scholarly journals Structured random receptive fields enable informative sensory encodings

2021 ◽  
Author(s):  
Biraj Pandey ◽  
Marius Pachitariu ◽  
Bingni W. Brunton ◽  
Kameron Decker Harris
Keyword(s):  
Probability Distributions ◽  
Receptive Fields ◽  
Feature Model ◽  
Frequency Noise ◽  
Second Order Statistics ◽  
V1 Neurons ◽  
Covariance Functions ◽  
Training Samples ◽  
Classical Models ◽  
Using Data

AbstractThe brain must represent the outside world in a way that enables an animal to survive and thrive. In early sensory systems, populations of neurons have a variety of receptive fields that are structured to detect features in input statistics. Alongside this structure, experimental recordings consistently show that these receptive fields also have a great deal of unexplained variability, which has often been ignored in classical models of sensory neurons. In this work, we model neuronal receptive fields as random samples from probability distributions in two sensory modalities, using data from insect mechanosensors and from neurons of mammalian primary visual cortex (V1). In particular, we build generative receptive field models where our random distributions are Gaussian processes with covariance functions that match the second-order statistics of experimental receptive data. We show theoretical results that these random feature neurons effectively perform randomized wavelet transform on the inputs in the temporal domain for mechanosensory neurons and spatial domain for V1 neurons. Such a transformation removes irrelevant components in the inputs, such as high-frequency noise, and boosts the signal. We demonstrate that these random feature neurons produce better learning from fewer training samples and with smaller networks in a variety of artificial tasks. The random feature model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

scholarly journals Emergent orientation selectivity from random networks in mouse visual cortex

2018 ◽  
Author(s):  
J.J. Pattadkal ◽  
G. Mato ◽  
C. van Vreeswijk ◽  
N. J. Priebe ◽  
D. Hansel
Keyword(s):  
Visual Cortex ◽  
Calcium Imaging ◽  
Receptive Fields ◽  
Orientation Selectivity ◽  
Random Networks ◽  
Two Dimensional ◽  
V1 Neurons ◽  
Mouse Visual Cortex ◽  
Whole Cell Recordings ◽  
Random Connectivity

SummaryWe study the connectivity principles underlying the emergence of orientation selectivity in primary visual cortex (V1) of mammals lacking an orientation map. We present a computational model in which random connectivity gives rise to orientation selectivity that matches experimental observations. It predicts that mouse V1 neurons should exhibit intricate receptive fields in the two-dimensional frequency domain, causing shift in orientation preferences with spatial frequency. We find evidence for these features in mouse V1 using calcium imaging and intracellular whole cell recordings.

scholarly journals Spatial and Temporal Organization of Composite Receptive Fields in the Songbird Auditory Forebrain

2021 ◽  
Author(s):  
Nasim Winchester Vahidi
Keyword(s):  
Vocal Communication ◽  
Receptive Fields ◽  
Temporal Organization ◽  
Quadratic Model ◽  
Bird Songs ◽  
Communication Signals ◽  
Complete Representation ◽  
Auditory Forebrain ◽  
Neuron Populations ◽  
Classical Models

The mechanisms underlying how single auditory neurons and neuron populations encode natural and acoustically complex vocal signals, such as human speech or bird songs, are not well understood. Classical models focus on individual neurons, whose spike rates vary systematically as a function of change in a small number of simple acoustic dimensions. However, neurons in the caudal medial nidopallium (NCM), an auditory forebrain region in songbirds that is analogous to the secondary auditory cortex in mammals, have composite receptive fields (CRFs) that comprise multiple acoustic features tied to both increases and decreases in firing rates. Here, we investigated the anatomical organization and temporal activation patterns of auditory CRFs in European starlings exposed to natural vocal communication signals (songs). We recorded extracellular electrophysiological responses to various bird songs at auditory NCM sites, including both single and multiple neurons, and we then applied a quadratic model to extract large sets of CRF features that were tied to excitatory and suppressive responses at each measurement site. We found that the superset of CRF features yielded spatially and temporally distributed, generalizable representations of a conspecific song. Individual sites responded to acoustically diverse features, as there was no discernable organization of features across anatomically ordered sites. The CRF features at each site yielded broad, temporally distributed responses that spanned the entire duration of many starling songs, which can last for 50 s or more. Based on these results, we estimated that a nearly complete representation of any conspecific song, regardless of length, can be obtained by evaluating populations as small as 100 neurons. We conclude that natural acoustic communication signals drive a distributed yet highly redundant representation across the songbird auditory forebrain, in which adjacent neurons contribute to the encoding of multiple diverse and time-varying spectro-temporal features.

