95 research outputs found
Brandt, Berg and Militarisation in Nigeria
SUMMARY Nigeria's increasing militarisation is partly a consequence of the government's programme for economic recovery. This programme, influenced by the World Bank model of structural adjustment, aims at ensuring the conditions for long?run capitalist development, by actively seeking the participation of foreign capital. To earn the confidence of foreign capital and control the social consequences of an austerity package an authoritarian government is required. SOMMAIRE Brandt, Berg et la militarisation au Nigéria La militarisation croissante du Nigéria est grandement due au programme gouvernemental pour le redressement économique. Ce programme, influencé par le modèle de la Banque Mondiale d'adjustement structurel, a pour but d'assurer les conditions de développement capitaliste à long terme, en recherchant activement la participation du capital étranger. Afin d'avoir la confiance du capital étranger et de contrôler les conséquences sociales d'un programme d'austérité, un gouvernement authoritaire est obligatoire. RESUMEN Brandt, Berg y la militarización en Nigeria La creciente militarización de Nigeria se debe, en parte, al programa de recuperación económica del gobierno. Este programa — influenciado por el modelo de ajuste estructural del Banco Mundial — tiene como objectivo asegurar condiciones de desarrollo capitalista de largo plazo, mediante una búsqueda activa de la participación del capital extranjero. Se requiere un gobierno autoritario para obtener la confianza del capital extranjero y el control de las consecuencias sociales de un plan de austeridad
Utilizing Light-field Imaging Technology in Neurosurgery
Traditional still cameras can only focus on a single plane for each image while rendering everything outside of that plane out of focus. However, new light-field imaging technology makes it possible to adjust the focus plane after an image has already been captured. This technology allows the viewer to interactively explore an image with objects and anatomy at varying depths and clearly focus on any feature of interest by selecting that location during post-capture viewing. These images with adjustable focus can serve as valuable educational tools for neurosurgical residents. We explore the utility of light-field cameras and review their strengths and limitations compared to other conventional types of imaging. The strength of light-field images is the adjustable focus, as opposed to the fixed-focus of traditional photography and video. A light-field image also is interactive by nature, as it requires the viewer to select the plane of focus and helps with visualizing the three-dimensional anatomy of an image. Limitations include the relatively low resolution of light-field images compared to traditional photography and video. Although light-field imaging is still in its infancy, there are several potential uses for the technology to complement traditional still photography and videography in neurosurgical education
Utilizing Light-field Imaging Technology in Neurosurgery
Traditional still cameras can only focus on a single plane for each image while rendering everything outside of that plane out of focus. However, new light-field imaging technology makes it possible to adjust the focus plane after an image has already been captured. This technology allows the viewer to interactively explore an image with objects and anatomy at varying depths and clearly focus on any feature of interest by selecting that location during post-capture viewing. These images with adjustable focus can serve as valuable educational tools for neurosurgical residents. We explore the utility of light-field cameras and review their strengths and limitations compared to other conventional types of imaging. The strength of light-field images is the adjustable focus, as opposed to the fixed-focus of traditional photography and video. A light-field image also is interactive by nature, as it requires the viewer to select the plane of focus and helps with visualizing the three-dimensional anatomy of an image. Limitations include the relatively low resolution of light-field images compared to traditional photography and video. Although light-field imaging is still in its infancy, there are several potential uses for the technology to complement traditional still photography and videography in neurosurgical education
Searching for simplicity: Approaches to the analysis of neurons and behavior
What fascinates us about animal behavior is its richness and complexity, but
understanding behavior and its neural basis requires a simpler description.
Traditionally, simplification has been imposed by training animals to engage in
a limited set of behaviors, by hand scoring behaviors into discrete classes, or
by limiting the sensory experience of the organism. An alternative is to ask
whether we can search through the dynamics of natural behaviors to find
explicit evidence that these behaviors are simpler than they might have been.
We review two mathematical approaches to simplification, dimensionality
reduction and the maximum entropy method, and we draw on examples from
different levels of biological organization, from the crawling behavior of C.
