Temporal Dynamics of Decision-Making during Motion Perception in the Visual Cortex

How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.National Science Foundation (SBE-0354378, IIS-02-05271); Office of Naval Research (N00014-01-1-0624); National Institutes of Health (R01-DC-02852

Study of the Problems of Persons with Disability (PWD) Using FRMs

In this paper authors find the interrelations and the hidden pattern of the problems faced by the PWDs and their caretakers using Fuzzy Relational Maps (FRMs). Here they have taken the problems faced by the rural persons with disabilities in Melmalayanur and Kurinjipadi Blocks, Tamil Nadu, India

Gene Therapy in Cardiac Arrhythmias

Gene therapy has progressed from a dream to a bedside reality in quite a few human diseases. From its first application in adenosine deaminase deficiency, through the years, its application has evolved to vascular angiogenesis and cardiac arrhythmias. Gene based biological pacemakers using viral vectors or mesenchymal cells tested in animal models hold much promise. Induction of pacemaker activity within the left bundle branch can provide stable heart rates. Genetic modification of the AV node mimicking beta blockade can be therapeutic in the management of atrial fibrillation. G protein overexpression to modify the AV node also is experimental. Modification and expression of potassium channel genes altering the delayed rectifier potassium currents may permit better management of congenital long QT syndromes. Arrhythmias in a failing heart are due to abnormal calcium cycling. Potential targets for genetic modulation include the sarcoplasmic reticulum calcium pump, calsequestrin and sodium calcium exchanger.Lastly the ethical concerns need to be addressed

Task-Irrelevant Perceptual Learning Specific to the Contrast Polarity of Motion Stimuli

Studies of perceptual learning have focused on aspects of learning that are related to early stages of sensory processing. However, conclusions that perceptual learning results in low-level sensory plasticity are of great controversy, largely because such learning can often be attributed to plasticity in later stages of sensory processing or in the decision processes. To address this controversy, we developed a novel random dot motion (RDM) stimulus to target motion cells selective to contrast polarity, by ensuring the motion direction information arises only from signal dot onsets and not their offsets, and used these stimuli in conjunction with the paradigm of task-irrelevant perceptual learning (TIPL). In TIPL, learning is achieved in response to a stimulus by subliminally pairing that stimulus with the targets of an unrelated training task. In this manner, we are able to probe learning for an aspect of motion processing thought to be a function of directional V1 simple cells with a learning procedure that dissociates the learned stimulus from the decision processes relevant to the training task. Our results show learning for the exposed contrast polarity and that this learning does not transfer to the unexposed contrast polarity. These results suggest that TIPL for motion stimuli may occur at the stage of directional V1 simple cells.CELEST, an NSF Science of Learning Center (SBE-0354378); Defense Advanced Research Projects Agency SyNAPSE program (HR0011-09-3-0001, HR001-09-C-0011); National Science Foundation (BCS-0549036); National Institutes of Health (R21 EY017737

Rapidly reconfigurable slow-light system based on off-resonant Raman absorption

We present a slow-light system based on dual Raman absorption resonances in warm rubidium vapor. Each Raman absorption resonance is produced by a control beam in an off-resonant Λ system. This system combines all optical control of the Raman absorption and the low-dispersion broadening properties of the double Lorentzian absorption slow light. The bandwidth, group delay, and central frequency of the slow-light system can all be tuned dynamically by changing the properties of the control beam. We demonstrate multiple pulse delays with low distortion and show that such a system has fast switching dynamics and thus fast reconfiguration rates

Sensor coverage and actors relocation in wireless sensor and actor networks (WSAN) : optimization models and approximation algorithms

"December 2010.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Esra Sisikoglu.Wireless Sensors and Actor Networks (WSAN) have a wide variety of applications such as military surveillance, object tracking and habitat monitoring. Sensors are data gathering devices. Selecting the minimum number of sensors for network coverage is crucial to reduce the cost of installation and data processing time. Actors in a WSAN are decision-making units. They need to be communicating with their fellow actors in order to respond to events. Therefore, the need to maintain a connected inter-actor network at all times is critical. In the Actor Relocation Problem (Chapter 2) of this thesis we considered the problem of finding optimal strategies to restore connectivity when inter-actor network fails. We used a mixed integer programming formulation to find the optimal relocation strategies for actors in which the total travel distance is minimized. In our formulation we used powers of the adjacency matrix to generate constraints that ensure connectivity. In the Sensor Coverage Problem (Chapter 3) we developed a mixed integer programming model to find the minimum number of sensors and their locations to cover a given area. We also developed a bi-level algorithm that runs two separate optimization algorithms iteratively to find the location of sensors such that every point in a continuous area is covered.Includes bibliographical references (pages 61-63)

Neuromodulated attention and goal-driven perception in uncertain domains.

In uncertain domains, the goals are often unknown and need to be predicted by the organism or system. In this paper, contrastive Excitation Backprop (c-EB) was used in two goal-driven perception tasks - one with pairs of noisy MNIST digits and the other with a robot in an action-based attention scenario. The first task included attending to even, odd, low, and high digits, whereas the second task included action goals, such as "eat", "work-on-computer", "read", and "say-hi" that led to attention to objects associated with those actions. The system needed to increase attention to target items and decrease attention to distractor items and background noise. Because the valid goal was unknown, an online learning model based on the cholinergic and noradrenergic neuromodulatory systems was used to predict a noisy goal (expected uncertainty) and re-adapt when the goal changed (unexpected uncertainty). This neurobiologically plausible model demonstrates how neuromodulatory systems can predict goals in uncertain domains and how attentional mechanisms can enhance the perception for that goal