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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 523 records · Page 29Linked to original sources

bioRxiv2015

Self-organization of Computation in Neural Systems

When learning a complex task our nervous system self-organizes large groups of neurons into coherent dynamic activity patterns. During this, a cell assembly network with multiple, simultaneously active, and computationally powerful assemblies is formed; a process which is so far not understood. Here we show that the com- bination of synaptic plasticity with the slower process of synaptic scaling achieves formation of such assembly networks. This type of self-organization allows executing a difficult, six degrees of freedom, manipulation task with a robot where assemblies need to learn computing complex non-linear transforms and - for execution - must cooperate with each other without interference. This mechanism, thus, permits for the first time the guided self-organization of computationally powerful sub-structures in dynamic networks for behavior control.

Neuroscience↗

bioRxiv2015

A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex

We developed a large-scale dynamical model of the macaque neocortex based on recent quantitative connectivity data. A hierarchy of timescales naturally emerges from this system: sensory areas show brief, transient responses to input (appropriate for sensory processing), whereas association areas integrate inputs over time and exhibit persistent activity (suitable for decision-making and working memory). The model displays multiple temporal hierarchies, as evidenced by contrasting responses to visual and somatosensory stimulation. Moreover, slower prefrontal and temporal areas have a disproportionate impact on global brain dynamics. These findings establish for the first time a circuit mechanism for \"temporal receptive windows\" that are progressively enlarged along the cortical hierarchy, extend the concept of working memory from local to large circuits, and suggest a re-interpretation of functional connectivity measures.\n\nConflict of InterestThe authors declare no competing financial interests.\n\nAcknowledgementsWe thank Nikola Markov and John Murray for discussions. This work was supported by ONR grant N00014-13-1-0297 and NIH grant R01MH062349 (X.-J.W.) and by ANR-11-BSV4-501 and LABEX CORTEX (ANR-11-LABX-0042) of Universite de Lyon, program \"Investissements dAvenir\" (ANR-11-IDEX-0007) operated by the French National Research Agency (H.K.). The connectivity data is available at www.core-nets.org.

Neuroscience↗

bioRxiv2015

Repellent compound with larger protective zone than DEET identified through activity-screening of Ir40a neurons, does not require Or function

The widely used insect repellent DEET has a limited spatial zone of protection, requiring it to be applied over all exposed areas of skin. Identification of insect DEET-sensing neurons expressing a highly conserved Ionotropic receptor, Ir40a, provides an opportunity to identify new structural classes of volatile agonists as potential spatial repellents. By imaging the activity of the Ir40a+ neurons in D. melanogaster expressing the calcium sensitive GCaMP3 protein, we identify a strong agonist, 4-methylpiperidine, with a much higher vapor pressure than DEET. Behavioral testing reveals that 4-methylpiperidine repels Aedes aegypti, which is consistent with our model that Ir40a marks a conserved innate aversive pathway. Using a spatial repellency assay we demonstrate that 4-methylpiperidine applied to one part of the hand repels mosquitoes on another part effectively, whereas DEET cannot do so. Using orco mutant A. aegypti we demonstrate that avoidance to 4- methypiperidine is not dependent on Or family function. Additional testing of orco mutant mosquitoes demonstrates that they are also effectively repelled by DEET, without coming in contact with it, in heat attraction assays. Together, these results support our initial observations that the conserved Ir pathway plays a key role in olfactory repellency and can be used to identify new classes of repellents.

Neuroscience↗

bioRxiv2015

Visual cues determine hippocampal directional selectivity

Both spatial and directional information are necessary for navigation. Rodent hippocampal neurons show spatial selectivity in all environments1, but directional tuning only on linear paths2-8.The sensory mechanisms underlying directionality are unknown, though vestibular and visual cues are thought to be crucial. However, hippocampal neurons are thought to show no angular modulation during two-dimensional random foraging despite the presence of vestibular and visual cues6,7. Additionally, specific aspects of visual cues have not been directly linked to hippocampal responses in rodents. To resolve these issues we manipulated vestibular and visual cues in a series of experiments. We first measured hippocampal activity during random foraging in real world (RW) where we found that neurons firing exhibited significant modulation by head-direction. In fact, the fraction of modulated neurons was comparable to that in the head-direction system9. These findings are contrary to commonly held beliefs about hippocampal directionality6,7. To isolate the contribution of visual cues we measured neural responses in a visually similar virtual reality (VR) where the range of vestibular inputs is minimized5,10,11. Significant directional modulation was not only found in VR, but it was comparable to that in RW. Several additional experiments revealed that changes in the angular information contained in the visual cues induced corresponding changes in hippocampal head-directional modulation. Remarkably, for head-directionally modulated neurons, the ensemble activity was biased towards the sole visual cue. These results demonstrate that robust vestibular cues are not required for hippocampal directional selectivity, while visual cues are not only sufficient but also play a causal role in driving hippocampal responses.

