Optimal measurement of visual motion across spatial and temporal scales

Sensory systems use limited resources to mediate the perception of a great variety of objects and events. Here a normative framework is presented for exploring how the problem of efficient allocation of resources can be solved in visual perception. Starting with a basic property of every measurement, captured by Gabor's uncertainty relation about the location and frequency content of signals, prescriptions are developed for optimal allocation of sensors for reliable perception of visual motion. This study reveals that a large-scale characteristic of human vision (the spatiotemporal contrast sensitivity function) is similar to the optimal prescription, and it suggests that some previously puzzling phenomena of visual sensitivity, adaptation, and perceptual organization have simple principled explanations.Comment: 28 pages, 10 figures, 2 appendices; in press in Favorskaya MN and Jain LC (Eds), Computer Vision in Advanced Control Systems using Conventional and Intelligent Paradigms, Intelligent Systems Reference Library, Springer-Verlag, Berli

Expression of endothelia and lymphocyte adhesion molecules in bronchus-associated lymphoid tissue (BALT) in adult human lung

BACKGROUND: Bronchus-associated lymphoid tissue (BALT) is the secondary lymphoid tissue in bronchial mucosa and is involved in the development of bronchopulmonary immune responses. Although migration of lymphocytes from blood vessels into secondary lymphoid tissues is critical for the development of appropriate adaptive immunity, the endothelia and lymphocyte adhesion molecules that recruit specific subsets of lymphocytes into human BALT are not known. The aim of this study was to determine which adhesion molecules are expressed on lymphocytes and high endothelial venules (HEVs) in human BALT. METHODS: We immunostained frozen sections of BALT from lobectomy specimens from 17 patients with lung carcinoma with a panel of monoclonal antibodies to endothelia and lymphocyte adhesion molecules. RESULTS: Sections of BALT showed B cell follicles surrounded by T cells. Most BALT CD4+ T cells had a CD45RO+ memory phenotype. Almost all BALT B cells expressed alpha4 integrin and L-selectin. In contrast, 43% of BALT T cells expressed alpha4 integrin and 20% of BALT T cells expressed L-selectin. Almost all BALT lymphocytes expressed LFA-1. HEVs, which support the migration of lymphocytes from the bloodstream into secondary lymphoid tissues, were prominent in BALT. All HEVs expressed peripheral node addressin, most HEVs expressed vascular cell adhesion molecule-1, and no HEVs expressed mucosal addressin cell adhesion molecule-1. CONCLUSION: Human BALT expresses endothelia and lymphocyte adhesion molecules that may be important in recruiting naive and memory/effector lymphocytes to BALT during protective and pathologic bronchopulmonary immune responses

Environmental Enrichment Promotes Plasticity and Visual Acuity Recovery in Adult Monocular Amblyopic Rats

Loss of visual acuity caused by abnormal visual experience during development (amblyopia) is an untreatable pathology in adults. In some occasions, amblyopic patients loose vision in their better eye owing to accidents or illnesses. While this condition is relevant both for its clinical importance and because it represents a case in which binocular interactions in the visual cortex are suppressed, it has scarcely been studied in animal models. We investigated whether exposure to environmental enrichment (EE) is effective in triggering recovery of vision in adult amblyopic rats rendered monocular by optic nerve dissection in their normal eye. By employing both electrophysiological and behavioral assessments, we found a full recovery of visual acuity in enriched rats compared to controls reared in standard conditions. Moreover, we report that EE modulates the expression of GAD67 and BDNF. The non invasive nature of EE renders this paradigm promising for amblyopia therapy in adult monocular people

Simply longer is not better: reversal of theta burst after-effect with prolonged stimulation

From all rTMS protocols at present, the theta burst stimulation (TBS) is considered the most efficient in terms of number of impulses and intensity required during a given stimulation. The aim of this study was to investigate the effects of inhibitory and excitatory TBS protocols on motor cortex excitability when the duration of stimulation was doubled. Fourteen healthy volunteers were tested under four conditions: intermittent theta bust stimulation (iTBS), continuous theta burst stimulation (cTBS), prolonged intermittent theta bust stimulation (ProiTBS) and prolonged continuous theta burst stimulation (ProcTBS). The prolonged paradigms were twice as long as the conventional TBS protocols. Conventional facilitatory iTBS converted into inhibitory when it was applied for twice as long, while the normally inhibitory cTBS became facilitatory when the stimulation duration was doubled. Our results show that TBS-induced plasticity cannot be deliberately enhanced simply by prolonging TBS protocols. Instead, when stimulating too long, after-effects will be reversed. This finding supplements findings at the short end of the stimulation duration range, where it was shown that conventional cTBS is excitatory in the first half and switches to inhibition only after the full length protocol. It is relevant for clinical applications for which an ongoing need for further protocol improvement is imminent

