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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 595 records · Page 33Linked to original sources

bioRxiv2015

Bivariate spatial point patterns in the retina: a reproducible review

In this article I present a reproducible review of recent research to investigate the spatial positioning of neurons in the nervous system. In particular, I focus on the relative spatial positioning of pairs of cell types within the retina. I examine three different cases by which two types of neurons might be arranged relative to each other. (1) Cells of different type might be effectively independent of each other. (2) Cells of one type are randomly assigned one of two labels to create two related populations. (3) Interactions between cells of different type generate functional dependencies. I show briefly how spatial statistic techniques can be applied to investigate the nature of spatial interactions between two cell types. Finally, I have termed this article a reproducible review because all the data and computer code are integrated into the manuscript so that others can repeat the analysis presented here. I close the review with a discussion of this concept.

Neuroscience↗

bioRxiv2015

Dynamic information routing in complex networks

Abstract Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this generic mechanism specifically for oscillatory dynamics and analyze how individual unit properties, the network topology and external inputs coact to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine non-local network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function.

Neuroscience↗

bioRxiv2015

Optimal compensation for neuron death

The brain has an impressive ability to withstand neural damage. Diseases that kill neurons can go unnoticed for years, and incomplete brain lesions or silencing of neurons often fail to produce any effect. How does the brain compensate for such damage, and what are the limits of this compensation? We propose that neural circuits optimally compensate for neuron death, thereby preserving their function as much as possible. We show that this compensation can explain changes in tuning curves induced by neuron silencing across a variety of systems, including the primary visual cortex. We find that optimal compensation can be implemented through the dynamics of networks with a tight balance of excitation and inhibition, without requiring synaptic plasticity. The limits of this compensatory mechanism are reached when excitation and inhibition become unbalanced, thereby demarcating a recovery boundary, where signal representation fails and where diseases may become symptomatic.

Neuroscience↗

bioRxiv2015

Ramp coding with population averaging predicts human cortical face-space representations and perception

The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli. In the fusiform face area, a face-space coding model with sigmoidal ramp tuning provided a better account of the data than one based on exemplar tuning. However, an image-processing model with weighted banks of Gabor filters performed similarly. Accounting for the data required the inclusion of a measurement-level population averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings. Our study demonstrates the importance of comparing multiple models and of modeling the measurement process in computational neuroimaging.\n\nAuthor SummaryHumans recognize conspecifics by their faces. Understanding how faces are recognized is an open computational problem with relevance to theories of perception, social cognition, and the engineering of computer vision systems. Here we measured brain activity with functional MRI while human participants viewed individual faces. We developed multiple computational models inspired by known response preferences of single neurons in the primate visual cortex. We then compared these neuronal models to patterns of brain activity corresponding to individual faces. The data were consistent with a model where neurons respond to directions in a high-dimensional space of faces. It also proved essential to model how functional MRI voxels locally average the responses of tens of thousands of neurons. The study highlights the challenges in adjudicating between alternative computational theories of visual information processing.

Neuroscience↗

bioRxiv2015

AN EXTENSION OF THE FRI FRAMEWORK FOR CALCIUM TRANSIENT DETECTION

Two-photon calcium imaging of the brain allows the spatiotemporal activity of neuronal networks to be monitored at cellular resolution. In order to analyse this activity it must first be possible to detect, with high temporal resolution, spikes from the time series corresponding to single neurons. Previous work has shown that finite rate of innovation (FRI) theory can be used to reconstruct spike trains from noisy calcium imaging data. In this paper we extend the FRI framework for spike detection from calcium imaging data to encompass data generated by a larger class of calcium indicators, including the genetically encoded indicator GCaMP6s. Furthermore, we implement least squares model-order estimation and perform a noise reduction procedure ( pre-whitening) in order to increase the robustness of the algorithm. We demonstrate high spike detection performance on real data generated by GCaMP6s, detecting 90% of electrophysiologically-validated spikes.\n\nIndex Terms-- Calcium imaging, Calcium transient detection, Finite rate of innovation, GCaMP6s

Neuroscience↗

bioRxiv2015

Deep neural networks: a new framework for modelling biological vision and brain information processing