Benfordʼs Law Geometry

Benford's Law ◽  
2015 ◽  
Author(s):  
Lawrence Leemis
Keyword(s):  
Empirical Data ◽  
Probability Distributions ◽  
Benford’S Law ◽  
Data Sets ◽  
Normal Distributions ◽  
Benford's Law ◽  
Using Data

This chapter switches from the traditional analysis of Benford's law using data sets to a search for probability distributions that obey Benford's law. It begins by briefly discussing the origins of Benford's law through the independent efforts of Simon Newcomb (1835–1909) and Frank Benford, Jr. (1883–1948), both of whom made their discoveries through empirical data. Although Benford's law applies to a wide variety of data sets, none of the popular parametric distributions, such as the exponential and normal distributions, agree exactly with Benford's law. The chapter thus highlights the failures of several of these well-known probability distributions in conforming to Benford's law, considers what types of probability distributions might produce data that obey Benford's law, and looks at some of the geometry associated with these probability distributions.

scholarly journals A Robust Dynamic Classifier Selection Approach for Hyperspectral Images with Imprecise Label Information

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5262
Author(s):  
Meizhu Li ◽  
Shaoguang Huang ◽  
Jasper De Bock ◽  
Gert de Cooman ◽  
Aleksandra Pižurica
Keyword(s):  
Hyperspectral Image ◽  
Probability Distributions ◽  
Imprecise Probabilities ◽  
Data Sets ◽  
Classifier Selection ◽  
Training Samples ◽  
Proposed Model ◽  
Label Information ◽  
Dynamic Classifier Selection ◽  
The Individual

Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels. Despite the growing importance of this aspect, it has not been sufficiently studied in the literature yet. In this paper, we analyze the effect of erroneous sample labels on probability distributions of the principal components of HSIs, and provide in this way a statistical analysis of the resulting uncertainty in classifiers. Building on the theory of imprecise probabilities, we develop a novel robust dynamic classifier selection (R-DCS) model for data classification with erroneous labels. Particularly, spectral and spatial features are extracted from HSIs to construct two individual classifiers for the dynamic selection, respectively. The proposed R-DCS model is based on the robustness of the classifiers’ predictions: the extent to which a classifier can be altered without changing its prediction. We provide three possible selection strategies for the proposed model with different computational complexities and apply them on three benchmark data sets. Experimental results demonstrate that the proposed model outperforms the individual classifiers it selects from and is more robust to errors in labels compared to widely adopted approaches.

scholarly journals Spatial Distribution of Contextual Interactions in Primary Visual Cortex and in Visual Perception

2000 ◽  
Vol 84 (4) ◽  
pp. 2048-2062 ◽  
Author(s):  
Mitesh K. Kapadia ◽  
Gerald Westheimer ◽  
Charles D. Gilbert
Keyword(s):  
Spatial Distribution ◽  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Cortical Neurons ◽  
Receptive Fields ◽  
Human Perception ◽  
Spatial Arrangement ◽  
Orthogonal Axis ◽  
V1 Neurons ◽  
Contextual Interactions

To examine the role of primary visual cortex in visuospatial integration, we studied the spatial arrangement of contextual interactions in the response properties of neurons in primary visual cortex of alert monkeys and in human perception. We found a spatial segregation of opposing contextual interactions. At the level of cortical neurons, excitatory interactions were located along the ends of receptive fields, while inhibitory interactions were strongest along the orthogonal axis. Parallel psychophysical studies in human observers showed opposing contextual interactions surrounding a target line with a similar spatial distribution. The results suggest that V1 neurons can participate in multiple perceptual processes via spatially segregated and functionally distinct components of their receptive fields.

Can we forecast the arrival of ICMEs for the whole Solar Systems?

2020 ◽  
Author(s):  
Dario Del Moro ◽  
Gianluca Napoletano ◽  
Francesco Berrilli ◽  
Luca Giovannelli ◽  
Ermanno Pietropaolo ◽  
...  
Keyword(s):  
Space Weather ◽  
Weather Forecasting ◽  
Probability Distributions ◽  
Computation Time ◽  
Space Mission ◽  
Interplanetary Coronal Mass Ejections ◽  
Solar Systems ◽  
Transit Times ◽  
Solar System Bodies ◽  
Using Data

<p>Solar wind transients, i.e. interplanetary coronal mass ejections (ICMEs) drive Space Weather throughout the heliosphere and the prediction of their impact on different solar system bodies is one of the primary goals of the Planetary Space Weather forecasting. We realized a procedure based on the Drag-Based Model (Vrsnak et al., 2013, Napoletano et al. 2018) which uses probability distributions for the input parameters, and allows the evaluation of the uncertainty on the forecast. This approach has been tested against a set of ICMEs whose transit times are known, obtaining extremely promising results.</p><p>We apply this model to propagate a sample of ICMEs from their sources on the solar surface into the heliosphere. We made use of the seminal works by Prise et al. (2015), Winslow et al. (2015) and Witasse et al. (2017) who tracked the ICMEs through their journeys using data from several spacecraft.</p><p>Considering the extremely short computation time needed by the model to propagate ICMEs, this approach is a promising candidate to forecast ICME arrival to planetary bodies and spacecraft in the whole heliosphere, with relevant application to space-mission short-term planning.</p>

