The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed.

/pdf/spiking-neuron-models-single-neurons-populations-plasticity-58gzrd6ih6.pdf

Spiking Neuron Models: Single Neurons, Populations, Plasticity

中枢神経系疾患の治療は正常細胞（ニューロン）の機能維持を目的とするが，脳血管障害のように機能障害の原因が細胞の死滅に基づくことは多い．一方，脳腫瘍の治療においては薬物療法や放射線療法といった腫瘍細胞の死滅を目標とするものが大きな位置を占める．いずれの場合にも，細胞死の機序を理解することは各種病態や治療法の理解のうえで重要である．現在のところ最も研究の進んでいる細胞死の型はアポトーシスである．そのなかで重要な位置を占めるミトコンドリアにおける反応および抗アポトーシス因子について概要を紹介する．

Neuroscience 細胞死：最近の知見

Hebbian models of development and learning require both activity-dependent synaptic plasticity and a mechanism that induces competition between different synapses. One form of experimentally observed long-term synaptic plasticity, which we call spike-timing-dependent plasticity (STDP), depends on the relative timing of pre- and postsynaptic action potentials. In modeling studies, we find that this form of synaptic modification can automatically balance synaptic strengths to make postsynaptic firing irregular but more sensitive to presynaptic spike timing. It has been argued that neurons in vivo operate in such a balanced regime. Synapses modifiable by STDP compete for control of the timing of postsynaptic action potentials. Inputs that fire the postsynaptic neuron with short latency or that act in correlated groups are able to compete most successfully and develop strong synapses, while synapses of longer-latency or less-effective inputs are weakened.

/pdf/competitive-hebbian-learning-through-spike-timing-dependent-17innjp0ag.pdf

Competitive Hebbian learning through spike-timing-dependent synaptic plasticity

A paradox that exists in auditory and electrosensory neural systems is that they encode behaviorally relevant signals in the range of a few microseconds with neurons that are at least one order of magnitude slower. The importance of temporal coding in neural information processing is not clear yet. A central question is whether neuronal firing can be more precise than the time constants of the neuronal processes involved. Here we address this problem using the auditory system of the barn owl as an example. We present a modelling study based on computer simulations of a neuron in the laminar nucleus. Three observations explain the paradox. First, spiking of an 'integrate-and-fire' neuron driven by excitatory postsynaptic potentials with a width at half-maximum height of 250 micros, has an accuracy of 25 micros if the presynaptic signals arrive coherently. Second, the necessary degree of coherence in the signal arrival times can be attained during ontogenetic development by virtue of an unsupervised hebbian learning rule. Learning selects connections with matching delays from a broad distribution of axons with random delays. Third, the learning rule also selects the correct delays from two independent groups of inputs, for example, from the left and right ear.

/pdf/a-neuronal-learning-rule-for-sub-millisecond-temporal-coding-57369ghp02.pdf

A neuronal learning rule for sub-millisecond temporal coding

Neural encoding of binocular disparity: energy models, position shifts and phase shifts.

The eyes of moving animals experience a continuously changing flow of stimulation. Information contained in this ‘optical flow field’ can be extracted to control movement1–4. I present here evidence that pre-landing deceleration in flies is determined by such information: onset of deceleration is triggered when the ratio of the image expansion of a target on the retina to the image size (‘relative retinal expansion velocity’, RREV) reaches a critical value.

Flow-field variables trigger landing in flies

This paper investigates the role of the central nucleus of the barn owl's inferior colliculus in determination of the sound-source azimuth. The central nucleus contains many neurons that are sensitive to interaural time difference (ITD), the cue for azimuth in the barn owl. The response of these neurons varies in a cyclic manner with the ITD of a tone or noise burst. Response maxima recur at integer multiples of the period of the stimulating tone, or, if the stimulus is noise, at integer multiples of the period corresponding to the neuron's best frequency. Such neurons can signal, by means of their relative spike rate, the phase difference between the sounds reaching the left and right ears. Since an interaural phase difference corresponds to more than one ITD, these neurons represent ITD ambiguously. We call this phenomenon phase ambiguity. The central nucleus is tonotopically organized and its neurons are narrowly tuned to frequency. Neurons in an array perpendicular to isofrequency laminae form a physiological and anatomical unit; only one ITD, the array-specific ITD, activates all neurons in an array at the same relative level. We, therefore, may say that, in the central nucleus, an ITD is conserved in an array of neurons. Array-specific ITDs are mapped and encompass the entire auditory space of the barn owl. Individual space-specific neurons of the external nucleus, which receive inputs from a wide range of frequency channels (Knudsen and Konishi, 1978), are selective for a unique ITD. Space-specific neurons do not show phase ambiguity when stimulated with noise (Takahashi and Konishi, 1986). Space-specific neurons receive inputs from arrays that are selective for the same ITD. The collective response of the neurons in an array may be the basis for the absence of phase ambiguity in space-specific neurons.

https://www.jneurosci.org/content/jneuro/7/10/3105.full.pdf

Representation of interaural time difference in the central nucleus of the barn owl's inferior colliculus.

In this work, an acoustic feature set based on a gammatone filterbank is introduced for large vocabulary speech recognition. The gammatone features presented here lead to competitive results on the EPPS English task, and considerable improvements were obtained by subsequent combination to a number of standard acoustic features, i.e. MFCC, PLP, MF-PLP, and VTLN plus voicedness. Best results were obtained when combining gammatone features to all other features using weighted ROVER, resulting in a relative improvement of about 12% in word error rate compared to the best single feature system. We also found that ROVER gives better results for feature combination than both log-linear model combination and LDA.

/pdf/gammatone-features-and-feature-combination-for-large-q8ohb7zfpy.pdf

Hermann Wagner

Papers

A neuronal learning rule for sub-millisecond temporal coding

Neural encoding of binocular disparity: energy models, position shifts and phase shifts.

Flow-field variables trigger landing in flies

Representation of interaural time difference in the central nucleus of the barn owl's inferior colliculus.

Gammatone Features and Feature Combination for Large Vocabulary Speech Recognition