assemblies, viruses, and transport complexes for ions or macromolecules. This article, organized as a series of lectures, presents the biophysical principles of three-dimensional analysis of objects possessing different symmetries.     

基于初级视觉皮层的图像匹配模型

提出一种基于初级视觉皮层的图像匹配模型。该模型只采用方位选择性细胞和皮层内有限范围水平连接等Ｖ１基本单元,它以链码表示的目标轮廓作为知识,允许该知识以时间脉冲的形式控制Ｖ１区内神经细胞的动态活动,使与知识轮廓形状相符合的轮廓内的细胞,逐步进入并维持在兴奋状态,最终实现对视野中特定目标轮廓的提取     

Imaging of objects through active electrolocation in Gnathonemus petersii.

The weakly electric fish Gnathonemus petersii detects, localizes, and analyzes objects during active electrolocation even in complete darkness. This enables these fish to lead a nocturnal life and find and identify their prey (small insect larvae) on the ground of their freshwater habitat. During active electrolocation, fish produce a series of brief electric signals, electric organ discharges (EOD), with an electric organ in their tail. Each EOD builds up a stable electric field around the fish, which is distorted only by nearby objects. Field distortions lead to changes of the transepidermal electric current flow at a region of the fish's electroreceptive skin surface called the 'electric image'. Within the electric image, locally perceived EODs can be either altered in amplitude or waveform by an object. Fish measure both parameters to assess object properties, such as the capacitive and resistive components of the object's complex impedance. the object's size and shape, and its distance from the fish. None of these object properties can be evaluated in isolation, but have to be inferred during parallel processing of electric image spatial and qualitative parameters. Two anterior skin regions of G. petersii appear to possess particular properties for special electrolocation tasks and we therefore refer to them as 'foveal' regions. Because of its high electroreceptor density, the electric field geometry around it, and its behavioral use, the 'nasal region' between the nares and the mouth at the head of the fish is suggested to be a fovea for long-range guidance and object detection. We propose that the 'Schnauzenorgan', a long and flexible chin appendix covered densely with electroreceptor organs, is a second electroreceptive fovea associated with a short-range (food) identification system. Together, these two electric foveae constitute an effective prey detection and identification system.