Residual dynamics resolves recurrent contributions to neural computation

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals—that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations

Femtosecond nonlinear ultrasonics in gold probed with ultrashort surface plasmons

Fundamental interactions induced by lattice vibrations on ultrafast time scales become increasingly important for modern nanoscience and technology. Experimental access to the physical properties of acoustic phonons in the THz frequency range and over the entire Brillouin zone is crucial for understanding electric and thermal transport in solids and their compounds. Here, we report on the generation and nonlinear propagation of giant (1 percent) acoustic strain pulses in hybrid gold/cobalt bilayer structures probed with ultrafast surface plasmon interferometry. This new technique allows for unambiguous characterization of arbitrary ultrafast acoustic transients. The giant acoustic pulses experience substantial nonlinear reshaping already after a propagation distance of 100 nm in a crystalline gold layer. Excellent agreement with the Korteveg-de Vries model points to future quantitative nonlinear femtosecond THz-ultrasonics at the nano-scale in metals at room temperature

Magnetic polarons in weakly doped high-Tc superconductors

We consider a spin Hamiltonian describing $d$-$d$ exchange interactions between localized spins $d$ of a finite antiferromagnet as well as $p$-$d$ interactions between a conducting hole ($p$) and localized spins. The spin Hamiltonian is solved numerically with use of Lanczos method of diagonalization. We conclude that $p$-$d$ exchange interaction leads to localization of magnetic polarons. Quantum fluctuations of the antiferromagnet strengthen this effect and make the formation of polarons localized in one site possible even for weak $p$-$d$ coupling. Total energy calculations, including the kinetic energy, do not change essentially the phase diagram of magnetic polarons formation. For parameters reasonable for high-$T_c$ superconductors either a polaron localized on one lattice cell or a small ferron can form. For reasonable values of the dielectric function and $p$-$d$ coupling, the contributions of magnetic and phonon terms in the formation of a polaron in weakly doped high-$T_c$ materials are comparable.Comment: revised, revtex-4, 12 pages 8 eps figure

Direct Observation of Propagating Gigahertz Coherent Guided Acoustic Phonons in Free Standing Single Copper Nanowires

We report on gigahertz acoustic phonon waveguiding in free-standing single copper nanowires studied by femtosecond transient reflectivity measurements. The results are discussed on the basis of the semianalytical resolution of the Pochhammer and Chree equation. The spreading of the generated Gaussian wave packet of two different modes is derived analytically and compared with the observed oscillations of the sample reflectivity. These experiments provide a unique way to independently obtain geometrical and material characterization. This direct observation of coherent guided acoustic phonons in a single nano-object is also the first step toward nanolateral size acoustic transducer and comprehensive studies of the thermal properties of nanowires

TOM40 Mediates Mitochondrial Dysfunction Induced by α-Synuclein Accumulation in Parkinson's Disease.

Alpha-synuclein (α-Syn) accumulation/aggregation and mitochondrial dysfunction play prominent roles in the pathology of Parkinson's disease. We have previously shown that postmortem human dopaminergic neurons from PD brains accumulate high levels of mitochondrial DNA (mtDNA) deletions. We now addressed the question, whether alterations in a component of the mitochondrial import machinery -TOM40- might contribute to the mitochondrial dysfunction and damage in PD. For this purpose, we studied levels of TOM40, mtDNA deletions, oxidative damage, energy production, and complexes of the respiratory chain in brain homogenates as well as in single neurons, using laser-capture-microdissection in transgenic mice overexpressing human wildtype α-Syn. Additionally, we used lentivirus-mediated stereotactic delivery of a component of this import machinery into mouse brain as a novel therapeutic strategy. We report here that TOM40 is significantly reduced in the brain of PD patients and in α-Syn transgenic mice. TOM40 deficits were associated with increased mtDNA deletions and oxidative DNA damage, and with decreased energy production and altered levels of complex I proteins in α-Syn transgenic mice. Lentiviral-mediated overexpression of Tom40 in α-Syn-transgenic mice brains ameliorated energy deficits as well as oxidative burden. Our results suggest that alterations in the mitochondrial protein transport machinery might contribute to mitochondrial impairment in α-Synucleinopathies

Natural images from the birthplace of the human eye

Here we introduce a database of calibrated natural images publicly available through an easy-to-use web interface. Using a Nikon D70 digital SLR camera, we acquired about 5000 six-megapixel images of Okavango Delta of Botswana, a tropical savanna habitat similar to where the human eye is thought to have evolved. Some sequences of images were captured unsystematically while following a baboon troop, while others were designed to vary a single parameter such as aperture, object distance, time of day or position on the horizon. Images are available in the raw RGB format and in grayscale. Images are also available in units relevant to the physiology of human cone photoreceptors, where pixel values represent the expected number of photoisomerizations per second for cones sensitive to long (L), medium (M) and short (S) wavelengths. This database is distributed under a Creative Commons Attribution-Noncommercial Unported license to facilitate research in computer vision, psychophysics of perception, and visual neuroscience.Comment: Submitted to PLoS ON

An Image Statistics–Based Model for Fixation Prediction

The problem of predicting where people look at, or equivalently salient region detection, has been related to the statistics of several types of low-level image features. Among these features, contrast and edge information seem to have the highest correlation with the fixation locations. The contrast distribution of natural images can be adequately characterized using a two-parameter Weibull distribution. This distribution catches the structure of local contrast and edge frequency in a highly meaningful way. We exploit these observations and investigate whether the parameters of the Weibull distribution constitute a simple model for predicting where people fixate when viewing natural images. Using a set of images with associated eye movements, we assess the joint distribution of the Weibull parameters at fixated and non-fixated regions. Then, we build a simple classifier based on the log-likelihood ratio between these two joint distributions. Our results show that as few as two values per image region are already enough to achieve a performance comparable with the state-of-the-art in bottom-up saliency prediction

Efficient Temporal Processing of Naturalistic Sounds

In this study, we investigate the ability of the mammalian auditory pathway to adapt its strategy for temporal processing under natural stimulus conditions. We derive temporal receptive fields from the responses of neurons in the inferior colliculus to vocalization stimuli with and without additional ambient noise. We find that the onset of ambient noise evokes a change in receptive field dynamics that corresponds to a change from bandpass to lowpass temporal filtering. We show that these changes occur within a few hundred milliseconds of the onset of the noise and are evident across a range of overall stimulus intensities. Using a simple model, we illustrate how these changes in temporal processing exploit differences in the statistical properties of vocalizations and ambient noises to increase the information in the neural response in a manner consistent with the principles of efficient coding

Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing