1,720,976 research outputs found
A Bayesian Account for Estimating the Number of Neurons during Spike Sorting
No full textExtracellular recordings have long been an invaluable tool for understanding neural
population activity. Spike sorting is the process of unmixing the contributing sources in
a recording to obtain the spiking activity of individual neurons. Identifying the correct
number of neurons is an error-prone process involving a considerable amount of intrinsic
uncertainty. However, most spike sorting algorithms do not account for this uncertainty,
but instead use a single point estimate. Using a fully probabilistic approach, we
demonstrate that the point estimate leads to systematic misestimation of the number of
neurons.
We estimate the number of neurons present in the data by sampling from the actual
posterior distribution using reversible jump Markov chain Monte Carlo, in the context
of realistic ground truth data. The expected value of the probabilistic estimate is then
compared to the widely used maximum a posteriori (MAP) estimate of the number of
neurons.
We find that even in the absence of incorrect modelling assumptions, using a point
estimate leads to a systematic underestimation of the number of present neurons. This
effect is visible for a wide range of values for the recording time and the noise available
in the recording. More specifically, we find that decreasing noise leads to a decrease in
this bias only for high sorting accuracy. If the sorting accuracy is low, this effect is
reversed. Furthermore, we find that the size of the bias can initially be decreased by
increasing the recording time, but for longer recordings this effect comes to a halt.
Misestimating the number of neurons contributes to errors in dividing spikes into
clusters, and thus impacts the clarity of the results, e.g. by fusing different neurons, or
splitting single neurons. As a consequence, correlations and other estimated properties
would be affected. The present results provide an analytical guide to correct for this
error
Analytical Properties of Model Performance Evaluation Using Predictive Power in Neuroscience
No full textThe presence of external and internal noise is a ubiquitous challenge when modelling complex neurobiological processes. In particular, to assess the quality of a model, it is essential to evaluate its predictions in the context of noise, or more generally, uncon-trolled variance. A recently defined indicator, predictive power, provides model quality estimates corrected for the uncontrolled variance. We provide an analytic derivation of predictive power and explore its convergence properties and model dependence. We find that predictive power and its variance exhibit fast convergence as a function of the number of trials. Reliable results are achieved with both linear, semilinear (e.g. gen-eralized linear) and nonlinear models for Gaussian noise, although behaviour for other distributions is less consistent. Predictive power further exhibits a dependence on model dimension, which can be compensated by the combination of cross-validation and in-sample estimates. In summary, predictive power exhibits fast and reliable convergence for different models and noise-characteristics and thus provides a useful tool for the assessment of model quality across many disciplines in neuroscience and computational biology
Reconstructing the perceptual organization of sound from neural responses
No full textBackground: Sets of stimuli can span different stimulus spaces. Examples include linear, circular or planar. Since the neural system does not know the geometry of this stimulus space, it needs to have a way of estimating it from information contained in the neural population responses. Recently, a set of techniques was proposed that can achieve this estimation, known as representational similarity analysis (RSA).
Methods: We expand the current framework of RSA by establishing a criterion for taking into account the local geometry of the neural response manifold. We refer to this expansion as gRSA (global RSA). To do so, we compute distances between stimuli within the response manifold (Local Distance Matrix, LDM). Once pairwise distances have been identified, we reconstruct the global geometry from the local geometry by recreating the neighborhoods of the manifold (Global Distance Matrix, GDM). The GDM is constructed by stochastic exploration of the LDM. Once a certain value of cross correlation is established two neighbors are identified based on a local decoder. That way, the path between two stimuli in the response manifold can be thought as the shortest distance between two responses within that manifold.
Results: We applied gRSA to simulations and real data (neural responses from the auditory cortex of the ferret). We successfully reconstructed the stimulus geometry of the simulated data. The analysis led to a satisfactory reconstruction of the stimulus space geometry for the real responses.
Conclusion: The perseverance of similarity from the external to the internal space (2nd order isomorphism) is only achieved when the local geometry is taken into account. Our results showed that when this local aspect is not taken into account, the 2nd order isomorphism is sometimes violated and the stimulus space reconstruction can fail
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Bayesian integration of tactile and motor information reduces uncertainty about an object’s location in mice
No full textMice modulate their rhythmic whisking behaviour when they encounter objects in order to influence the acquired sensory information from the touched surface. However, how the motor commands are selected and what mechanism is used to integrate tactile and self-motion information over space and time for object localization is still under debate. We showed with an optimal control and a Bayesian model that a mouse can match their whisking amplitude to the distance to an object in order to prevent strong bending of their whiskers. Moreover, the mouse could estimate an object’s location in head-centered coordinates by optimally integrating contact signals with body movements (velocity) and whisker movements (angular velocity). This resulted in a reduction in uncertainty, but not in accuracy, about the platform location with multiple contacts. Moreover, this uncertainty reduction was more pronounced when distributed sensory signals coming from multiple whiskers were included. The results suggest that body and whisker velocity, and contact information are sufficient in order to get a reliable estimate of object location. We propose a mechanism of sensorimotor integration in the mouse’s brain
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Scale-Free Dynamics of Brain Network Activity in Mice During Novelty and Exploration.
No full textAbstract: The brain is considered a "critical system," which continuously transitions between two
phases: in one the neural activity amplifies and spreads over the largest distances in the network,
and in the other the neural activity is reduced and localized. A strong indication that a system is
in a critical state is scale-free behaviour, which is best described by the exponent of a power-law
function. This scaling exponent can be obtained from Demeaned Fluctuation Analysis (DMA), and
indicates in which state the system is. In this study, we analyzed local field potential (LFP)
recordings from the hippocampal-cortical network in 6 mice during an object recognition task, and
DMA was applied for frequencies from 2 Hz to 150 Hz to identify neural oscillations and regions
indicating above-noise level scaling exponents for each experimental stage. Our results suggest
that there is a significant increase of hippocampal scaling exponents in beta
(24-29 Hz) associated with novelty and exploration compared to rest. We also found evidence
suggesting that different CA1 hippocampal sides might be contributing differently to the scaling
properties of theta (4-7 Hz) associated with novelty detection. We hypothesize that scaling
dynamics in theta might be reflecting coordination of information in the hippocampal-cortical
network during object recognition. The greatest variability in scaling dynamics was observed in
gamma (96-102) in the parietal cortex during object exploration. We therefore hypothesize that
parietal gamma scaling dynamics reflect a rather general mechanism involved in the task.
Overall, our results suggest that the scaling dynamics of different frequency bands can be linked
to behavioral outcomes, and reflect different processes involved in the object recognition task.
Keywords: Demeaned Fluctuation Analysis, Criticality, Scaling Exponents, LFP, Object
Exploration, Novelty
Scale-Free Dynamics of Brain Network Activity in Mice During Novelty and Exploration.
No full textAbstract: The brain is considered a "critical system," which continuously transitions between
two phases: in one the neural activity amplifies and spreads over the largest distances in the
network, and in the other the neural activity is reduced and localized. A strong indication that a
system is in a critical state is scale-free behaviour, which is best described by the exponent of a
power-law function. This scaling exponent can be obtained from Demeaned Fluctuation Analysis
(DMA), and indicates in which state the system is. In this study, we analyzed local field potential
(LFP) recordings from the hippocampal-cortical network in 6 mice during an object recognition
task, and DMA was applied for frequencies from 2 Hz to 150 Hz to identify neural oscillations
and regions indicating above-noise level scaling exponents for each experimental stage. Our
results suggest that there is a significant increase of hippocampal scaling exponents in beta
(24-29 Hz) associated with novelty and exploration compared to rest. We also found evidence
suggesting that different CA1 hippocampal sides might be contributing differently to the scaling
properties of theta (4-7 Hz) associated with novelty detection. We hypothesize that scaling
dynamics in theta might be reflecting coordination of information in the hippocampal-cortical
network during object recognition. The greatest variability in scaling dynamics was observed in
gamma (96-102) in the parietal cortex during object exploration. We therefore hypothesize that
parietal gamma scaling dynamics reflect a rather general mechanism involved in the task.
Overall, our results suggest that the scaling dynamics of different frequency bands can be linked
to behavioral outcomes, and reflect different processes involved in the object recognition task.
Keywords : Demeaned Fluctuation Analysis, Criticality, Scaling Exponents, LFP, Object
Exploration, Novelty
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