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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 145 records · Page 8Linked to original sources

bioRxiv2016

Visual field map clusters in human frontoparietal cortex

The visual neurosciences have made enormous progress in recent decades, in part because of the ability to drive visual areas by their sensory inputs, allowing researchers to reliably define visual areas across individuals and across species. Similar strategies for parceling higher-order cortex have proven elusive. Here, using a novel experimental task and nonlinear population receptive field modeling we map and characterize the topographic organization of several regions in human frontoparietal cortex. We discover maps of both polar angle and eccentricity that are organized into clusters, similar to visual cortex, where multiple maps of polar angle of the contralateral visual field share a confluent fovea. This is striking because neural activity in frontoparietal cortex is believed to reflect higher-order cognitive functions rather than external sensory processing. Perhaps the spatial topography in frontoparietal cortex parallels the retinotopic organization of sensory cortex to enable an efficient interface between perception and higher-order cognitive processes. Critically, these visual maps constitute well-defined anatomical units that future study of frontoparietal cortex can reliably target.

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bioRxiv2016

The successor representation in human reinforcement learning

Theories of reward learning in neuroscience have focused on two families of algorithms, thought to capture deliberative vs. habitual choice. \"Model-based\" algorithms compute the value of candidate actions from scratch, whereas \"model-free\" algorithms make choice more efficient but less flexible by storing pre-computed action values. We examine an intermediate algorithmic family, the successor representation (SR), which balances flexibility and efficiency by storing partially computed action values: predictions about future events. These pre-computation strategies differ in how they update their choices following changes in a task. SRs reliance on stored predictions about future states predicts a unique signature of insensitivity to changes in the tasks sequence of events, but flexible adjustment following changes to rewards. We provide evidence for such differential sensitivity in two behavioral studies with humans. These results suggest that the SR is a computational substrate for semi-flexible choice in humans, introducing a subtler, more cognitive notion of habit.

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bioRxiv2016

The HTM Spatial Pooler: a neocortical algorithm for online sparse distributed coding

1.Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the spatial pooler, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the spatial pooler outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the spatial pooler in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

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bioRxiv2016

Test-retest reliability of functional connectivity networks during naturalistic fMRI paradigms

Functional connectivity analysis has become a powerful tool for probing the human brain function and its breakdown in neuropsychiatry disorders. So far, most studies adopted resting state paradigm to examine functional connectivity networks in the brain, thanks to its low demand and high tolerance that are essential for clinical studies. However, the test-retest reliability of resting state connectivity measures is moderate, potentially due to its low behavioral constraint. On the other hand, naturalistic neuroimaging paradigms, an emerging approach for cognitive neuroscience with high ecological validity, could potentially improve the reliability of functional connectivity measures. To test this hypothesis, we characterized the test-retest reliability of functional connectivity measures during a natural viewing condition, and benchmarked it against resting state connectivity measures acquired within the same functional magnetic resonance imaging (fMRI) session. We found that the reliability of connectivity and graph theoretical measures of brain networks is significantly improved during natural viewing conditions over resting state conditions, with an average increase of almost 50% across various connectivity measures. Not only sensory networks for audio-visual processing become more reliable, higher order brain networks, such as default mode and attention networks, also appear to show higher reliability during natural viewing. Our results support the use of natural viewing paradigms in estimating functional connectivity of brain networks, and have important implications for clinical application of fMRI.

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bioRxiv2016

Network activity influences the subthreshold and spiking visual responses of pyramidal neurons in a three-layer cortex

A primary goal of systems neuroscience is to understand cortical function, which typically involves studying spontaneous and sensory-evoked cortical activity. Mounting evidence suggests a strong and complex relationship between the ongoing and evoked state. To date, most work in this area has been based on spiking in populations of neurons. While advantageous in many respects, this approach is limited in scope; it records the activities of a minority of neurons, and gives no direct indication of the underlying subthreshold dynamics. Membrane potential recordings can fill these gaps in our understanding, but are difficult to obtain in vivo. Here, we record subthreshold cortical visual responses in the ex vivo turtle eye-attached whole-brain preparation, which is ideally-suited to such a study. In the absence of visual stimulation, the network is \"synchronous\"; neurons display network-mediated transitions between low- and high-conductance membrane potential states. The prevalence of these slow-wave transitions varies across turtles and recording sessions. Visual stimulation evokes similar high-conductance states, which are on average larger and less reliable when the ongoing state is more synchronous. Responses are muted when immediately preceded by large, spontaneous high-conductance events. Evoked spiking is sparse, highly variable across trials, and mediated by concerted synaptic inputs that are in general only very weakly correlated with inputs to nearby neurons. Together, these results highlight the multiplexed influence of the cortical network on the spontaneous and sensory-evoked activity of individual cortical neurons.

