Patricia Ñeco, Angelo G. Torrente, Pietro Mesirca, Esther Zorio, Nian Liu, Silvia G. Priori, Carlo Napolitano, Sylvain Richard, Jean‐Pierre Bénitah, Matteo E. Mangoni, Ana M. Gómez,
Background— Catecholaminergic polymorphic ventricular tachycardia is characterized by stress-triggered syncope and sudden death. Patients with catecholaminergic polymorphic ventricular tachycardia manifest sinoatrial node (SAN) dysfunction, the mechanisms of which remain unexplored. Methods and Results— We investigated SAN [Ca 2+ ] i handling in mice carrying the catecholaminergic polymorphic ventricular tachycardia–linked mutation of ryanodine receptor (RyR2 R4496C ) and their wild-type (WT) littermates. ...
Tópico(s): Cardiac Arrhythmias and Treatments
2012 - Lippincott Williams & Wilkins | Circulation
William R. Softky, Christof Koch,
... 5): 643–646. https://doi.org/10.1162/neco.1992.4.5.643 Article history Received: October 29 1991 Accepted: February 04 1992 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn Email Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation William R. Softky, Christof Koch; Cortical Cells Should Fire Regularly, But Do Not. Neural Comput 1992; 4 (5): 643–646. doi: https://doi.org/10.1162/neco.1992.4.5.643 Download citation file: Ris ( ...
Tópico(s): Advanced Memory and Neural Computing
1992 - The MIT Press | Neural Computation
William G. Baxt, Halbert White,
... 3): 624–638. https://doi.org/10.1162/neco.1995.7.3.624 Article history Received: April 21 1994 Accepted: July 22 1994 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn Email Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation William G. Baxt, Halbert White; Bootstrapping Confidence Intervals for Clinical Input Variable Effects in a Network Trained to Identify the Presence of Acute Myocardial Infarction. Neural Comput 1995; 7 (3): 624–638. doi: https://doi.org/10.1162/neco.1995.7.3.624 Download citation file: Ris ( ...
Tópico(s): Machine Learning in Healthcare
1995 - The MIT Press | Neural Computation
Produção Nacional
Revisado por pares
Kyria Santiago Nascimento, David Alencar Araripe, Vanir Reis Pinto‐Junior, Vinicius José Silva Osterne, Francisco William Viana Martins, Antônio Hadson Bastos Neco, Gil A. Farias, Benildo Sousa Cavada,
Tópico(s): Biochemical and Molecular Research
2018 - Springer Science+Business Media | Journal of Molecular Modeling
Elizabeth B. Torres, Richa Rai, Sejal Mistry, Brenda Gupta,
The research-grade Autism Diagnostic Observational Schedule (ADOS) is a broadly used instrument that informs and steers much of the science of autism. Despite its broad use, little is known about the empirical variability inherently present in the scores of the ADOS scale or their appropriateness to define change and its rate, to repeatedly use this test to characterize neurodevelopmental trajectories. Here we examine the empirical distributions of research-grade ADOS scores from 1324 records in ...
Tópico(s): Virology and Viral Diseases
2020 - The MIT Press | Neural Computation
Samuel Oye Bandele, Arimiyau Adewale,
... using Z-test, Fisher’s Transformation and Hotelling Williams Test. The results of the findings showed that WAEC, NECO and NABTEB mathematics achievement Examinations are highly reliable ...
Tópico(s): Educational Assessment and Pedagogy
2013 - Richtmann Publishing | Journal of Educational and Social Research
Lance R. Williams, David W. Jacobs,
We describe a local parallel method for computing the stochastic completion field introduced in the previous article (Williams and Jacobs, 1997). The stochastic completion field represents the likelihood that a completion joining two contour fragments passes through any given position and orientation in the image plane. It is based on the assumption that the prior probability distribution of completion shape can be modeled as a random walk in a lattice of discrete positions and orientations. The ...
Tópico(s): Image and Signal Denoising Methods
1997 - The MIT Press | Neural Computation
E. Paxon Frady, Ashish Kapoor, Eric Horvitz, William B. Kristan,
Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/subjects, become exponentially more difficult in high dimensions, such as recognizing dozens of neurons/brain regions simultaneously. We present a framework and tools for functional neurocartography-the large- ...
Tópico(s): EEG and Brain-Computer Interfaces
2016 - The MIT Press | Neural Computation
Samuel A. Neymotin, Robert A. McDougal, Mohamed Sherif, Christopher P. Fall, Michael L. Hines, William W. Lytton,
Calcium ([Formula: see text]) waves provide a complement to neuronal electrical signaling, forming a key part of a neuron’s second messenger system. We developed a reaction-diffusion model of an apical dendrite with diffusible inositol triphosphate ([Formula: see text]), diffusible [Formula: see text], [Formula: see text] receptors ([Formula: see text]s), endoplasmic reticulum (ER) [Formula: see text] leak, and ER pump (SERCA) on ER. [Formula: see text] is released from ER stores via [Formula: see text]s upon ...
