Leon N Cooper

• PhD
• Professor at Brown University

We report on mobility measurements of electron bubbles in superfluid helium-4. Electrons are introduced into the liquid from a plasma discharge in the vapor. For electrons with energy only a small amount above the minimum energy needed to enter the liquid, the wave function is partially transmitted into the liquid. We investigate the possibility th...

In a series of previous studies, we provided a stochastic description of a theory of synaptic plasticity. This theory, called BCM from the names of the three authors, has been formulated in two ways: the original formulation, where the plasticity threshold is defined as the square of the time-averaged neuronal activity, and a newer formulation, whe...

Gold nanoparticles are a potential method for enhancing radiation therapy, causing extra damage to tumors when irradiated through the Auger effect. One of the major obstacles to using gold nanoparticles in human trials is the relatively large amount of gold required. This paper details an experiment where a relatively small amount of gold (200 μg)...

Significance Copper-cysteamine nanoparticles can be activated directly by X-rays to produce singlet oxygen. The use of copper-cysteamine nanoparticles (conjugated with pH-low insertion peptide) can enhance the effects of X-ray–induced photodynamic therapy, to lead to improved tumor treatment in mice. The results of this study demonstrate the potent...

Biological indicators would be of use in radiation dosimetry in situations where an exposed person is not wearing a dosimeter, or when physical dosimeters are insufficient to estimate the risk caused by the radiation exposure. In this work, we investigate the use of gene expression as a dosimeter. Gene expression analysis was done on 15,222 genes o...

Exotic ions are negatively charged objects which have been detected in superfluid helium-4 at temperatures in the vicinity of 1 K. Mobility experiments in several different labs have revealed the existence of at least 18 such objects. These ions have a higher mobility than the normal negative ion and appear to be singly charged and smaller. We summ...

Enhancing the effect of radiation on tumors would be a significant improvement in radiation therapy. With radiation enhancement, less radiation could be used to achieve the same goals, lessening damage to healthy tissue and lessening side effects. Gold nanoparticles are a promising method for achieving this enhancement, particularly when the gold n...

Significance Nanometer-sized gold particles are shown to increase the effectiveness of radiation in killing cancer cells. Improved radiation effectiveness allows less radiation to be used, reducing adverse effects to patients. Alternatively, more cancer killing could be possible while using current radiation doses. Here we used pH Low-Insertion Pep...

An electron in liquid helium forces open a cavity referred as an electron bubble. These objects have been studied in many past experiments. It has been discovered that under certain conditions other negatively charged objects can be produced but the nature of these “exotic ions” is not understood. We have made a series of experiments to measure the...

We investigate the biological effects of radiation using adult Drosophila melanogaster as a model organism, focusing on gene expression and lifespan analysis to determine the effect of different radiation doses. Our results support a threshold effect in response to radiation: no effect on lifespan and no permanent effect on gene expression is seen...

The time-of-flight (ToF) estimation problem is common in sonar, ultrasound, radar, and other remote sensing applications. The conventional ToF maximum-likelihood estimator (MLE) exhibits a rapid deterioration in the accuracy when the signal-to-noise ratio (SNR) falls below a certain threshold. This threshold effect emerges mostly due to appearance...

The mean squared error of the classical maximum likelihood time-of-flight (ToF) estimator increases dramatically when the signal-to-noise ratio falls below a certain threshold. For narrow-band signals, this well-known threshold effect occurs largely due to the biased outliers which are induced by the local maxima of the source signal autocorrelatio...

When the signal-to-noise ratio (SNR) falls below a certain level, the error of the time-of-flight (ToF) maximum likelihood estimator abruptly increases due to the well-known threshold effect. Nevertheless, operating near and below the threshold SNR value might be necessary for many remote sensing applications due to power-related constraints. These...

Abstract Because a very large number of gene expression data sets are currently publicly available, comparisons across experiments between different laboratories have become a common task. However, most existing methods of comparing gene expression data sets require setting arbitrary cutoffs (e.g., for statistical significance or fold change), whic...

Thirty years have passed since the publication of Elie Bienenstock, Leon Cooper and Paul Munro's 'Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex', known as the BCM theory of synaptic plasticity. This theory has guided experimentalists to discover some fundamental properties of sy...

Time of arrival (ToA) estimation is essential for many types of remote sensing applications including radar, sonar, and underground exploration. The standard method for ToA estimation employs a matched filter for computing the maximum likelihood estimator (MLE) for ToA. The accuracy of the MLE decreases rapidly whenever the amount of noise in a rec...

Dual phospho/dephosphorylation cycles, as well as covalent enzymatic-catalyzed modifications of substrates are widely diffused within cellular systems and are crucial for the control of complex responses such as learning, memory, and cellular fate determination. Despite the large body of deterministic studies and the increasing work aimed at elucid...