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bioRxiv2016

A LOGICAL INQUIRY OF EMOTIONS AND COGNITION

Experimental procedures in neuroscience rely on the standpoints that the mind is a functional state of the brain and a clear subdivision among different mental faculties does exist in the cortex. According to cognitive neuroscientists, the term \"mind\" encompasses just the \"cognitive\" faculties, such as consciousness, perception, thinking, judgement, memory, leaving apart the \"emotional\" states. Here, taking into account the powerful tools of the first-order predicate logic, we evaluated whether: a) the mind is a function on the physical brain activity; b) different mental faculties can be reduced to a more general one; c) the division of mental faculties in cognition and emotion holds true. We demonstrated that nervous activity is equivalent to mental faculties and that emotions and cognition do not stand for two separated functions of the mind. This means that, counter to our common-sense belief, cognition and emotions are splitted and every faculty of the mind necessarily displays a counterpart in other ones. We point out how it is possible for condensed mind faculties to be unglued in order to become apparently different functions. Therefore, seemingly different mind faculties turn out to be equivalent, because the same logical framework holds for all the types of brain activities, independent of their boundaries and magnitude.

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bioRxiv2016

Activating task relevant prior knowledge increases active information storage in content specific brain areas

Predictive coding suggests that the brain infers the causes of its sensations by combining sensory evidence with internal predictions based on available prior knowledge. However, the neurophysiological correlates of (pre-)activated prior knowledge serving these predictions are still unknown. Based on the idea that such pre-activated prior knowledge must be maintained until needed we measured the amount of maintained information in neural signals via the active information storage (AIS) measure. AIS was calculated on whole-brain beamformer-reconstructed source time-courses from magnetoencephalography (MEG) recordings of 52 human subjects during the baseline of a Mooney face/house detection task. Pre-activation of prior knowledge for faces showed as alpha- and beta-band related AIS increases in content specific areas; these AIS increases were behaviourally relevant in brain area FFA. Further, AIS allowed decoding of the cued category on a trial-by-trial basis. Moreover, top-down transfer of predictions estimated by transfer entropy was associated with beta frequencies. Our results support accounts that activated prior knowledge and the corresponding predictions are signalled in low-frequency activity (<30 Hz).\n\nSignificance statementOur perception is not only determined by the information our eyes/retina and other sensory organs receive from the outside world, but strongly depends also on information already present in our brains like prior knowledge about specific situations or objects. A currently popular theory in neuroscience, predictive coding theory, suggests that this prior knowledge is used by the brain to form internal predictions about upcoming sensory information. However, neurophysiological evidence for this hypothesis is rare - mostly because this kind of evidence requires making strong a-priori assumptions about the specific predictions the brain makes and the brain areas involved. Using a novel, assumption-free approach we find that face-related prior knowledge and the derived predictions are represented and transferred in low-frequency brain activity.

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bioRxiv2016

Decoding the nonconscious dynamics of thought generation

Much of economics, psychology and neuroscience have focused on thought dynamics and how they control our behavior, from individual moral choices to the irrationality of market dynamics. However, how much of our thoughts we actually control when we feel we make deliberate choices remains unknown. Here we show that the content of thoughts can be decoded from activity patterns as early as 11 seconds before individuals report having formed the volitional thought. Participants freely chose which of two differently oriented and colored gratings to think about. Using functional magnetic resonance imaging (fMRI) and pattern classification methods, we consistently classified the contents of thoughts using activity patterns recorded before and after the thought was reported. We found that activity patterns were predictive as far as 11 seconds before the conscious thought, in visual, frontal and subcortical areas. These predictive patterns contained similar information to the responses evoked by unattended perceptual gratings and were evident in individual visual areas. Interestingly, neural information present before the decision was associated with the vividness of future thoughts, suggesting that preceding nonconscious sensory-like representations can impact the content and strength of future conscious thoughts. Our results suggest that thoughts and their strength can be biased by prior spontaneous nonconscious perception-like representations, advancing theories of free will and models of intrusive and repetitive thought production.

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bioRxiv2016

Multi-scale approaches for high-speed imaging and analysis of large neural populations

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution.\n\nHere we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover neural activity from data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to \"zoom out\" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.\n\nAuthor SummaryThe voxel rate of imaging systems ultimately sets the limit on the speed of data acquisition. These limits often mean that only a small fraction of the activity of large neuronal populations can be observed at high spatio-temporal resolution. For imaging of very large populations with single cell resolution, temporal resolution is typically sacrificed. Here we propose a multi-scale approach to achieve single cell precision using fast imaging at reduced spatial resolution. In the first phase the spatial location and shape of each neuron is obtained at standard spatial resolution; in the second phase imaging is performed at much lower spatial resolution. We show that we can apply a demixing algorithm to accurately recover each neurons activity from the low-resolution data by exploiting the high-resolution cellular maps estimated in the first imaging phase. Thus by decreasing the spatial resolution in the second phase, we can compress the video data significantly, and potentially acquire images over an order-of-magnitude larger area, or image at significantly higher temporal resolution, with minimal loss in accuracy of the recovered neuronal activity. We evaluate this approach on real data from light-sheet and 2-photon calcium imaging.

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bioRxiv2016

A Resource for Assessing Information Processing in the Developing Brain Using EEG and Eye Tracking

We present a dataset combining electrophysiology and eye tracking intended as a resource for the investigation of information processing in the developing brain. The dataset includes high-density task-based and task-free EEG, eye tracking, and cognitive and behavioral data collected from 126 individuals (ages: 6-44). The task battery spans both the simple/complex and passive/active dimensions to cover a range of approaches prevalent in modern cognitive neuroscience. The active task paradigms facilitate principled deconstruction of core components of task performance in the developing brain, whereas the passive paradigms permit the examination of intrinsic functional network activity during varying amounts of external stimulation. Alongside these neurophysiological data, we include an abbreviated cognitive test battery and questionnaire-based measures of psychiatric functioning. We hope that this dataset will lead to the development of novel assays of neural processes fundamental to information processing, which can be used to index healthy brain development as well as detect pathologic processes.

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bioRxiv2016

Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images

A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noises in the extracted features and increasing the performance of prediction. In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multi-class prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Experimental studies on 4 visual categories (words, consonants, objects and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.

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bioRxiv2016

The Fruit Fly Brain Observatory: from structure to function

The Fruit Fly Brain Observatory (FFBO) is a collaborative effort between experimentalists, theorists and computational neuroscientists at Columbia University, National Tsing Hua University and Sheffield University with the goal to (i) create an open platform for the emulation and biological validation of fruit fly brain models in health and disease, (ii) standardize tools and methods for graphical rendering, representation and manipulation of brain circuits, (iii) standardize tools for representation of fruit fly brain data and its abstractions and support for natural language queries, (iv) create a focus for the neuroscience community with interests in the fruit fly brain and encourage the sharing of fruit fly brain structural data and executable code worldwide. NeuroNLP and NeuroGFX, two key FFBO applications, aim to address two major challenges, respectively: i) seamlessly integrate structural and genetic data from multiple sources that can be intuitively queried, effectively visualized and extensively manipulated, ii) devise executable brain circuit models anchored in structural data for understanding and developing novel hypotheses about brain function. NeuroNLP enables researchers to use plain English (or other languages) to probe biological data that are integrated into a novel database system, called NeuroArch, that we developed for integrating biological and abstract data models of the fruit fly brain. With powerful 3D graphical visualization, NeuroNLP presents a highly accessible portal for the fruit fly brain data. NeuroGFX provides users highly intuitive tools to execute neural circuit models with Neurokernel, an open-source platform for emulating the fruit fly brain, with full data support from the NeuroArch database and visualization support from an interactive graphical interface. Brain circuits can be configured with high flexibility and investigated on multiple levels, e.g., whole brain, neuropil, and local circuit levels. The FFBO is publicly available and accessible at http://fruitflybrain.org from any modern web browsers, including those running on smartphones.

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bioRxiv2016

Searching for Principles of Brain Computation

Highlights O_LIHints for computational principles from experimental data\nC_LIO_LIComputational role of diverse network components\nC_LIO_LIEmergence and computational role of assemblies\nC_LIO_LIProbabilistic inference through stochastic network dynamics\nC_LIO_LIOngoing network rewiring and compensation through synaptic sampling\nC_LI\n\nAbstractExperimental methods in neuroscience, such as calcium-imaging and recordings with multielectrode arrays, are advancing at a rapid pace. They produce insight into the simultaneous activity of large numbers of neurons, and into plasticity processes in the brains of awake and behaving animals. These new data constrain models for neural computation and network plasticity that underlie perception, cognition, behavior, and learning. I will discuss in this short article four such constraints: Inherent recurrent network activity and heterogeneous dynamic properties of neurons and synapses, stereotypical spatio-temporal activity patterns in networks of neurons, high trial-to-trial variability of network responses, and functional stability in spite of permanently ongoing changes in the network. I am proposing that these constraints provide hints to underlying principles of brain computation and learning.

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bioRxiv2016

Dynamic flexibility in striatal-cortical circuits supports reinforcement learning

Complex learned behaviors must involve the integrated action of distributed brain circuits. While the contributions of individual regions to learning have been extensively investigated, understanding how distributed brain networks orchestrate their activity over the course of learning remains elusive. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain time-resolved descriptions of network coordination during reinforcement learning. We found that learning to associate visual cues with reward involves dynamic changes in network coupling between the striatum and distributed brain regions, including visual, orbitofrontal, and ventromedial prefrontal cortex. Moreover, we found that flexibility in striatal network dynamics correlates with participants learning rate and inverse temperature, two parameters derived from reinforcement learning models. Finally, we found that not all forms of learning relate to this circuit: episodic memory, measured in the same participants at the same time, was related to dynamic connectivity in distinct brain networks. These results suggest that dynamic changes in striatal-centered networks provide a mechanism for information integration during reinforcement learning.\n\nSignificance StatementLearning from the outcomes of actions-referred to as reinforcement learning-is an essential part of life. The roles of individual brain regions in reinforcement learning have been well characterized in terms of the updating of values for actions or sensory stimuli. Missing from this account, however, is a description of the manner in which different brain areas interact during learning to integrate sensory and value information. Here we characterize flexible striatal-cortical network dynamics that relate to reinforcement learning behavior.

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bioRxiv2016

When null hypothesis significance testing is unsuitable for research: a reassessment

Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of psychology, cognitive neuroscience and biomedical science in general. We review these shortcomings and suggest that, after about 60 years of negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. Different inferential methods (NHST, likelihood estimation, Bayesian methods, false-discovery rate control) may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Studies should optimally be pre-registered and raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out. Instead, we should encourage either more in-depth statistical training of more researchers and/or more widespread involvement of professional statisticians in all research.

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bioRxiv2016

Identifying Model-Based and Model-Free Patterns in Behavior on Multi-Step Tasks

Recent years have seen a surge of research into the neuroscience of planning. Much of this work has taken advantage of a two-step sequential decision task developed by Daw et al. (2011), which gives the ability to diagnose whether or not subjects behavior is the result of planning. Here, we present simulations which suggest that the techniques most commonly used to analyze data from this task may be confounded in important ways. We introduce a new analysis technique, which suffers from fewer of these issues. This technique also presents a richer view of behavior, making it useful for characterizing patterns in behavior in a theory-neutral manner. This allows it to provide an important check on the assumptions of more theory-driven analysis such as agent-based model-fitting.

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bioRxiv2016

From static to temporal network theory - applications to functional brain connectivity

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, there has been a growing interest to examine the temporal dynamics of the brain's network activity. While different approaches to capture fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. Temporal network theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences and engineering. The objective of this paper is twofold: (i) to present a detailed description of the central tenets and outline measures from temporal network theory; (ii) apply these measures to a resting-state fMRI dataset to illustrate their utility. Further, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this paper are freely available as a python package Teneto.

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bioRxiv2016

Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis

Simultaneous recordings from N electrodes generate N-dimensional time series that call for efficient representations to expose relevant aspects of the underlying dynamics.\n\nBinning the time series defines a sequence of neural activity vectors that populate the N-dimensional space as a density distribution, especially informative when the neural dynamics proceeds as a noisy path through metastable states (often a case of interest in neuroscience); this makes clustering in the N-dimensional space a natural choice.\n\nWe apply a variant of the mean-shift algorithm to perform such clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memory attractors.\n\nThe neural states identified as clusters centroids are then used to define a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities.\n\nWe next consider the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects; we develop a procedure, inspired by Boltzmann learning but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations.\n\nAfter clustering the activity generated by such multi-modular spiking networks, we cast their multi-dimensional dynamics in the form of the symbolic sequence of the clusters centroids; this representation naturally lends itself to complexity estimates that provide compact information on memory effects like those induced by spike-frequency adaptation. Specifically, to obtain a relative complexity measure we compare the Lempel-Ziv complexity of the actual centroid sequence to the one of Markov processes sharing the same transition probabilities between centroids; as an illustration, we show that the dependence of such relative complexity on the characteristic time scale of spike-frequency adaptation.

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