elegans to the control of smooth pursuit eye movements in primates, and from
the coding of natural scenes by networks of neurons in the retina to the rules
of English spelling. In each case, we argue that the explicit search for
simplicity uncovers new and unexpected features of the biological system, and
that the evidence for simplification gives us a language with which to phrase
new questions for the next generation of experiments. The fact that similar
mathematical structures succeed in taming the complexity of very different
biological systems hints that there is something more general to be discovered
Stimulus-dependent maximum entropy models of neural population codes
Neural populations encode information about their stimulus in a collective
fashion, by joint activity patterns of spiking and silence. A full account of
this mapping from stimulus to neural activity is given by the conditional
probability distribution over neural codewords given the sensory input. To be
able to infer a model for this distribution from large-scale neural recordings,
we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal
extension of the canonical linear-nonlinear model of a single neuron, to a
pairwise-coupled neural population. The model is able to capture the
single-cell response properties as well as the correlations in neural spiking
due to shared stimulus and due to effective neuron-to-neuron connections. Here
we show that in a population of 100 retinal ganglion cells in the salamander
retina responding to temporal white-noise stimuli, dependencies between cells
play an important encoding role. As a result, the SDME model gives a more
accurate account of single cell responses and in particular outperforms
uncoupled models in reproducing the distributions of codewords emitted in
response to a stimulus. We show how the SDME model, in conjunction with static
maximum entropy models of population vocabulary, can be used to estimate
information-theoretic quantities like surprise and information transmission in
a neural population.Comment: 11 pages, 7 figure
Gibbs distribution analysis of temporal correlations structure in retina ganglion cells
We present a method to estimate Gibbs distributions with
\textit{spatio-temporal} constraints on spike trains statistics. We apply this
method to spike trains recorded from ganglion cells of the salamander retina,
in response to natural movies. Our analysis, restricted to a few neurons,
performs more accurately than pairwise synchronization models (Ising) or the
1-time step Markov models (\cite{marre-boustani-etal:09}) to describe the
statistics of spatio-temporal spike patterns and emphasizes the role of higher
order spatio-temporal interactions.Comment: To appear in J. Physiol. Pari
Are biological systems poised at criticality?
Many of life's most fascinating phenomena emerge from interactions among many
elements--many amino acids determine the structure of a single protein, many
genes determine the fate of a cell, many neurons are involved in shaping our
thoughts and memories. Physicists have long hoped that these collective
behaviors could be described using the ideas and methods of statistical
mechanics. In the past few years, new, larger scale experiments have made it
possible to construct statistical mechanics models of biological systems
directly from real data. We review the surprising successes of this "inverse"
approach, using examples form families of proteins, networks of neurons, and
flocks of birds. Remarkably, in all these cases the models that emerge from the
data are poised at a very special point in their parameter space--a critical
point. This suggests there may be some deeper theoretical principle behind the
behavior of these diverse systems.Comment: 21 page
Genotype to phenotype mapping and the fitness landscape of the E. coli lac promoter
Genotype-to-phenotype maps and the related fitness landscapes that include
epistatic interactions are difficult to measure because of their high
dimensional structure. Here we construct such a map using the recently
collected corpora of high-throughput sequence data from the 75 base pairs long
mutagenized E. coli lac promoter region, where each sequence is associated with
its phenotype, the induced transcriptional activity measured by a fluorescent
reporter. We find that the additive (non-epistatic) contributions of individual
mutations account for about two-thirds of the explainable phenotype variance,
while pairwise epistasis explains about 7% of the variance for the full
mutagenized sequence and about 15% for the subsequence associated with protein
binding sites. Surprisingly, there is no evidence for third order epistatic
contributions, and our inferred fitness landscape is essentially single peaked,
with a small amount of antagonistic epistasis. There is a significant selective
pressure on the wild type, which we deduce to be multi-objective optimal for
gene expression in environments with different nutrient sources. We identify
transcription factor (CRP) and RNA polymerase binding sites in the promotor
region and their interactions without difficult optimization steps. In
particular, we observe evidence for previously unexplored genetic regulatory
mechanisms, possibly kinetic in nature. We conclude with a cautionary note that
inferred properties of fitness landscapes may be severely influenced by biases
in the sequence data
Representation of Dynamical Stimuli in Populations of Threshold Neurons
Many sensory or cognitive events are associated with dynamic current modulations in cortical neurons. This raises an urgent demand for tractable model approaches addressing the merits and limits of potential encoding strategies. Yet, current theoretical approaches addressing the response to mean- and variance-encoded stimuli rarely provide complete response functions for both modes of encoding in the presence of correlated noise. Here, we investigate the neuronal population response to dynamical modifications of the mean or variance of the synaptic bombardment using an alternative threshold model framework. In the variance and mean channel, we provide explicit expressions for the linear and non-linear frequency response functions in the presence of correlated noise and use them to derive population rate response to step-like stimuli. For mean-encoded signals, we find that the complete response function depends only on the temporal width of the input correlation function, but not on other functional specifics. Furthermore, we show that both mean- and variance-encoded signals can relay high-frequency inputs, and in both schemes step-like changes can be detected instantaneously. Finally, we obtain the pairwise spike correlation function and the spike triggered average from the linear mean-evoked response function. These results provide a maximally tractable limiting case that complements and extends previous results obtained in the integrate and fire framework
- …