Loss of Receptor on Tuberculin-Reactive T-Cells Marks Active Pulmonary Tuberculosis

BACKGROUND: Tuberculin-specific T-cell responses have low diagnostic specificity in BCG vaccinated populations. While subunit-antigen (e.g. ESAT-6, CFP-10) based tests are useful for diagnosing latent tuberculosis infection, there is no reliable immunological test for active pulmonary tuberculosis. Notably, all existing immunological tuberculosis-tests are based on T-cell response size, whereas the diagnostic potential of T-cell response quality has never been explored. This includes surface marker expression and functionality of mycobacterial antigen specific T-cells. METHODOLOGY/PRINCIPAL FINDINGS: Flow-cytometry was used to examine over-night antigen-stimulated T-cells from tuberculosis patients and controls. Tuberculin and/or the relatively M. tuberculosis specific ESAT-6 protein were used as stimulants. A set of classic surface markers of T-cell naive/memory differentiation was selected and IFN-gamma production was used to identify T-cells recognizing these antigens. The percentage of tuberculin-specific T-helper-cells lacking the surface receptor CD27, a state associated with advanced differentiation, varied considerably between individuals (from less than 5% to more than 95%). Healthy BCG vaccinated individuals had significantly fewer CD27-negative tuberculin-reactive CD4 T-cells than patients with smear and/or culture positive pulmonary tuberculosis, discriminating these groups with high sensitivity and specificity, whereas individuals with latent tuberculosis infection exhibited levels in between. CONCLUSIONS/SIGNIFICANCE: Smear and/or culture positive pulmonary tuberculosis can be diagnosed by a rapid and reliable immunological test based on the distribution of CD27 expression on peripheral blood tuberculin specific T-cells. This test works very well even in a BCG vaccinated population. It is simple and will be of great utility in situations where sputum specimens are difficult to obtain or sputum-smear is negative. It will also help avoid unnecessary hospitalization and patient isolation

Learning with a network of competing synapses

Competition between synapses arises in some forms of correlation-based plasticity. Here we propose a game theory-inspired model of synaptic interactions whose dynamics is driven by competition between synapses in their weak and strong states, which are characterized by different timescales. The learning of inputs and memory are meaningfully definable in an effective description of networked synaptic populations. We study, numerically and analytically, the dynamic responses of the effective system to various signal types, particularly with reference to an existing empirical motor adaptation model. The dependence of the system-level behavior on the synaptic parameters, and the signal strength, is brought out in a clear manner, thus illuminating issues such as those of optimal performance, and the functional role of multiple timescales.Comment: 16 pages, 9 figures; published in PLoS ON

Towards a General Theory of Neural Computation Based on Prediction by Single Neurons

Although there has been tremendous progress in understanding the mechanics of the nervous system, there has not been a general theory of its computational function. Here I present a theory that relates the established biophysical properties of single generic neurons to principles of Bayesian probability theory, reinforcement learning and efficient coding. I suggest that this theory addresses the general computational problem facing the nervous system. Each neuron is proposed to mirror the function of the whole system in learning to predict aspects of the world related to future reward. According to the model, a typical neuron receives current information about the state of the world from a subset of its excitatory synaptic inputs, and prior information from its other inputs. Prior information would be contributed by synaptic inputs representing distinct regions of space, and by different types of non-synaptic, voltage-regulated channels representing distinct periods of the past. The neuron's membrane voltage is proposed to signal the difference between current and prior information (“prediction error” or “surprise”). A neuron would apply a Hebbian plasticity rule to select those excitatory inputs that are the most closely correlated with reward but are the least predictable, since unpredictable inputs provide the neuron with the most “new” information about future reward. To minimize the error in its predictions and to respond only when excitation is “new and surprising,” the neuron selects amongst its prior information sources through an anti-Hebbian rule. The unique inputs of a mature neuron would therefore result from learning about spatial and temporal patterns in its local environment, and by extension, the external world. Thus the theory describes how the structure of the mature nervous system could reflect the structure of the external world, and how the complexity and intelligence of the system might develop from a population of undifferentiated neurons, each implementing similar learning algorithms