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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 631 records · Page 35Linked to original sources

bioRxiv2015

Learning in cortical networks through error back-propagation

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error back-propagation algorithm. However, in the back-propagation algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of pre-synaptic and post-synaptic neurons. Several models have been proposed that approximate the back-propagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the back-propagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.

Neuroscience↗

bioRxiv2016

Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability

Resting state fMRI (R-fMRI) is a powerful in-vivo tool for examining the functional architecture of the human brain. Recent studies have demonstrated the ability to characterize transitions between functionally distinct cortical areas through the mapping of gradients in intrinsic functional connectivity (iFC) profiles. To date, this novel approach has primarily been applied to iFC profiles averaged across groups of individuals, or in one case, a single individual scanned multiple times. Here, we used a publically available R-fMRI dataset, in which 30 healthy participants were scanned 10 times (10 minutes per session), to investigate differences in full-brain transition profiles (i.e., gradient maps, edge maps) across individuals, and their reliability. Despite similarities, we found individual-specific variations in transition zone properties, which were repeatable over time. Reliability of individual-specific variations increased with number of time points; however, even with only 10 minutes, individuals were nearly always distinguishable from one another. Higher-order networks exhibited greater between-individual variation for transition profiles, while sensory and motor networks exhibited greater within-individual variation. The gradients defined using alternative iFC measures revealed similar but distinct gradient profiles for each. These results illustrate the utility of gradient-based iFC approaches for studying inter-individual variation in brain function.

Neuroscience↗

bioRxiv2016

Shared experience, shared memory: a common structure for brain activity during naturalistic recall

Our daily lives revolve around sharing experiences and memories with others. When different people recount the same events, how similar are their underlying neural representations? In this study, participants viewed a fifty-minute audio-visual movie, then verbally described the events while undergoing functional MRI. These descriptions were completely unguided and highly detailed, lasting for up to forty minutes. As each person spoke, event-specific spatial patterns were reinstated (movie-vs.-recall correlation) in default network, medial temporal, and high-level visual areas; moreover, individual event patterns were highly discriminable and similar between people during recollection (recall-vs.-recall similarity), suggesting the existence of spatially organized memory representations. In posterior medial cortex, medial prefrontal cortex, and angular gyrus, activity patterns during recall were more similar between people than to patterns elicited by the movie, indicating systematic reshaping of percept into memory across individuals. These results reveal striking similarity in how neural activity underlying real-life memories is organized and transformed in the brains of different people as they speak spontaneously about past events.

Neuroscience↗

bioRxiv2016

Random recurrent networks near criticality capture the broadband power distribution of human ECoG dynamics

The power spectrum of brain electric field potential recordings is dominated by an arrhythmic broadband signal but a mechanistic account of its underlying neural network dynamics is lacking. Here we show how the broadband power spectrum of field potential recordings can be explained by a simple random network of nodes near criticality. Such a recurrent network produces activity with a combination of a fast and a slow autocorrelation time constant, with the fast mode corresponding to local dynamics and the slow mode resulting from recurrent excitatory connections across the network. These modes are combined to produce a power spectrum similar to that observed in human intracranial EEG (i.e., electrocorticography, ECoG) recordings. Moreover, such a network naturally converts input correlations across nodes into temporal autocorrelation of the network activity. Consequently, increased independence between nodes results in a reduction in low-frequency power, which offers a possible explanation for observed changes in ECoG power spectra during task performance. Lastly, changes in network coupling produce changes in network activity power spectra reminiscent of those seen in human ECoG recordings across different arousal states. This model thus links macroscopic features of the empirical ECoG power spectrum to a parsimonious underlying network structure and proposes potential mechanisms for changes in ECoG power spectra observed across behavioral and arousal states. This provides a computational framework within which to generate and test hypotheses about the cellular and network mechanisms underlying whole brain electrical dynamics, their variations across behavioral states as well as abnormalities associated with brain diseases.

Neuroscience↗

bioRxiv2016

Local opposite orientation preferences in V1: fMRI sensitivity to fine-grained pattern information

The orientation of a visual grating can be decoded from human primary visual cortex (V1) using functional magnetic resonance imaging (fMRI) at conventional resolutions (2-3 mm voxel width, 3T scanner). It is unclear to what extent this information originates from different spatial scales of neuronal selectivity, ranging from orientation columns to global areal maps. According to the global-areal-map account, fMRI orientation decoding relies exclusively on fMRI voxels in V1 exhibiting a radial or vertical preference. Here we show, by contrast, that 2-mm isotropic voxels in a small patch of V1 within a quarterfield representation exhibit reliable opposite selectivities. Sets of voxels with opposite selectivities are locally intermingled and each set can support orientation decoding. This indicates that global areal maps cannot fully account for orientation information in fMRI and demonstrates that fMRI also reflects fine-grained patterns of neuronal selectivity.\n\nSignificance statementConventional (3T) functional magnetic resonance imaging (fMRI) allows one measure brain activity at a spatial resolution of 2-3 mm. Brain response patterns in the primary visual cortex (V1) measured with fMRI have been shown to contain robust information about the orientation of visual grating stimuli. However, it is unclear whether this information arises only from global-areal patterns or also from more fine-grained patterns. Here we show that opposite orientation preferences are present and replicable within small V1 patches. This finding demonstrates that fine-grained fMRI patterns contribute to the orientation information present in fMRI data.

Neuroscience↗

bioRxiv2016

Evidence for a probabilistic, brain-distributed, recursive mechanism for decision-making

Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics.\n\nAuthor SummaryDecision-making is central to cognition. Abnormally-formed decisions characterize disorders like over-eating, Parkinsons and Huntingtons diseases, OCD, addiction, and compulsive gambling. Yet, a unified account of decisionmaking has, hitherto, remained elusive. Here we show the essential composition of the brains decision mechanism by matching experimental data from monkeys making decisions, to the knowable function of a novel statistical inference algorithm. Our algorithm maps onto the large-scale architecture of decision circuits in the primate brain, replicating the monkeys choice behaviour and the dynamics of the neural activity that accompany it. Validated in this way, our algorithm establishes a basic framework for understanding the mechanistic ingredients of decisionmaking in the brain, and thereby, a basic platform for understanding how pathologies arise from abnormal function.

Neuroscience↗

bioRxiv2016

A performance-optimized model of neural responses across the ventral visual stream

Human visual object recognition is subserved by a multitude of cortical areas. To make sense of this system, one line of research focused on response properties of primary visual cortex neurons and developed theoretical models of a set of canonical computations such as convolution, thresholding, exponentiating and normalization that could be hierarchically repeated to give rise to more complex representations. Another line or research focused on response properties of high-level visual cortex and linked these to semantic categories useful for object recognition. Here, we hypothesized that the panoply of visual representations in the human ventral stream may be understood as emergent properties of a system constrained both by simple canonical computations and by top-level, object recognition functionality in a single unified framework (Yamins et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Guclu and van Gerven, 2015). We built a deep convolutional neural network model optimized for object recognition and compared representations at various model levels using representational similarity analysis to human functional imaging responses elicited from viewing hundreds of image stimuli. Neural network layers developed representations that corresponded in a hierarchical consistent fashion to visual areas from V1 to LOC. This correspondence increased with optimization of the models recognition performance. These findings support a unified view of the ventral stream in which representations from the earliest to the latest stages can be understood as being built from basic computations inspired by modeling of early visual cortex shaped by optimization for high-level object-based performance constraints.\n\nSignificance StatementPrior work has taken two complimentary approaches to understanding the cortical processes underlying our ability to visually recognize objects. One approach identified canonical computations from primary visual cortex that could be hierarchically repeated and give rise to complex representations. Another approach linked later visual area responses to semantic categories useful for object recognition. Here we combined both approaches by optimizing a deep convolution neural network based on canonical computations to preform object recognition. We found that this network developed hierarchically similar response properties to those of visual areas we measured using functional imaging. Thus, we show that object-based performance optimization results in predictive models that not only share similarity with late visual areas, but also intermediate and early visual areas.

Neuroscience↗

bioRxiv2016

Cell Assembly Dynamics of Sparsely-connected Inhibitory Networks: a Simple Model for the Collective Activity of Striatal Projection Neurons

Striatal projection neurons form a sparsely-connected inhibitory network, and this arrangement may be essential for the appropriate temporal organization of behavior. Here we show that a simplified, sparse inhibitory network of Leaky-Integrate-and-Fire neurons can reproduce some key features of striatal population activity, as observed in brain slices. In particular we develop a new metric to determine the conditions under which sparse inhibitory networks form anti-correlated cell assemblies with time-varying activity of individual cells. We find that under these conditions the network displays an input-specific sequence of cell assembly switching, that effectively discriminates similar inputs. Our results support the proposal that GABAergic connections between striatal projection neurons allow stimulus-selective, temporally-extended sequential activation of cell assemblies. Furthermore, we help to show how altered intrastriatal GABAergic signaling may produce aberrant network-level information processing in disorders such as Parkinsons and Huntingtons diseases.\n\nAuthor SummaryNeuronal networks that are loosely coupled by inhibitory connections can exhibit potentially useful properties. These include the ability to produce slowly-changing activity patterns, that could be important for organizing actions and thoughts over time. The striatum is a major brain structure that is critical for appropriately timing behavior to receive rewards. Striatal projection neurons have loose inhibitory interconnections, and here we show that even a highly simplified model of this striatal network is capable of producing slowly-changing activity sequences. We examine some key parameters important for producing these dynamics, and help explain how changes in striatal connectivity may contribute to serious human disorders including Parkinsons and Huntingtons diseases.

Neuroscience↗

bioRxiv2016

Revisiting the assessment of inter-individual differences in fMRI activations-behavior relationships.

Characterizing inter-individual differences induced by clinical and social factors constitutes one of the most promising applications of neuroimaging. Paving the way for such applications, neuroimaging studies often report between-group differences in \"activations\" or correlations between such \"activations\" and individual traits. Here we raise cautionary warnings about some of those inter-individual analytic strategies. These warnings become critical when measures of \"activations\" are unstandardized coefficients of regressions between BOLD signal and individual behavior.\n\nFirst, using simple algebraic derivations, we show how inter-individual differences results can spuriously arise from neglecting the statistical relationships which link the ranges of individual BOLD activation and of recorded behavior. We also demonstrate how apparently contradictory results and interpretations may simply arise from the interaction of this scaling issue and the pre-processing of the behavioral variables. Second, using computational simulations, we illustrate how this issue percolates the booming field of model-based fMRI. Finally, we outline a set of recommendations, which might prove useful for researcher and reviewers confronted with questions involving inter-individual differences in neuroimaging.\n\nAuthor SummaryCharacterizing inter-individual differences induced by clinical and societal factors constitutes one of the most promising applications of neuroimaging. Paving the way for such applications, an increasing fraction of neuroimaging studies reports between-group differences in \"activations\" or correlations between \"activations\" and individual traits. In this manuscript, we focus on the typical analytical strategies employed in studies investigating how differences in behavior between individuals or groups of individuals are translated to differences of activations in specific brain regions. We notably question whether they are suitable to support inferences and claims about the neural underpinnings of differential cognition. Our core results demonstrate that typical inter-individual results can spuriously arise by overlooking plausible statistical relationships that link the ranges of individual BOLD activation with the ranges of produced behavior. We argue that these results challenge current classical interpretations of inter-individual results. Highlighting the methodological and theoretical gaps regarding the analysis and interpretation of inter-individual differences is fundamental to fulfilling the promises of neuroimaging.

Neuroscience↗

bioRxiv2016

Changes in attractor dynamics predict altered perceptual decision making with dorsolateral prefrontal tDCS

The left dorsolateral prefrontal cortex (dlPFC) has been linked to the accumulation and comparison of perceptual evidence for decision making independent of sensory and response modalities. We investigated the possible neural dynamics underlying the role of dlPFC in perceptual decision making, through a combination of noninvasive neurostimulation in humans and computational modeling. First, we used an established and biophysically realistic model of a decision making network that employs competition between neural populations. Simulation of depolarizing noninvasive brain stimulation in this model decreased decision time, while hyperpolarizing stimulation increased it. This behavioral effect was caused by an increase in the rate of neural activity integration via recurrent connections, as well as changes in the susceptibility of the network to noisy background inputs which modulated population firing rate differences prior to the onset of the stimulus. These pre-stimulus differences biased the response to one or the other option, thus speeding or slowing decisions.\n\nWe then tested these model predictions in healthy participants performing a perceptual decision making task while receiving transcranial direct current stimulation (tDCS) over the left dlPFC, analogous to our simulated network stimulation. We found a striking match between model predictions and experimental results: depolarizing (inward) currents reduced and hyperpolarizing (outward) currents increased response times, but accuracy remained unaffected. Our results provide interventional evidence for the role of left dlPFC in perceptual decision making, and suggest that this region integrates and compares sensory evidence through competitive interactions between pyramidal cell populations which are selective for each response option. Mechanistically, our model suggests that stimulation of this region changes the rate at which evidence can be accumulated through recurrent activity and its susceptibility to background noise. More generally, our approach demonstrates that a linkage between computational modeling and noninvasive brain stimulation allows mechanistic accounts of brain function to be causally tested.

Neuroscience↗

bioRxiv2016

The Neuro Bureau ADHD-200 Preprocessed Repository

In 2011, the \"ADHD-200 Global Competition\" was held with the aim of identifying biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (s-MRI) data collected on 973 individuals. Statisticians and computer scientists were potentially the most qualified for the machine learning aspect of the competition, but generally lacked the specialized skills to implement the necessary steps of data preparation for rs-fMRI. Realizing this barrier to entry, the Neuro Bureau prospectively collaborated with all competitors by preprocessing the data and sharing these results at the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) (http://www.nitrc.org/frs/?group_id=383). This \"ADHD-200 Preprocessed\" release included multiple analytical pipelines to cater to different philosophies of data analysis. The processed derivatives included denoised and registered 4D fMRI volumes, regional time series extracted from brain parcellations, maps of 10 intrinsic connectivity networks, fractional amplitude of low frequency fluctuation, and regional homogeneity, along with grey matter density maps. The data was used by several teams who competed in the ADHD-200 Global Competition, including the winning entry by a group of biostaticians. To the best of our knowledge, the ADHD-200 Preprocessed release was the first large public resource of preprocessed resting-state fMRI and structural MRI data, and remains to this day the only resource featuring a battery of alternative processing paths.

Neuroscience↗

bioRxiv2016

On Binocular Vision: The Geometric Horopter and Cyclopean Eye

We study geometric properties of horopters defined by the criterion of equality of angle. Our primary goal is to derive the precise geometry for anatomically correct horopters. When eyes fixate on points along a curve in the horizontal visual plane for which the vergence remains constant, this curve is the larger arc of a circle connecting the eyes rotation centers. This isovergence circle is known as the Vieth-Muller circle. We show that, along the isovergence circular arc, there is an infinite family of horizontal horopters formed by circular arcs connecting the nodal points. These horopters intersect at the point of symmetric convergence. We prove that the family of 3D geometric horopters consists of two perpendicular components. The first component consists of the horizontal horopters parametrized by vergence, the point of the isovergence circle, and the choice of the nodal point location. The second component is formed by straight lines parametrized by vergence. Each of these straight lines is perpendicular to the visual plane and passes through the point of symmetric convergence. Finally, we evaluate the difference between the geometric horopter and the Vieth-Muller circle for typical near fixation distances and discuss its possible significance for depth discrimination and other related functions of vision that make use of disparity processing.

Neuroscience↗

bioRxiv2016

Believer-Skeptic meets Actor-Critic: Rethinking the role basal ganglia pathways in decision-making and reinforcement learning

The flexibility of behavioral control is a testament to the brains capacity for dynamically resolving uncertainty during goal-directed actions. This ability to select actions and learn from immediate feedback is driven by the dynamics of basal ganglia (BG) pathways. A growing body of empirical evidence conflicts with the traditional view that these pathways act as independent levers for facilitating (i.e., direct pathway) or suppressing (i.e., indirect pathway) motor output, suggesting instead that they engage in a dynamic competition during action decisions that computationally captures action uncertainty. Here we discuss the utility of encoding action uncertainty as a dynamic competition between opposing control pathways and provide evidence that this simple mechanism may have powerful implications for bridging neurocomputational theories of decision making and reinforcement learning.

Neuroscience↗

bioRxiv2016

Nanoscale dynamics of synaptic vesicle trafficking and fusion at the presynaptic active zone

The cytomatrix at the active zone (CAZ) is a macromolecular complex that facilitates the supply of release-ready synaptic vesicles to support neurotransmitter release at synapses. To reveal the dynamics of this supply process in living synapses, we used super-resolution imaging to track single vesicles at voltage-clamped presynaptic terminals of retinal bipolar neurons, whose CAZ contains a specialized structure--the synaptic ribbon--that supports both fast, transient and slow, sustained modes of transmission. We find that the synaptic ribbon serves a dual function as a conduit for diffusion of synaptic vesicles and a platform for vesicles to fuse distal to the plasma membrane itself, via compound fusion. The combination of these functions allows the ribbon-type CAZ to achieve the continuous transmitter release required by synapses of neurons that carry tonic, graded visual signals in the retina.

Neuroscience↗

bioRxiv2016

A neurocomputational model of human frontopolar cortex function

The frontopolar cortex (FPC), the most anterior part of the lateral prefrontal cortex corresponding to Brodmanns area 10, is involved in human high-order cognition, including reasoning, problem-solving and multitasking. Its specific contribution to prefrontal executive function, however, remains unclear. A neurocomputational model suggests that the FPC implements a basic process referred to as cognitive branching that maintains a task in a pending state during the execution of another, and enables to revert back to it upon completion of the ongoing one. However, the FPC is engaged in other cognitive functions including prospective memory, relational reasoning, episodic memory retrieval and attentional set-shifting, which are not directly linked to the notion of cognitive branching. Here we used a neurocomputional branching model to simulate the involvement of the FPC in these various cognitive functions. Simulation results indicate that the model accounts for the variety of FPC activations observed in these various experimental paradigms. Thus, the present study provides theoretical evidence suggesting that all these behavioral paradigms implicitly involve branching processes, and supports the idea that cognitive branching is the core function of the human frontopolar cortex.

Neuroscience↗

bioRxiv2016

Areas activated during naturalistic reading comprehension overlap topological visual, auditory, and somatotomotor maps

Cortical mapping techniques using fMRI have been instrumental in identifying the boundaries of topological (neighbor-preserving) maps in early sensory areas. The presence of topological maps beyond early sensory areas raises the possibility that they might play a significant role in other cognitive systems, and that topological mapping might help to delineate areas involved in higher cognitive processes. In this study, we combine surface based visual, auditory, and somatomotor mapping methods with a naturalistic reading comprehension task in the same group of subjects to provide a qualitative and quantitative assessment of the cortical overlap between sensory-motor maps in all major sensory modalities, and reading processing regions. Our results suggest that cortical activation during naturalistic reading comprehension overlaps more extensively with topological sensory-motor maps than has been heretofore appreciated. Reading activation in regions adjacent to occipital lobe and inferior parietal lobe completely overlaps visual maps, whereas most of frontal activation for reading in dorsolateral and ventral prefrontal cortex overlaps both visual and auditory maps. Even classical language regions in superior temporal cortex are partially overlapped by topological visual and auditory maps. By contrast, the main overlap with somatomotor maps is restricted to dorsolateral frontal cortex.

Neuroscience↗

bioRxiv2016

Intracranial Markers of Conscious Face Perception in Humans

The comparison between perceived and unperceived trials at perceptual threshold isolates not only the core neuronal substrate of a particular conscious perception, but also aspects of brain activity that facilitate, hinder or tend to follow conscious perception. We take a step towards the resolution of these confounds by combining an analysis of ECoG neuronal responses observed during the presentation of faces partially masked by Continuous Flash Suppression, and those responses observed during the unmasked presentation of faces and other images in the same subjects. Neuronal activity in both the fusiform gyrus and the superior temporal sulcus discriminated seen vs. unseen faces in the masked paradigm and upright faces vs. other categories in the unmasked paradigm. However, only the former discriminated upright vs. inverted faces in the unmasked paradigm. Our results suggest a prominent role for the fusiform gyrus in the configural perception of faces.

Neuroscience↗

bioRxiv2016

A Bayesian framework for comparing the structure of spontaneous correlated activity recorded under different conditions

Distant-dependent correlations in spontaneous retinal activity are thought to be instructive in the development of the retinotopic map and eye-specific segregation maps. Many studies which seek to investigate these correlations and their role in map formation record spontaneous retinal activity from different pheno-types or experimental conditions and compare the distance-dependence of the correlations between different conditions. They seek to demonstrate that these correlations differ significantly, and this analysis is often key to the studys conclusions. In this work, we assess the methods of inference which have been previously used to investigate this problem and conclude that they are inadequate. We propose a hierarchical Bayesian framework to model distant-dependent correlations in spontaneous retinal activity data and specify a method which uses the data to specify the form of the model. This model allows us to assess the evidence for/against differences in correlations between experimental conditions in a more robust and credible way. We demonstrate the use of this method by applying it to data from two studies of spontaneous retinal activity. We believe however the framework to be rather more general and that it can be used in a wide range of datasets where distance and correlation are substitute for other independent and dependent variables from experiments.

Neuroscience↗