Neuroscience↗

bioRxiv2015

Non-dichotomous inference using bootstrapped evidence

The problems with classical frequentist statistics have recently received much attention, yet the enthusiasm of researchers to adopt alternatives like Bayesian inference remains modest. Here I present the bootstrapped evidence test, an objective resampling procedure that takes the precision with which both the experimental and null hypothesis can be estimated into account. Simulations and reanalysis of actual experimental data demonstrate that this test minimizes false positives while maintaining sensitivity. It is equally applicable to a wide range of situations and thus minimizes problems arising from analytical flexibility. Critically, it does not dichotomize the results based on an arbitrary significance level but instead quantifies how well the data support either the alternative or the null hypothesis. It is thus particularly useful in situations with considerable uncertainty about the expected effect size. Because it is non-parametric, it is also robust to severe violations of assumptions made by classical statistics.

Neuroscience↗

bioRxiv2015

Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing

Functional magnetic resonance imaging (fMRI) research is routinely criticized for being statistically underpowered due to characteristically small sample sizes and much larger sample sizes are being increasingly recommended. Additionally, various sources of artifact inherent in fMRI data can have detrimental impact on effect size estimates and statistical power. Here we show how specific removal of non-BOLD artifacts can improve effect size estimation and statistical power in task-fMRI contexts, with particular application to the social-cognitive domain of mentalizing/theory of mind. Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA). Group-level effect size estimates on two different mentalizing tasks were enhanced by ME-ICA at a median rate of 24% in regions canonically associated with mentalizing, while much more substantial boosts (40-149%) were observed in non-canonical cerebellar areas. This effect size boosting is primarily a consequence of reduction of non-BOLD noise at the subject-level, which then translates into consequent reductions in between-subject variance at the group-level. Power simulations demonstrate that enhanced effect size enables highly-powered studies at traditional sample sizes. Cerebellar effects observed after applying ME-ICA may be unobservable with conventional imaging at traditional sample sizes. Thus, ME-ICA allows for principled design-agnostic non-BOLD artifact removal that can substantially improve effect size estimates and statistical power in task-fMRI contexts. ME-ICA could help issues regarding statistical power and non-BOLD noise and enable potential for novel discovery of aspects of brain organization that are currently under-appreciated and not well understood.

Neuroscience↗

bioRxiv2015

A mathematical framework for statistical decision confidence

Decision confidence is a forecast about the probability that a decision will be correct. Confidence can be framed as an objective mathematical quantity the Bayesian posterior probability, providing a formal definition of statistical decision confidence. Here we use this definition as a starting point to develop a normative statistical framework for decision confidence. We analytically prove interrelations between statistical decision confidence and other observable decision measures. Among these is a counterintuitive property of confidence that the lowest average confidence occurs when classifiers err in the presence of the strongest evidence. These results lay the foundations for a mathematically rigorous treatment of decision confidence that can lead to a common framework for understanding confidence across different research domains, from human behavior to neural representations.

Neuroscience↗

bioRxiv2015

Crossvalidation in brain imaging analysis

Crossvalidation is a method for estimating predictive performance and adjudicating between multiple models. On each of k folds of the process, k-1 of k independent subsets of the data (training set) are used to fit the parameters of each model and the left-out subset (test set) is used to estimate predictive performance. The method is statistically efficient, because training data are reused for testing and performance estimates combined across folds. The method requires no assumptions, provides nearly unbiased (slightly conservative) estimates of predictive performance, and is generally applicable because it amounts to a direct empirical test of each model.

