On the Marginal Utility of Deploying Measurement Infrastructure

The cost and complexity of deploying measurement infrastructure in the Internet for the purpose of analyzing its structure and behavior is considerable. Basic questions about the utility of increasing the number of measurements and/or measurement sites have not yet been addressed which has lead to a "more is better" approach to wide-area measurements. In this paper, we quantify the marginal utility of performing wide-area measurements in the context of Internet topology discovery. We characterize topology in terms of nodes, links, node degree distribution, and end-to-end flows using statistical and information-theoretic techniques. We classify nodes discovered on the routes between a set of 8 sources and 1277 destinations to differentiate nodes which make up the so called "backbone" from those which border the backbone and those on links between the border nodes and destination nodes. This process includes reducing nodes that advertise multiple interfaces to single IP addresses. We show that the utility of adding sources goes down significantly after 2 from the perspective of interface, node, link and node degree discovery. We show that the utility of adding destinations is constant for interfaces, nodes, links and node degree indicating that it is more important to add destinations than sources. Finally, we analyze paths through the backbone and show that shared link distributions approximate a power law indicating that a small number of backbone links in our study are very heavily utilized.

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.