Recent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals and not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build neurobiologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Neuroscience↗

bioRxiv2015

Balanced Oscillatory Coupling Improves Information Flow

All animals are able to rapidly change their behavior. The neural basis of such flexibility requires that groups of distant neural ensembles rapidly alter communications with selectivity and fidelity. Low frequency oscillations are a strong candidate for how neurons coordinate communication via the dynamic instantiation of functional networks. These dynamic networks are argued to rapidly guide the flow of information, with the presumption that stronger oscillations more strongly influence information flow. Surprisingly, there is scant evidence or theoretical support for how oscillatory activity might enhance information flow. Here we introduce a novel computational model for oscillatory neural communication and show that, rather than the strength of the oscillation, it is the balance between excitatory and inhibitory neuronal activity that has the largest effect on information flow. When coupling between an oscillation and spiking has balanced excitatory-inhibitory inputs, information flow is enhanced via improved discriminability between signal and noise. In contrast, when coupling is unbalanced, driven either by excessive excitation or inhibition, information flow is obstructed, regardless of the strength of the oscillation. A multitude of neuropathologies, including Parkinsons disease, schizophrenia, and autism, are associated with oscillatory disruptions and excitation-inhibition imbalances. Our results show that understanding the distinction between balanced and unbalanced oscillatory coupling offers a unifying mechanistic framework for understanding effective neural communication and its disruption in neuropathology.

Neuroscience↗

bioRxiv2015

Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.

Neuroscience↗

bioRxiv2015

Faster processing of moving compared to flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion

When the brain has determined the position of a moving object, due to anatomical and processing delays, the object will have already moved to a new location. Given the statistical regularities present in natural motion, the brain may have acquired compensatory mechanisms to minimize the mismatch between the perceived and the real position of a moving object. A well-known visual illusion -- the flash lag effect -- points towards such a possibility. Although many psychophysical models have been suggested to explain this illusion, their predictions have not been tested at the neural level, particularly in a species of animal known to perceive the illusion. Towards this, we recorded neural responses to flashed and moving bars from primary visual cortex (V1) of awake, fixating macaque monkeys. We found that the response latency to moving bars of varying speed, motion direction and luminance was shorter than that to flashes, in a manner that is consistent with psychophysical results. At the level of V1, our results support the differential latency model positing that flashed and moving bars have different latencies. As we found a neural correlate of the illusion in passively fixating monkeys, our results also suggest that judging the instantaneous position of the moving bar at the time of flash -- as required by the postdiction/motion-biasing model -- may not be necessary for observing a neural correlate of the illusion. Our results also suggest that the brain may have evolved mechanisms to process moving stimuli faster and closer to real time compared with briefly appearing stationary stimuli.\n\nNew and NoteworthyWe report several observations in awake macaque V1 that provide support for the differential latency model of the flash lag illusion. We find that the equal latency of flash and moving stimuli as assumed by motion integration/postdiction models does not hold in V1. We show that in macaque V1, motion processing latency depends on stimulus luminance, speed and motion direction in a manner consistent with several psychophysical properties of the flash lag illusion.

Neuroscience↗

bioRxiv2015

Cortical state and natural movie responses in cat visual cortex

AbstractHow does cortical state affect neural responses to naturalistic stimuli, and is it analogous between anesthetized and awake animals? We recorded spikes and local field potential (LFP) from all layers of isoflurane-anesthetized cat primary visual cortex (V1) while repeatedly presenting wide-field natural scene movie clips. Spiking responses of single units were remarkably precise, reliable and sparse, with lognormally distributed mean firing rates. Many units had distinct barcode-like firing patterns, with features as little as 10 ms wide. LFP-derived cortical state switched spontaneously between synchronized (1/f) and desynchronized (broadband). Surprisingly, responses were more precise, reliable and sparse during the synchronized than desynchronized state. Because the desynchronized state under anesthesia is thought to correspond to attending periods in awake animals, during which responses are enhanced, our results complicate the analogy between cortical states in anesthetized and awake animals. The presenceof orientation maps in cat V1 may explain contrary reports in anesthetized rodents, and predicts a similar result in anesthetized ferret and primate V1.\n\nSignificance StatementGlobal brain activity changes spontaneously over time and can be characterized along a spectrum from slow synchronized activity, to fast desynchronized activity. This spectrum is similar in awake, asleep and anesthetized animals, but is its effect on neural responses the same in all cases? Here we show that neural responses to natural movies in anesthetized cat visual cortex are more precise during synchronized activity. This is contrary to reports in anesthetized rodents, which we suggest may be due to greater columnar organization in cat visual cortex. Since this is also contrary to enhanced responses and behavioural performance during attention, when activity is desynchronized, our results suggest that similar brain states in awake and anesthetized animals may not be functionally analogous.