Tópico(s): Neuroscience and Neuropharmacology Research
2015 - The MIT Press | Neural Computation
George L. Chadderdon, Ashutosh Mohan, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Joseph T. Francis, Gordon M. Shepherd, William W. Lytton,
The deceptively simple laminar structure of neocortex belies the complexity of intra- and interlaminar connectivity. We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would ...
Tópico(s): Neuroscience and Neuropharmacology Research
2014 - The MIT Press | Neural Computation
Ali A. Minai, William B. Levy,
We investigate the dynamics of a class of recurrent random networks with sparse, asymmetric excitatory connectivity and global shunting inhibition mediated by a single interneuron. Using probabilistic arguments and a hyperbolic tangent approximation to the gaussian, we develop a simple method for setting the average level of firing activity in these networks. We demonstrate through simulations that our technique works well and extends to networks with more complicated inhibitory schemes. We are ...
Tópico(s): Neuroscience and Neuropharmacology Research
1994 - The MIT Press | Neural Computation
Peter Hancock, Leslie S. Smith, William A. Phillips,
We show that a form of synaptic plasticity recently discovered in slices of the rat visual cortex (Artola et al. 1990) can support an error-correcting learning rule. The rule increases weights when both pre- and postsynaptic units are highly active, and decreases them when pre-synaptic activity is high and postsynaptic activation is less than the threshold for weight increment but greater than a lower threshold. We show that this rule corrects false positive outputs in feedforward associative memory, ...
Tópico(s): Memory and Neural Mechanisms
1991 - The MIT Press | Neural Computation
A nonlinear artificial neural network trained by backpropagation was applied to the diagnosis of acute myocardial infarction (coronary occlusion) in patients presenting to the emergency department with acute anterior chest pain. Three-hundred and fifty-six patients were retrospectively studied, of which 236 did not have acute myocardial infarction and 120 did have infarction. The network was trained on a randomly chosen set of half of the patients who had not sustained acute myocardial infarction ...
Tópico(s): Acute Myocardial Infarction Research
1990 - The MIT Press | Neural Computation
In this note, I study how the precision of a binary classifier depends on the ratio r of positive to negative cases in the test set, as well as the classifier's true and false-positive rates. This relationship allows prediction of how the precision-recall curve will change with r, which seems not to be well known. It also allows prediction of how Fβ and the precision gain and recall gain measures of Flach and Kull (2015) vary with r.
Tópico(s): Machine Learning and Data Classification
2021 - The MIT Press | Neural Computation
Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams,
Despite the fact that complex visual scenes contain multiple, overlapping objects, people perform object recognition with ease and accuracy. One operation that facilitates recognition is an early segmentation process in which features of objects are grouped and labeled according to which object they belong. Current computational systems that perform this operation are based on predefined grouping heuristics. We describe a system called MAGIC that learns how to group features based on a set of presegmented ...
Tópico(s): Advanced Image and Video Retrieval Techniques
1992 - The MIT Press | Neural Computation
The real-time recurrent learning (RTRL) algorithm (Robinson and Fallside 1987; Williams and Zipser 1989) requires O(n 4 ) computations per time step, where n is the number of noninput units. I describe a method suited for on-line learning that computes exactly the same gradient and requires fixed-size storage of the same order but has an average time complexity per time step of O(n 3 ).
Tópico(s): Energy Efficient Wireless Sensor Networks
1992 - The MIT Press | Neural Computation
William M. Brown, Alex Bäcker,
The efficiency of neuronal encoding in sensory and motor systems has been proposed as a first principle governing response properties within the central nervous system. We present a continuation of a theoretical study presented by Zhang and Sejnowski, where the influence of neuronal tuning properties on encoding accuracy is analyzed using information theory. When a finite stimulus space is considered, we show that the encoding accuracy improves with narrow tuning for one- and two-dimensional stimuli. ...
Tópico(s): Neuroscience and Neural Engineering
2006 - The MIT Press | Neural Computation
William H. Alexander, Joshua W. Brown,
Anterior cingulate and dorsolateral prefrontal cortex (ACC and dlPFC, respectively) are core components of the cognitive control network. Activation of these regions is routinely observed in tasks that involve monitoring the external environment and maintaining information in order to generate appropriate responses. Despite the ubiquity of studies reporting coactivation of these two regions, a consensus on how they interact to support cognitive control has yet to emerge. In this letter, we present ...
Tópico(s): Functional Brain Connectivity Studies
2015 - The MIT Press | Neural Computation
Samuel A. Neymotin, George L. Chadderdon, Cliff C. Kerr, Joseph T. Francis, William W. Lytton,
Neocortical mechanisms of learning sensorimotor control involve a complex series of interactions at multiple levels, from synaptic mechanisms to cellular dynamics to network connectomics. We developed a model of sensory and motor neocortex consisting of 704 spiking model neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA and GABA A synapses. We trained our model using spike-timing-dependent reinforcement learning ...
Tópico(s): Neuroscience and Neural Engineering
2013 - The MIT Press | Neural Computation
Daniel Ruderman, William Bialek,
In many biological systems the primary transduction of sensory stimuli occurs in a regular array of receptors. Because of this discrete sampling it is usually assumed that the organism has no knowledge of signals beyond the Nyquist frequency. In fact, higher frequency signals are expected to mask the available lower frequency information as a result of aliasing. It has been suggested that these considerations are important in understanding, for example, the design of the receptor lattice in the ...
