A Neural Computation for Visual Acuity in the Presence of Eye Movements

Humans can distinguish visual stimuli that differ by features the size of only a few photoreceptors. This is possible despite the incessant image motion due to fixational eye movements, which can be many times larger than the features to be distinguished. To perform well, the brain must identify the retinal firing patterns induced by the stimulus while discounting similar patterns caused by spontaneous retinal activity. This is a challenge since the trajectory of the eye movements, and consequently, the stimulus position, are unknown. We derive a decision rule for using retinal spike trains to discriminate between two stimuli, given that their retinal image moves with an unknown random walk trajectory. This algorithm dynamically estimates the probability of the stimulus at different retinal locations, and uses this to modulate the influence of retinal spikes acquired later. Applied to a simple orientation-discrimination task, the algorithm performance is consistent with human acuity, whereas naive strategies that neglect eye movements perform much worse. We then show how a simple, biologically plausible neural network could implement this algorithm using a local, activity-dependent gain and lateral interactions approximately matched to the statistics of eye movements. Finally, we discuss evidence that such a network could be operating in the primary visual cortex

Generalization of graph network inferences in higher-order probabilistic graphical models

Probabilistic graphical models provide a powerful tool to describe complex statistical structure, with many real-world applications in science and engineering from controlling robotic arms to understanding neuronal computations. A major challenge for these graphical models is that inferences such as marginalization are intractable for general graphs. These inferences are often approximated by a distributed message-passing algorithm such as Belief Propagation, which does not always perform well on graphs with cycles, nor can it always be easily specified for complex continuous probability distributions. Such difficulties arise frequently in expressive graphical models that include intractable higher-order interactions. In this paper we construct iterative message-passing algorithms using Graph Neural Networks defined on factor graphs to achieve fast approximate inference on graphical models that involve many-variable interactions. Experimental results on several families of graphical models demonstrate the out-of-distribution generalization capability of our method to different sized graphs, and indicate the domain in which our method gains advantage over Belief Propagation.Comment: 9 pages, 2 figure

Results from the First World-Wide Web Survey

The explosion of World-Wide Web (WWW) across the Internet is staggering, both in terms of number of users and the amount of activity. However, to date, no reliable characterization exists of WWW users. In this paper, we report results from a survey that was posted on the Web for a month, in January of 1994. There were several goals motivating our survey. First, we wished to demonstrate a proof of concept for WWW technologies as a useful survey medium. Second, we wanted to bet a-test the design and content of surveys dealing with the Web. Third, as mentioned, we hoped to begin to describe the ra nge of Web users. In one month, we had over 4,700 respondents to our survey. Their responses helped us to begin to characterize WWW users, their reasons for using the WWW, and their opinions of WWW tools and technologies