Neuroscience↗

bioRxiv2015

A protein aggregation-dependent scaling-free synaptic facilitation rule for neural network simulations

The regulation of mRNA translation at synaptic level is believed to be fundamental in memory and learning at cellular level. A family of RNA binding proteins (RBPs) which emerged to be important during development and in adult neurons is the one of Cytoplasmic Polyadenylation Element Binding proteins (CPEBs). Drosophila Orb2 (homolog of vertebrate CPEB2 protein and of the neural isoform of Aplysia CPEB) has been found to be involved in the translation of plasticity-dependent mRNAs and has been associated to Long Term Memory (LTM). Orb2 protein presents two main isoforms, Orb2A and Orb2B, which form an activity induced amyloid-like functional aggregate, which is thought to be the translation-inducing state of the RBP. Here we present a two-states continuous differential model for Orb2A-Orb2B aggregation and we propose it, more generally, as a new synaptic facilitation rule for learning processes involving protein aggregation-dependent plasticity (PADP).

Neuroscience↗

bioRxiv2015

Automatic segmentation of Drosophila neural compartments using GAL4 expression data reveals novel visual pathways

We made use of two recent, large-scale Drosophila GAL4 libraries and associated confocal imaging datasets to automatically segment large brain regions into smaller putative functional units such as neuropils and fiber tracts. The method we developed is based on the hypothesis that molecular identity can be used to assign individual voxels to biologically meaningful regions. Our results (available at https://strawlab.org/braincode) are consistent with this hypothesis because regions with well-known anatomy, namely the antennal lobes and central complex, were automatically segmented into familiar compartments. We then applied the algorithm to the central brain regions receiving input from the optic lobes. Based on the automated segmentation and manual validation, we can identify and provide promising driver lines for 10 previously identified and 14 novel types of visual projection neurons and their associated optic glomeruli. The same strategy can be used in other brain regions and likely other species, including vertebrates.

Neuroscience↗

bioRxiv2015

Mind the drift - improving sensitivity to fMRI pattern information by accounting for temporal pattern drift

Analyzing functional magnetic resonance imaging (fMRI) pattern similarity is becoming increasingly popular because it allows one to relate distributed patterns of voxel activity to continuous perceptual and cognitive states of the human brain. Here we show that fMRI pattern similarity estimates are severely affected by temporal pattern drifts in fMRI data - even after voxel-wise detrending. For this particular dataset, the drift effect obscures orientation information as measured by fMRI pattern dissimilarities. We demonstrate that orientation information can be recovered using three different methods: 1. Regressing out the drift component through linear modeling; 2. Computing representational distances between conditions measured in independent imaging runs; 3. Crossvalidation of pattern distance estimates. One possible source of temporal pattern drift could be random walk like fluctuations -- physiological or scanner related -- occurring within single voxel timecourses. This explanation is consistent with voxel-wise detrending not alleviating pattern drift effects. In addition, this would explain why crossvalidated pattern distances are robust to temporal drift because a random walk process is expected to give rise to non-replicable drift directions. Given these findings, we recommend that future fMRI studies take pattern drift into account when analyzing pattern similarity as this can greatly enhance the sensitivity to experimental effects of interest.

Neuroscience↗

bioRxiv2015

Consistent estimation of complete neuronal connectivity in large neuronal populations using sparse "shotgun" neuronal activity sampling

We investigate the properties of the recently proposed \"shotgun\" sampling approach for the common inputs problem in the functional estimation of neuronal connectivity. We study the asymptotic correctness, the speed of convergence, and the data size requirements of such an approach. We find that the shotgun approach can be expected to allow the inference of the complete connectivity matrix in large neuronal populations under some rather general conditions. However, we find that the posterior error of the shotgun connectivity estimator may grow quickly with the size of the unobserved neuronal populations, the connectivity strength, and the square of the observations sparseness. This implies that the shotgun connectivity estimation will require significant amounts of neuronal activity data whenever the number of neurons in the observed populations is small. We present a numerical approach for solving the shotgun estimation problem in general settings and use it to demonstrate the shotgun connectivity inference in simulated synfire and weakly coupled cortical neuronal networks.

