Invariant Temporal Dynamics Underlie Perceptual Stability in Human Visual Cortex.

An inherent limitation of human visual system research stems from its reliance on highly controlled laboratory conditions. Visual processing in the real world differs substantially from such controlled conditions. In particular, during natural vision, we continuously sample the dynamic environment by variable eye movements that lead to inherent instability of the optical image.

On Expression Patterns and Developmental Origin of Human Brain Regions.

Anatomical substructures of the human brain have characteristic cell-types, connectivity and local circuitry, which are reflected in area-specific transcriptome signatures, but the principles governing area-specific transcription and their relation to brain development are still being studied. In adult rodents, areal transcriptome patterns agree with the embryonic origin of brain regions, but the processes and genes that preserve an embryonic signature in regional expression profiles were not quantified. Furthermore, it is not clear how embryonic-origin signatures of adult-brain expression interplay with changes in expression patterns during development.

Learning to classify neural activity from a mouse model of Alzheimer's disease amyloidosis versus controls.

The mechanisms underlying Alzheimer's disease (AD) onset and progression are not yet elucidated. The extent to which alterations in the activity of individual neurons of an AD model are significant, and the phase at which they can be captured, point to the intensity of the pathology and imply the stage at which it can be detected. Using a machine-learning algorithm, we present a successful cell-by-cell classification of intracellularly recorded neurons from the B6C3 APPswe/PS1dE9 AD model, versus wildtypes controls, at both a late stage and at an early stage, when the plaque pathology and behavioral deficits are absent or rare.

Gene Expression Switching of Receptor Subunits in Human Brain Development.

Synaptic receptors in the human brain consist of multiple protein subunits, many of which have multiple variants, coded by different genes, and are differentially expressed across brain regions and developmental stages. The brain can tune the electrophysiological properties of synapses to regulate plasticity and information processing by switching from one protein variant to another. Such condition-dependent variant switch during development has been demonstrated in several neurotransmitter systems including NMDA and GABA.

A unifying principle underlying the extracellular field potential spectral responses in the human cortex.

Electrophysiological mass potentials show complex spectral changes upon neuronal activation. However, it is unknown to what extent these complex band-limited changes are interrelated or, alternatively, reflect separate neuronal processes. To address this question, intracranial electrocorticograms (ECoG) responses were recorded in patients engaged in visuomotor tasks.

Positive correlation between ADAR expression and its targets suggests a complex regulation mediated by RNA editing in the human brain.

A-to-I RNA editing by adenosine deaminases acting on RNA is a post-transcriptional modification that is crucial for normal life and development in vertebrates. RNA editing has been shown to be very abundant in the human transcriptome, specifically at the primate-specific Alu elements. The functional role of this wide-spread effect is still not clear; it is believed that editing of transcripts is a mechanism for their down-regulation via processes such as nuclear retention or RNA degradation.

Amyloid-β disrupts ongoing spontaneous activity in sensory cortex.

Unlabelled: The effect of Alzheimer's disease pathology on activity of individual neocortical neurons in the intact neural network remains obscure. Ongoing spontaneous activity, which constitutes most of neocortical activity, is the background template on which further evoked-activity is superimposed. We compared in vivo intracellular recordings and local field potentials (LFP) of ongoing activity in the barrel cortex of APP/PS1 transgenic mice and age-matched littermate CONTROLS, following significant amyloid-β (Aβ) accumulation and aggregation.

Transitions in the transcriptome of the serotonergic and dopaminergic systems in the human brain during adolescence.

Adolescence is a period of profound neurophysiological, behavioral, cognitive and psychological changes, but not much is known about the underlying molecular neural mechanisms. The aim of this study was to systematically analyze expression levels of the genes forming serotonergic and dopaminergic synapses during adolescence. We analyzed the mRNA expression profiles of genes that code for all components of serotonergic and dopaminergic synapses, in 16 brain areas from human and non-human primates from public domain databases, to detect genes whose expression changes during adolescence.

Specialization of gene expression during mouse brain development.

The transcriptome of the brain changes during development, reflecting processes that determine functional specialization of brain regions. We analyzed gene expression, measured using in situ hybridization across the full developing mouse brain, to quantify functional specialization of brain regions. Surprisingly, we found that during the time that the brain becomes anatomically regionalized in early development, transcription specialization actually decreases reaching a low, "neurotypic", point around birth.

FuncISH: learning a functional representation of neural ISH images.

Motivation: High-spatial resolution imaging datasets of mammalian brains have recently become available in unprecedented amounts. Images now reveal highly complex patterns of gene expression varying on multiple scales. The challenge in analyzing these images is both in extracting the patterns that are most relevant functionally and in providing a meaningful representation that allows neuroscientists to interpret the extracted patterns.

Predicting protein-protein interactions in the post synaptic density.

The post synaptic density (PSD) is a specialization of the cytoskeleton at the synaptic junction, composed of hundreds of different proteins. Characterizing the protein components of the PSD and their interactions can help elucidate the mechanism of long-term changes in synaptic plasticity, which underlie learning and memory. Unfortunately, our knowledge of the proteome and interactome of the PSD is still partial and noisy.

A Bayesian approach for characterizing direction tuning curves in the supplementary motor area of behaving monkeys.

