264 research outputs found
Weak pairwise correlations imply strongly correlated network states in a neural population
Biological networks have so many possible states that exhaustive sampling is
impossible. Successful analysis thus depends on simplifying hypotheses, but
experiments on many systems hint that complicated, higher order interactions
among large groups of elements play an important role. In the vertebrate
retina, we show that weak correlations between pairs of neurons coexist with
strongly collective behavior in the responses of ten or more neurons.
Surprisingly, we find that this collective behavior is described quantitatively
by models that capture the observed pairwise correlations but assume no higher
order interactions. These maximum entropy models are equivalent to Ising
models, and predict that larger networks are completely dominated by
correlation effects. This suggests that the neural code has associative or
error-correcting properties, and we provide preliminary evidence for such
behavior. As a first test for the generality of these ideas, we show that
similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and
Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah
(http://cosyne.org
Frequency decoding of periodically timed action potentials through distinct activity patterns in a random neural network
Frequency discrimination is a fundamental task of the auditory system. The
mammalian inner ear, or cochlea, provides a place code in which different
frequencies are detected at different spatial locations. However, a temporal
code based on spike timing is also available: action potentials evoked in an
auditory-nerve fiber by a low-frequency tone occur at a preferred phase of the
stimulus-they exhibit phase locking-and thus provide temporal information about
the tone's frequency. In an accompanying psychoacoustic study, and in agreement
with previous experiments, we show that humans employ this temporal information
for discrimination of low frequencies. How might such temporal information be
read out in the brain? Here we demonstrate that recurrent random neural
networks in which connections between neurons introduce characteristic time
delays, and in which neurons require temporally coinciding inputs for spike
initiation, can perform sharp frequency discrimination when stimulated with
phase-locked inputs. Although the frequency resolution achieved by such
networks is limited by the noise in phase locking, the resolution for realistic
values reaches the tiny frequency difference of 0.2% that has been measured in
humans.Comment: 16 pages, 5 figures, and supplementary informatio
Synchronization of Integrate and Fire oscillators with global coupling
In this article we study the behavior of globally coupled assemblies of a
large number of Integrate and Fire oscillators with excitatory pulse-like
interactions. On some simple models we show that the additive effects of pulses
on the state of Integrate and Fire oscillators are sufficient for the
synchronization of the relaxations of all the oscillators. This synchronization
occurs in two forms depending on the system: either the oscillators evolve ``en
bloc'' at the same phase and therefore relax together or the oscillators do not
remain in phase but their relaxations occur always in stable avalanches. We
prove that synchronization can occur independently of the convexity or
concavity of the oscillators evolution function. Furthermore the presence of
disorder, up to some level, is not only compatible with synchronization, but
removes some possible degeneracy of identical systems and allows new mechanisms
towards this state.Comment: 37 pages, 19 postscript figures, Latex 2
Synchronisation in networks of delay-coupled type-I excitable systems
We use a generic model for type-I excitability (known as the SNIPER or SNIC
model) to describe the local dynamics of nodes within a network in the presence
of non-zero coupling delays. Utilising the method of the Master Stability
Function, we investigate the stability of the zero-lag synchronised dynamics of
the network nodes and its dependence on the two coupling parameters, namely the
coupling strength and delay time. Unlike in the FitzHugh-Nagumo model (a model
for type-II excitability), there are parameter ranges where the stability of
synchronisation depends on the coupling strength and delay time. One important
implication of these results is that there exist complex networks for which the
adding of inhibitory links in a small-world fashion may not only lead to a loss
of stable synchronisation, but may also restabilise synchronisation or
introduce multiple transitions between synchronisation and desynchronisation.
To underline the scope of our results, we show using the Stuart-Landau model
that such multiple transitions do not only occur in excitable systems, but also
in oscillatory ones.Comment: 10 pages, 9 figure
Microguards and micromessengers of the genome
The regulation of gene expression is of fundamental importance to maintain organismal function and integrity and requires a multifaceted and highly ordered sequence of events. The cyclic nature of gene expression is known as ‘transcription dynamics’. Disruption or perturbation of these dynamics can result in significant fitness costs arising from genome instability, accelerated ageing and disease. We review recent research that supports the idea that an important new role for small RNAs, particularly microRNAs (miRNAs), is in protecting the genome against short-term transcriptional fluctuations, in a process we term ‘microguarding’. An additional emerging role for miRNAs is as ‘micromessengers’—through alteration of gene expression in target cells to which they are trafficked within microvesicles. We describe the scant but emerging evidence that miRNAs can be moved between different cells, individuals and even species, to exert biologically significant responses. With these two new roles, miRNAs have the potential to protect against deleterious gene expression variation from perturbation and to themselves perturb the expression of genes in target cells. These interactions between cells will frequently be subject to conflicts of interest when they occur between unrelated cells that lack a coincidence of fitness interests. Hence, there is the potential for miRNAs to represent both a means to resolve conflicts of interest, as well as instigate them. We conclude by exploring this conflict hypothesis, by describing some of the initial evidence consistent with it and proposing new ideas for future research into this exciting topic
An associative memory of Hodgkin-Huxley neuron networks with Willshaw-type synaptic couplings
An associative memory has been discussed of neural networks consisting of
spiking N (=100) Hodgkin-Huxley (HH) neurons with time-delayed couplings, which
memorize P patterns in their synaptic weights. In addition to excitatory
synapses whose strengths are modified after the Willshaw-type learning rule
with the 0/1 code for quiescent/active states, the network includes uniform
inhibitory synapses which are introduced to reduce cross-talk noises. Our
simulations of the HH neuron network for the noise-free state have shown to
yield a fairly good performance with the storage capacity of for the low neuron activity of . This
storage capacity of our temporal-code network is comparable to that of the
rate-code model with the Willshaw-type synapses. Our HH neuron network is
realized not to be vulnerable to the distribution of time delays in couplings.
The variability of interspace interval (ISI) of output spike trains in the
process of retrieving stored patterns is also discussed.Comment: 15 pages, 3 figures, changed Titl
Seasonal changes in patterns of gene expression in avian song control brain regions.
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Photoperiod and hormonal cues drive dramatic seasonal changes in structure and function of the avian song control system. Little is known, however, about the patterns of gene expression associated with seasonal changes. Here we address this issue by altering the hormonal and photoperiodic conditions in seasonally-breeding Gambel's white-crowned sparrows and extracting RNA from the telencephalic song control nuclei HVC and RA across multiple time points that capture different stages of growth and regression. We chose HVC and RA because while both nuclei change in volume across seasons, the cellular mechanisms underlying these changes differ. We thus hypothesized that different genes would be expressed between HVC and RA. We tested this by using the extracted RNA to perform a cDNA microarray hybridization developed by the SoNG initiative. We then validated these results using qRT-PCR. We found that 363 genes varied by more than 1.5 fold (>log(2) 0.585) in expression in HVC and/or RA. Supporting our hypothesis, only 59 of these 363 genes were found to vary in both nuclei, while 132 gene expression changes were HVC specific and 172 were RA specific. We then assigned many of these genes to functional categories relevant to the different mechanisms underlying seasonal change in HVC and RA, including neurogenesis, apoptosis, cell growth, dendrite arborization and axonal growth, angiogenesis, endocrinology, growth factors, and electrophysiology. This revealed categorical differences in the kinds of genes regulated in HVC and RA. These results show that different molecular programs underlie seasonal changes in HVC and RA, and that gene expression is time specific across different reproductive conditions. Our results provide insights into the complex molecular pathways that underlie adult neural plasticity
Analysis of cancer metabolism with high-throughput technologies
<p>Abstract</p> <p>Background</p> <p>Recent advances in genomics and proteomics have allowed us to study the nuances of the Warburg effect – a long-standing puzzle in cancer energy metabolism – at an unprecedented level of detail. While modern next-generation sequencing technologies are extremely powerful, the lack of appropriate data analysis tools makes this study difficult. To meet this challenge, we developed a novel application for comparative analysis of gene expression and visualization of RNA-Seq data.</p> <p>Results</p> <p>We analyzed two biological samples (normal human brain tissue and human cancer cell lines) with high-energy, metabolic requirements. We calculated digital topology and the copy number of every expressed transcript. We observed subtle but remarkable qualitative and quantitative differences between the citric acid (TCA) cycle and glycolysis pathways. We found that in the first three steps of the TCA cycle, digital expression of aconitase 2 (<it>ACO2</it>) in the brain exceeded both citrate synthase (<it>CS</it>) and isocitrate dehydrogenase 2 (<it>IDH2</it>), while in cancer cells this trend was quite the opposite. In the glycolysis pathway, all genes showed higher expression levels in cancer cell lines; and most notably, digital gene expression of glyceraldehyde-3-phosphate dehydrogenase (<it>GAPDH</it>) and enolase (<it>ENO</it>) were considerably increased when compared to the brain sample.</p> <p>Conclusions</p> <p>The variations we observed should affect the rates and quantities of ATP production. We expect that the developed tool will provide insights into the subtleties related to the causality between the Warburg effect and neoplastic transformation. Even though we focused on well-known and extensively studied metabolic pathways, the data analysis and visualization pipeline that we developed is particularly valuable as it is global and pathway-independent.</p
Summation of connectivity strengths in the visual cortex reveals stability of neuronal microcircuits after plasticity
Abstract : Background: Within sensory systems, neurons are continuously affected by environmental stimulation. Recently, we showed that, on cell-pair basis, visual adaptation modulates the connectivity strength between similarly tuned neurons to orientation and we suggested that, on a larger scale, the connectivity strength between neurons forming sub-networks could be maintained after adaptation-induced-plasticity. In the present paper, based on the summation of the connectivity strengths, we sought to examine how, within cell-assemblies, functional connectivity is regulated during an exposure-based adaptation. Results: Using intrinsic optical imaging combined with electrophysiological recordings following the reconfiguration of the maps of the primary visual cortex by long stimulus exposure, we found that within functionally connected cells, the summed connectivity strengths remain almost equal although connections among individual pairs are modified. Neuronal selectivity appears to be strongly associated with neuronal connectivity in a “homeodynamic” manner which maintains the stability of cortical functional relationships after experience-dependent plasticity. Conclusions: Our results support the “homeostatic plasticity concept” giving new perspectives on how the summation in visual cortex leads to the stability within labile neuronal ensembles, depending on the newly acquired properties by neurons
Variability Measures of Positive Random Variables
During the stationary part of neuronal spiking response, the stimulus can be encoded in the firing rate, but also in the statistical structure of the interspike intervals. We propose and discuss two information-based measures of statistical dispersion of the interspike interval distribution, the entropy-based dispersion and Fisher information-based dispersion. The measures are compared with the frequently used concept of standard deviation. It is shown, that standard deviation is not well suited to quantify some aspects of dispersion that are often expected intuitively, such as the degree of randomness. The proposed dispersion measures are not entirely independent, although each describes the interspike intervals from a different point of view. The new methods are applied to common models of neuronal firing and to both simulated and experimental data
- …