Estimating individual benefits of medical or behavioral treatments in severely ill patients

2017 ◽  
Vol 28 (3) ◽  
pp. 911-927 ◽  
Author(s):  
Francisco J Diaz
Keyword(s):  
Clinical Trials ◽  
Chronic Conditions ◽  
Treatment Duration ◽  
Regression Models ◽  
Probability Distributions ◽  
Random Coefficients ◽  
New Development ◽  
Behavioral Treatments ◽  
The Common ◽  
Using Data

There is a need for statistical methods appropriate for the analysis of clinical trials from a personalized-medicine viewpoint as opposed to the common statistical practice that simply examines average treatment effects. This article proposes an approach to quantifying, reporting and analyzing individual benefits of medical or behavioral treatments to severely ill patients with chronic conditions, using data from clinical trials. The approach is a new development of a published framework for measuring the severity of a chronic disease and the benefits treatments provide to individuals, which utilizes regression models with random coefficients. Here, a patient is considered to be severely ill if the patient’s basal severity is close to one. This allows the derivation of a very flexible family of probability distributions of individual benefits that depend on treatment duration and the covariates included in the regression model. Our approach may enrich the statistical analysis of clinical trials of severely ill patients because it allows investigating the probability distribution of individual benefits in the patient population and the variables that influence it, and we can also measure the benefits achieved in specific patients including new patients. We illustrate our approach using data from a clinical trial of the anti-depressant imipramine.

The response dynamics of primate visual cortical neurons to simulated optical blur

2009 ◽  
Vol 26 (4) ◽  
pp. 411-420 ◽  
Author(s):  
MICHAEL L. RISNER ◽  
TIMOTHY J. GAWNE
Keyword(s):  
Cortical Neurons ◽  
Receptive Fields ◽  
Luminance Contrast ◽  
Form Perception ◽  
Response Dynamics ◽  
V1 Neurons ◽  
Spatial Frequencies ◽  
Wide Range ◽  
Optical Blur ◽  
Visual Cortical

AbstractNeurons in visual cortical area V1 typically respond well to lines or edges of specific orientations. There have been many studies investigating how the responses of these neurons to an oriented edge are affected by changes in luminance contrast. However, in natural images, edges vary not only in contrast but also in the degree of blur, both because of changes in focus and also because shadows are not sharp. The effect of blur on the response dynamics of visual cortical neurons has not been explored. We presented luminance-defined single edges in the receptive fields of parafoveal (1–6 deg eccentric) V1 neurons of two macaque monkeys trained to fixate a spot of light. We varied the width of the blurred region of the edge stimuli up to 0.36 deg of visual angle. Even though the neurons responded robustly to stimuli that only contained high spatial frequencies and 0.36 deg is much larger than the limits of acuity at this eccentricity, changing the degree of blur had minimal effect on the responses of these neurons to the edge. Primates need to measure blur at the fovea to evaluate image quality and control accommodation, but this might only involve a specialist subpopulation of neurons. If visual cortical neurons in general responded differently to sharp and blurred stimuli, then this could provide a cue for form perception, for example, by helping to disambiguate the luminance edges created by real objects from those created by shadows. On the other hand, it might be important to avoid the distraction of changing blur as objects move in and out of the plane of fixation. Our results support the latter hypothesis: the responses of parafoveal V1 neurons are largely unaffected by changes in blur over a wide range.

scholarly journals Can Fake News Detection Models Maintain the Performance through Time? A Longitudinal Evaluation of Twitter Publications

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2988
Author(s):  
Nuno Guimarães ◽  
Álvaro Figueira ◽  
Luís Torgo
Keyword(s):  
Social Networks ◽  
Negative Impact ◽  
Feature Model ◽  
Training Data ◽  
Fake News ◽  
Longitudinal Evaluation ◽  
Short Period ◽  
Testing Data ◽  
Using Data

The negative impact of false information on social networks is rapidly growing. Current research on the topic focused on the detection of fake news in a particular context or event (such as elections) or using data from a short period of time. Therefore, an evaluation of the current proposals in a long-term scenario where the topics discussed may change is lacking. In this work, we deviate from current approaches to the problem and instead focus on a longitudinal evaluation using social network publications spanning an 18-month period. We evaluate different combinations of features and supervised models in a long-term scenario where the training and testing data are ordered chronologically, and thus the robustness and stability of the models can be evaluated through time. We experimented with 3 different scenarios where the models are trained with 15-, 30-, and 60-day data periods. The results show that detection models trained with word-embedding features are the ones that perform better and are less likely to be affected by the change of topics (for example, the rise of COVID-19 conspiracy theories). Furthermore, the additional days of training data also increase the performance of the best feature/model combinations, although not very significantly (around 2%). The results presented in this paper build the foundations towards a more pragmatic approach to the evaluation of fake news detection models in social networks.

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