Neuroscience↗

bioRxiv2015

Echo state networks with multiple read-out modules

We propose a new readout architecture for echo state networks where multiple linear readout modules are activated at distinct time points to varying degrees by a separate controller module. The controller module, like the reservoir of the echo state network, can be initialized randomly. All linear readout modules are trained through simple linear regression, which is the only adaptive step in the modified algorithm. The resulting architecture provides modest improvements on a variety of time series processing tasks (between 5 to 50% in performance metric depending on the task studied). The novel architecture is guaranteed to perform at least as accurately as a conventional linear readout. It can be utilized as a general purpose readout method when augmentations to performance relative to the standard method is needed.\n\nPACS numbers: 05.45.Tp, 07.05.Mh

Neuroscience↗

bioRxiv2015

In vivo characterization of the connectivity and subcomponents of the human globus pallidus

Projections from the substantia nigra and striatum traverse through the pallidum on the way to their targets. To date, in vivo characterization of these pathways remains elusive. Here we used high angular resolution diffusion imaging (N=138) to study the characteristics and structural subcompartments of the human pallidum. Our results show that the diffusion orientation distribution at the pallidum is asymmetrically oriented in a dorsolateral direction, consistent with the orientation of underlying fiber systems. Furthermore, compared to the outer pallidal segment, the internal segment has more peaks in the orientation distribution function and stronger anisotropy in the primary fiber direction, consistent with known cellular differences between the underlying nuclei. These differences in orientation, complexity, and degree of anisotropy are sufficiently robust to automatically segment the pallidal nuclei using diffusion properties. Thus the gray matter diffusion signal can be useful as an in vivo measure of the collective nigrostriatal and striatonigral pathways.

Neuroscience↗

bioRxiv2015

Development of social behaviour in young zebrafish.

Adult zebrafish are robustly social animals whereas larva is not. We designed an assay to determine at what stage of development zebrafish begin to interact with and prefer other fish. One week old zebrafish do not show significant social preference whereas most 3 weeks old zebrafish strongly prefer to remain in a compartment where they can view conspecifics. However, for some individuals, the presence of conspecifics drives avoidance instead of attraction. Social preference is dependent on vision and requires viewing fish of a similar age/size. In addition, over the same 1-3 weeks period larval zebrafish increasingly tend to coordinate their movements, a simple form of social interaction. Finally, social preference and coupled interactions are differentially modified by an NMDAR antagonist and acute exposure to ethanol, both of which are known to alter social behavior in adult zebrafish.

Neuroscience↗

bioRxiv2015

On the structure of population activity under fluctuations in attentional state

AbstractAttention is commonly thought to improve behavioral performance by increasing response gain and suppressing shared variability in neuronal populations. However, both the focus and the strength of attention are likely to vary from one experimental trial to the next, thereby inducing response variability unknown to the experimenter. Here we study analytically how fluctuations in attentional state affect the structure of population responses in a simple model of spatial and feature attention. In our model, attention acts on the neural response exclusively by modulating each neurons gain. Neurons are conditionally independent given the stimulus and the attentional gain, and correlated activity arises only from trial-to-trial fluctuations of the attentional state, which are unknown to the experimenter. We find that this simple model can readily explain many aspects of neural response modulation under attention, such as increased response gain, reduced individual and shared variability, increased correlations with firing rates, limited range correlations, and differential correlations. We therefore suggest that attention may act primarily by increasing response gain of individual neurons without affecting their correlation structure. The experimentally observed reduction in correlations may instead result from reduced variability of the attentional gain when a stimulus is attended. Moreover, we show that attentional gain fluctuations - even if unknown to a downstream readout - do not impair the readout accuracy despite inducing limited-range correlations.

Neuroscience↗

bioRxiv2015

Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics

Neural circuits for behavior transform sensory inputs into motor outputs in patterns with strategic value. Determining how neurons along a sensorimotor circuit contribute to this transformation is central to understanding behavior. To do this, a quantitative framework to describe behavioral dynamics is needed. Here, we built a high-throughput optogenetic system for Drosophila larva to quantify the sensorimotor transformations underlying navigational behavior. We express CsChrimson, a red-shifted variant of Channelrhodopsin, in specific chemosensory neurons, and expose large numbers of freely moving animals to random optogenetic activation patterns. We quantify their behavioral responses and use reverse correlation analysis to uncover the linear and static nonlinear components of navigation dynamics as functions of optogenetic activation patterns of specific sensory neurons. We find that linear-nonlinear (LN) models accurately predict navigational decision-making for different optogenetic activation waveforms. We use our method to establish the valence and dynamics of navigation driven by optogenetic activation of different combinations of bitter sensing gustatory neurons. Our method captures the dynamics of optogenetically-induced behavior in compact, quantitative transformations that can be used to characterize circuits for sensorimotor processing and their contribution to navigational decision-making.