Tópico(s): Spectroscopy and Chemometric Analyses
1992 - The MIT Press | Neural Computation
An algorithm, called RTRL, for training fully recurrent neural networks has recently been studied by Williams and Zipser (1989a, b). Whereas RTRL has been shown to have great power and generality, it has the disadvantage of requiring a great deal of computation time. A technique is described here for reducing the amount of computation required by RTRL without changing the connectivity of the networks. This is accomplished by dividing the original network into subnets for the purpose of error propagation ...
Tópico(s): Ferroelectric and Negative Capacitance Devices
1989 - The MIT Press | Neural Computation
Neural network outputs are interpreted as parameters of statistical distributions. This allows us to fit conditional distributions in which the parameters depend on the inputs to the network. We exploit this in modeling multivariate data, including the univariate case, in which there may be input-dependent (e.g., time-dependent) correlations between output components. This provides a novel way of modeling conditional correlation that extends existing techniques for determining input-dependent (local) ...
Tópico(s): Fault Detection and Control Systems
1996 - The MIT Press | Neural Computation
Information about context can enable local processors to discover latent variables that are relevant to the context within which they occur, and it can also guide short-term processing. For example, Becker and Hinton (1992) have shown how context can guide learning, and Hummel and Biederman (1992) have shown how it can guide processing in a large neural net for object recognition. This article studies the basic capabilities of a local processor with two distinct classes of inputs: receptive field ...
Tópico(s): Domain Adaptation and Few-Shot Learning
1997 - The MIT Press | Neural Computation
Standard techniques for improved generalization from neural networks include weight decay and pruning. Weight decay has a Bayesian interpretation with the decay function corresponding to a prior over weights. The method of transformation groups and maximum entropy suggests a Laplace rather than a gaussian prior. After training, the weights then arrange themselves into two classes: (1) those with a common sensitivity to the data error and (2) those failing to achieve this sensitivity and that therefore ...
Tópico(s): Gaussian Processes and Bayesian Inference
1995 - The MIT Press | Neural Computation
Previous application of a mathematical theory of chains of coupled oscillators to the results of experiments on the lamprey spinal cord led to conclusions about the mechanisms of intersegmental coordination in the lamprey. The theory provides no direct link, however, to electrophysiological data obtained at the cellular level, nor are the details of the neuronal circuitry in the lamprey known. In this paper, a variant of the theory is developed for which the relevant variables can potentially be ...
Tópico(s): stochastic dynamics and bifurcation
1992 - The MIT Press | Neural Computation
When either detection rate (sensitivity) or false alarm rate (specificity) is optimized in an artificial neural network trained to identify myocardial infarction, the increase in the accuracy of one is always done at the expense of the accuracy of the other. To overcome this loss, two networks that were separately trained on populations of patients with different likelihoods of myocardial infarction were used in concert. One network was trained on clinical pattern sets derived from patients who had ...
Tópico(s): Anomaly Detection Techniques and Applications
1992 - The MIT Press | Neural Computation
William Severa, Ojas Parekh, Conrad D. James, James B. Aimone,
The dentate gyrus forms a critical link between the entorhinal cortex and CA3 by providing a sparse version of the signal. Concurrent with this increase in sparsity, a widely accepted theory suggests the dentate gyrus performs pattern separation-similar inputs yield decorrelated outputs. Although an active region of study and theory, few logically rigorous arguments detail the dentate gyrus's (DG) coding. We suggest a theoretically tractable, combinatorial model for this action. The model provides ...
Tópico(s): Neuroscience and Neuropharmacology Research
2016 - The MIT Press | Neural Computation
Daniel G. Partridge, William B. Yates,
In this paper we address the problem of constructing reliable neural-net implementations, given the assumption that any particular implementation will not be totally correct. The approach taken in this paper is to organize the inevitable errors so as to minimize their impact in the context of a multiversion system, i.e., the system functionality is reproduced in multiple versions, which together will constitute the neural-net system. The unique characteristics of neural computing are exploited in ...
Tópico(s): Fault Detection and Control Systems
1996 - The MIT Press | Neural Computation
In this paper we discuss the asymptotic properties of the most commonly used variant of the backpropagation algorithm in which network weights are trained by means of a local gradient descent on examples drawn randomly from a fixed training set, and the learning rate η of the gradient updates is held constant (simple backpropagation). Using stochastic approximation results, we show that for η → 0 this training process approaches a batch training. Further, we show that for small η one can approximate ...
Tópico(s): Model Reduction and Neural Networks
1994 - The MIT Press | Neural Computation
Ronald J. Williams, David Zipser,
The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have (1) the advantage that they do not require a precisely defined training interval, operating while the network runs; and (2) the disadvantage that they require nonlocal communication in the network being trained and are computationally expensive. These algorithms ...
Tópico(s): Cognitive Science and Education Research
1989 - The MIT Press | Neural Computation