Neural responses are commonly studied in terms of "tuning curves," characterizing changes in neuronal response as a function of a continuous stimulus parameter. In the motor system, neural responses to movement direction often follow a bell-shaped tuning curve for which the exact shape determines the properties of neuronal movement coding. Estimating the shape of that tuning curve robustly is hard, especially when directions are sampled unevenly and at a coarse resolution.

Localizing genes to cerebellar layers by classifying ISH images.

Gene expression controls how the brain develops and functions. Understanding control processes in the brain is particularly hard since they involve numerous types of neurons and glia, and very little is known about which genes are expressed in which cells and brain layers. Here we describe an approach to detect genes whose expression is primarily localized to a specific brain layer and apply it to the mouse cerebellum.

Auditory abstraction from spectro-temporal features to coding auditory entities.

The auditory system extracts behaviorally relevant information from acoustic stimuli. The average activity in auditory cortex is known to be sensitive to spectro-temporal patterns in sounds. However, it is not known whether the auditory cortex also processes more abstract features of sounds, which may be more behaviorally relevant than spectro-temporal patterns.

Sound retrieval and ranking using sparse auditory representations.

To create systems that understand the sounds that humans are exposed to in everyday life, we need to represent sounds with features that can discriminate among many different sound classes. Here, we use a sound-ranking framework to quantitatively evaluate such representations in a large-scale task. We have adapted a machine-vision method, the passive-aggressive model for image retrieval (PAMIR), which efficiently learns a linear mapping from a very large sparse feature space to a large query-term space.

Functional organization of the S. cerevisiae phosphorylation network.

Reversible protein phosphorylation is a signaling mechanism involved in all cellular processes. To create a systems view of the signaling apparatus in budding yeast, we generated an epistatic miniarray profile (E-MAP) comprised of 100,000 pairwise, quantitative genetic interactions, including virtually all protein and small-molecule kinases and phosphatases as well as key cellular regulators. Quantitative genetic interaction mapping reveals factors working in compensatory pathways (negative genetic interactions) or those operating in linear pathways (positive genetic interactions).

Timing of gene expression responses to environmental changes.

Cells respond to environmental perturbations with changes in their gene expression that are coordinated in magnitude and time. Timing information about individual genes, rather than clusters, provides a refined way to view and analyze responses, but it is hard to estimate accurately. To analyze response timing of individual genes, we developed a parametric model that captures the typical temporal responses: an abrupt early response followed by a second transition to a steady state.

Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network.

Significant insight about biological networks arises from the study of network motifs--overly abundant network subgraphs--but such wiring patterns do not specify when and how potential routes within a cellular network are used. To address this limitation, we introduce activity motifs, which capture patterns in the dynamic use of a network. Using this framework to analyze transcription in Saccharomyces cerevisiae metabolism, we find that cells use different timing activity motifs to optimize transcription timing in response to changing conditions: forward activation to produce metabolic compounds efficiently, backward shutoff to rapidly stop production of a detrimental product and synchronized activation for co-production of metabolites required for the same reaction.

Information theory in auditory research.

Mutual information (MI) is in increasing use as a way of quantifying neural responses. However, it is still considered with some doubts by many researchers, because it is not always clear what MI really measures, and because MI is hard to calculate in practice. This paper aims to clarify these issues.

Reduction of information redundancy in the ascending auditory pathway.

Information processing by a sensory system is reflected in the changes in stimulus representation along its successive processing stages. We measured information content and stimulus-induced redundancy in the neural responses to a set of natural sounds in three successive stations of the auditory pathway-inferior colliculus (IC), auditory thalamus (MGB), and primary auditory cortex (A1). Information about stimulus identity was somewhat reduced in single A1 and MGB neurons relative to single IC neurons, when information is measured using spike counts, latency, or temporal spiking patterns.

Discrete profile comparison using information bottleneck.

Sequence homologs are an important source of information about proteins. Amino acid profiles, representing the position-specific mutation probabilities found in profiles, are a richer encoding of biological sequences than the individual sequences themselves. However, profile comparisons are an order of magnitude slower than sequence comparisons, making profiles impractical for large datasets.

Encoding stimulus information by spike numbers and mean response time in primary auditory cortex.

Neurons can transmit information about sensory stimuli via their firing rate, spike latency, or by the occurrence of complex spike patterns. Identifying which aspects of the neural responses actually encode sensory information remains a fundamental question in neuroscience. Here we compared various approaches for estimating the information transmitted by neurons in auditory cortex in two very different experimental paradigms, one measuring spatial tuning and the other responses to complex natural stimuli.

Applying an artificial neural network to warfarin maintenance dose prediction.

Background: Oral anticoagulation with warfarin can lead to life-threatening events as a result of either over-anticoagulation or undertreatment. One of the main contributors to an undesirable warfarin effect is the need to adjust its daily dose for a specific patient. The dose is adjusted empirically based on the experience of the clinician, a method that is often imprecise.

Spike-timing-dependent plasticity and relevant mutual information maximization.

Synaptic plasticity was recently shown to depend on the relative timing of the pre- and postsynaptic spikes. This article analytically derives a spike-dependent learning rule based on the principle of information maximization for a single neuron with spiking inputs. This rule is then transformed into a biologically feasible rule, which is compared to the experimentally observed plasticity.