Neuroscience↗

bioRxiv2015

Stretch Induced Hyperexcitability of Mice Callosal Pathway

Memory and learning are thought to result from changes in synaptic strength. Previous studies on synaptic physiology in brain slices have traditionally been focused on biochemical processes. Here, we demonstrate with experiments on mouse brain slices that central nervous system plasticity is also sensitive to mechanical stretch. This is important, given the host of clinical conditions involving changes in mechanical tension on the brain, and the normal role that mechanical tension plays in brain development. A novel platform is developed to investigate neural responses to mechanical stretching. Flavoprotein autofluoresence (FA) imaging was employed for measuring neural activity. We observed that synaptic excitability substantially increases after a small (2.5%) stretch was held for 10 minutes and released. The increase is accumulative, i.e. multiple stretch cycles further increase the excitability. We also developed analytical tools to quantify the spatial spread and response strength. Results show that the spatial spread is less stable in slices undergoing the stretch-unstretch cycle. FA amplitude and activation rate decrease as excitability increases in stretch cases but not in electrically enhanced cases. These results collectively demonstrate that a small stretch in physiological range can modulate neural activities significantly, suggesting that mechanical events can be employed as a novel tool for the modulation of neural plasticity.

Neuroscience↗

bioRxiv2015

Cortical microcircuit determination through global perturbation and sparse sampling in grid cells

Under modern interrogation, famously well-studied neural circuits such as that for orientation tuning in V1 are steadily giving up their secrets, but quite basic questions about connectivity and dynamics, including whether most computation is done by lateral processing or by selective feedforward summation, remain unresolved. We show here that grid cells offer a particularly rich opportunity for dissecting the mechanistic underpinnings of a cortical circuit, through a strategy based on global circuit perturbation combined with sparse neural recordings. The strategy is based on the theoretical insight that small perturbations of circuit activity will result in characteristic quantal shifts in the spatial tuning relationships between grid cells, which should be observable from multisingle unit recordings of a small subsample of the population. The predicted shifts differ qualitatively across candidate recurrent network mechanisms, and also distinguish between recurrent versus feedforward mechanisms. More generally, the proposed strategy demonstrates how sparse neural recordings coupled with global perturbation in the grid cell system can reveal much more about circuit mechanism as it relates to function than can full knowledge of network activity or of the synaptic connectivity matrix.

Neuroscience↗

bioRxiv2015

Stochastic models of evidence accumulation in changing environments

Organisms and ecological groups accumulate evidence to make decisions. Classic experiments and theoretical studies have explored this process when the correct choice is fixed during each trial. However, the natural world constantly changes. Using sequential analysis we derive a tractable model of evidence accumulation when the correct option changes in time. Our analysis shows that ideal observers discount prior evidence at a rate determined by the volatility of the environment, and that the dynamics of evidence accumulation is governed by the information gained over an average environmental epoch. A plausible neural implementation of an optimal observer in a changing environment shows that, in contrast to previous models, neural populations representing alternate choices are coupled through excitation.

Neuroscience↗

bioRxiv2015

Attention selectively gates afferent signal transmission to area V4

Selective attention causes visual cortical neurons to act as if only one of multiple stimuli are within their receptive fields. This suggests that attention employs a, yet unknown, neuronal gating mechanism for transmitting only the information that is relevant for the current behavioral context. We introduce an experimental paradigm to causally investigate this putative gating and the mechanism underlying selective attention by determining the signal availability of two time-varying stimuli in local field potentials of V4 neurons. We find transmission of the low frequency (<20Hz) components only from the attended visual input signal and that the higher frequencies from both stimuli are attenuated. A minimal model implementing routing by synchrony replicates the attentional gating effect and explains the spectral transfer characteristics of the signals. It supports the proposal that selective gamma-band synchrony subserves signal routing in cortex and further substantiates our experimental finding that attention selectively gates signals already at the level of afferent synaptic input.

Neuroscience↗

bioRxiv2015

Consistent inter-protocol differences in resting-state functional connectomes between normal aging and mild cognitive impairment

Resting-state functional connectivity is a promising biomarker for Alzheimers disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimers disease and mild cognitive impairment (MCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from MCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore pooled four independent datasets, including 112 healthy controls and 143 patients with MCI, systematically testing multiple brain connections for consistent differences. The largest effects associated with MCI involved the ventromedial and dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with MCI exhibited significantly decreased connectivity within the frontal lobe, between frontal and temporal areas, and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified robust MCI-related connectivity changes that appear to be promising candidate biomarkers for Alzheimers disease.

Neuroscience↗