Neuroscience↗

bioRxiv2015

Timing of locomotor recovery modulated by the white gene in Drosophila

Locomotor recovery from anoxia follows the restoration of disordered ion distributions and neuronal excitability. The time taken for locomotor recovery after 30 s anoxia (around 10 min) is longer than the time for the propagation of action potentials to be restored (less than 1 min) in Drosophila wild-type. We report here that the white (w) gene modulates the timing of locomotor recovery. Wild-type flies displayed fast and consistent recovery of locomotion from anoxia, whereas mutants of w showed significantly delayed recovery. Genetic analysis including serial backcrossing revealed a strong association between the w locus and the timing of locomotor recovery, and haplo-insufficient function of w+ in promoting fast locomotor recovery. The locomotor recovery phenotype was independent of classic eye pigmentation, although both are associated with the w gene. Introducing up to four copies of mini-white (mw+) into w1118 was insufficient to promote fast and consistent locomotor recovery. However, flies carrying w+ duplicated to Y chromosome showed wild-type-like fast locomotor recovery. Furthermore, RNAi knockdown of w in neurons but not glia delayed locomotor recovery, and specifically, knockdown of w in putative subsets of serotonin neurons was sufficient to delay the locomotor recovery. These data reveal an additional role for w in modulating the timing of locomotor recovery from anoxia.

Neuroscience↗

bioRxiv2015

The Ionotropic Receptors IR21a and IR25a mediate cool sensing in Drosophila

Animals rely on highly sensitive thermoreceptors to seek out optimal temperatures, but the molecular mechanisms of thermosensing are not well understood. The Dorsal Organ Cool Cells (DOCCs) of the Drosophila larva are a set of exceptionally thermosensitive neurons critical for larval cool avoidance. Here we show that DOCC cool-sensing is mediated by Ionotropic Receptors (IRs), a family of sensory receptors widely studied in invertebrate chemical sensing. We find that two IRs, IR21a and IR25a, are required to mediate DOCC responses to cooling and are required for cool avoidance behavior. Furthermore, we find that ectopic expression of IR21a can confer cool-responsiveness in an Ir25a-dependent manner, suggesting an instructive role for IR21a in thermosensing. Together, these data show that IR family receptors can function together to mediate thermosensation of exquisite sensitivity.

Neuroscience↗

bioRxiv2015

Elevated gene expression of most microglial markers, and reduced expression of most pyramidal neuron and interneuron markers, in postmortem autism cortex

Autism Spectrum Disorders (ASD) are clinically and genetically heterogeneous. Nevertheless, a characteristic gene expression signature in postmortem cortex is shared across most ASD cases. Knowing whether this signature reflects changes in cells of particular types would help to determine the molecular, cellular, and anatomical etiology of ASD. To investigate we took advantage of cell type marker genes defined by recent single cell transcriptome sequencing from mouse or human cortex. We find that microglial markers showed significantly and substantially higher median expression in postmortem ASD than in control cortex for both mouse and human markers (21% higher for orthologous markers defined in mouse; 93% higher for human markers). In contrast, neuronal markers showed reduced median expression: 12% and 16% lower for mouse interneurons and pyramidal neurons, 40% lower for human neurons, and 36% lower for a class of human excitatory projection neurons. Cell type density alterations in ASD brains are indicated because distributions of these cell type markers are shifted concordantly. Importantly, orthologous mouse neuronal marker genes encoding proteins localized to diverse neuronal compartments all showed reduced median expression in ASD. Our results provide a framework for revealing the basis of transcriptomic differences in ASD. We propose comparing cell type density alterations in post-mortem tissue with and without these distinctive gene expression changes to reconcile results from stereological and transcriptomic studies.

Neuroscience↗

bioRxiv2015

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative explanation that captures the complexity of scene recognition, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and a novel quantitative model of how spatial layout representations may emerge in the human brain.

Neuroscience↗