312 research outputs found
Large-scale multielectrode recording and stimulation of neural activity
Large circuits of neurons are employed by the brain to encode and process information. How this encoding and processing is carried out is one of the central questions in neuroscience. Since individual neurons communicate with each other through electrical signals (action potentials), the recording of neural activity with arrays of extracellular electrodes is uniquely suited for the investigation of this question. Such recordings provide the combination of the best spatial (individual neurons) and temporal (individual action-potentials) resolutions compared to other large-scale imaging methods. Electrical stimulation of neural activity in turn has two very important applications: it enhances our understanding of neural circuits by allowing active interactions with them, and it is a basis for a large variety of neural prosthetic devices. Until recently, the state-of-the-art in neural activity recording systems consisted of several dozen electrodes with inter-electrode spacing ranging from tens to hundreds of microns. Using silicon microstrip detector expertise acquired in the field of high-energy physics, we created a unique neural activity readout and stimulation framework that consists of high-density electrode arrays, multi-channel custom-designed integrated circuits, a data acquisition system, and data-processing software. Using this framework we developed a number of neural readout and stimulation systems: (1) a 512-electrode system for recording the simultaneous activity of as many as hundreds of neurons, (2) a 61-electrode system for electrical stimulation and readout of neural activity in retinas and brain-tissue slices, and (3) a system with telemetry capabilities for recording neural activity in the intact brain of awake, naturally behaving animals. We will report on these systems, their various applications to the field of neurobiology, and novel scientific results obtained with some of them. We will also outline future directions
Statistical mechanics of voting
Decision procedures aggregating the preferences of multiple agents can
produce cycles and hence outcomes which have been described heuristically as
`chaotic'. We make this description precise by constructing an explicit
dynamical system from the agents' preferences and a voting rule. The dynamics
form a one dimensional statistical mechanics model; this suggests the use of
the topological entropy to quantify the complexity of the system. We formulate
natural political/social questions about the expected complexity of a voting
rule and degree of cohesion/diversity among agents in terms of random matrix
models---ensembles of statistical mechanics models---and compute quantitative
answers in some representative cases.Comment: 9 pages, plain TeX, 2 PostScript figures included with epsf.tex
(ignore the under/overfull \vbox error messages
Fast, scalable, Bayesian spike identification for multi-electrode arrays
We present an algorithm to identify individual neural spikes observed on
high-density multi-electrode arrays (MEAs). Our method can distinguish large
numbers of distinct neural units, even when spikes overlap, and accounts for
intrinsic variability of spikes from each unit. As MEAs grow larger, it is
important to find spike-identification methods that are scalable, that is, the
computational cost of spike fitting should scale well with the number of units
observed. Our algorithm accomplishes this goal, and is fast, because it
exploits the spatial locality of each unit and the basic biophysics of
extracellular signal propagation. Human intervention is minimized and
streamlined via a graphical interface. We illustrate our method on data from a
mammalian retina preparation and document its performance on simulated data
consisting of spikes added to experimentally measured background noise. The
algorithm is highly accurate
Catástrofe o nueva sociedad : modelo mundial latinoamericano
Versión en inglés disponible en la Biblioteca Digital del IDRC: Catastrophe or new society? a Latin American world mode
Local and global interactions in an evolutionary resource game
Conditions for the emergence of cooperation in a spatial common-pool resource game are studied. This combines in a unique way local and global interactions. A fixed number of harvesters are located on a spatial grid. Harvesters choose among three strategies: defection, cooperation, and enforcement. Individual payoffs are affected by both global factors, namely, aggregate harvest and resource stock level, and local factors, such as the imposition of sanctions on neighbors by enforcers. The evolution of strategies in the population is driven by social learning through imitation, based on local interaction or locally available information. Numerous types of equilibria exist in these settings. An important new finding is that clusters of cooperators and enforcers can survive among large groups of defectors. We discuss how the results contrast with the non-spatial, but otherwise similar, game of Sethi and Somanathan (American Economic Review 86(4):766–789, 1996)
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