Environmental and genetic interventions extend health span in a range of organisms by triggering changes in different specific but complementary pathways. We investigated the gene expression changes that occur across species when health span is extended via different interventions. To perform this comparison using heterogeneous datasets from differ...

A multiple comparison approach using whole genome transcriptional arrays was used to identify genes and pathways involved in calorie restriction/dietary restriction (DR) life span extension in Drosophila. Starting with a gene centric analysis comparing the changes in common between DR and two DR related molecular genetic life span extending manipul...

Some mammals use sound signals for communications and navigation in the air (bats) or underwater (dolphins). Recent biological discovery shows that blind mole rat is capable of detecting and avoiding underground obstacles using reflection from seismic signals. Such a remarkable capacity relies on the ability to localize the source of the reflection...

Memory fades. Is it possible to recreate the events that led to BCS? Can we recapture the great difficulty (even conjectured insolubility) superconductivity presented more than half a century ago? Perhaps not. But yellowed notes, like tea-soaked crumbs of madeleine, have awakened for me, sometimes with startling clarity, images and memories from th...

Time of Arrival (ToA) estimation is a cornerstone of many of the remote sensing applications including radar, sonar, and reflective seismology. The conventional Matched Filter Maximum Likelihood (MFML) ToA estimator suffers from rapid deterioration in the accuracy as Signal to Noise Ratio (SNR) falls below certain threshold value. In this paper we...

We analyze the effects of noise correlations in the input to, or among, BCM neurons using the Wigner semicircular law to construct random, positive-definite symmetric correlation matrices and compute their eigenvalue distributions. In the finite dimensional case, we compare our analytic results with numerical simulations and show the effects of cor...

Monocular deprivation experiments can be used to distinguish between different ideas concerning properties of cortical synaptic plasticity. Monocular deprivation by lid suture causes a rapid disconnection of the deprived eye connected to cortical neurons whereas total inactivation of the deprived eye produces much less of an ocular dominance shift....

Detection and identification of partially occluded targets in complex scenes becomes an increasingly important task in light of the latest developments in urban warfare. The construction of a system that can automatically identify selected targets or direct soldiers attention to the locations that may contain suspicious activity can be of great use...

During perception of complex objects, the highest density of fixations occurs on the regions that are most salient. For example, when looking at a face, the regions that receive the largest density of fixations are the eyes, the nose, and the mouth. The fact that some regions within an object are more informative than other regions means that a lea...

We show that a 2-step phospho/dephosphorylation cycle for the alpha-amino-3-hydroxy-5-methyl-4-isoxazole proprionic acid receptor (AMPAR), as used in in vivo learning experiments to assess long-term potentiation (LTP) induction and establishment, exhibits bistability for a wide range of parameters, consistent with values derived from biological lit...

In this work we introduce a probabilistic model for classifying segmented images. The proposed classifier is very general and it can deal both with images that were segmented with deterministic algorithms, such as the k-means algorithm, and with probabilistic clustering approaches, such as the Hidden Markov Random Field (HMRF) algorithm. Similarly,...

In this work we present a model that uses a Dirichlet process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient of the spatial constraint, which is locally dependent on each site, modulates the time-variant transition probability matrix. In our model, we use the DP in combination wi...

Ocular dominance (OD) plasticity is a robust paradigm for examining the functional consequences of synaptic plasticity. Previous experimental and theoretical results have shown that OD plasticity can be accounted for by known synaptic plasticity mechanisms, using the assumption that deprivation by lid suture eliminates spatial structure in the depr...

Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statistical test. This problem is typically approached by applying post hoc corrections to control the false discovery rate, without taking into account prior biological knowledge. Pathway o...

BCM (Bienenstock et al., 1982) refers to the theory of synaptic modification first proposed by Elie Bienenstock, Leon Cooper, and Paul Munro in 1982 to account for experiments measuring the selectivity of neurons in primary sensory [8]cortex and its dependency on neuronal input. It is characterized by a rule expressing synaptic change as a Hebb-lik...

Fundamental research can yield unforeseen benefits of great value for society, but often this happens only many years after the initial breakthroughs have been made. Can society find a way to pay back this debt?

A model of conscious mechanisms called Axiomatic Consciousness Theory (ACT) is used to develop a neuroarchitectural model of visual phenomenology. The result is an extension of concepts in neural systems towards phenomenal intentionality: the lack of ...

In this work we consider the Bayesian integrate and shift (BIAS) model for learning object categories and investigate its sensitivity to changes in the sizes and locations of fixation regions. We test the model using a face category and show that the learning algorithm is robust to large variations of the regions' sizes and locations. Specifically,...

Various forms of synaptic plasticity, including spike timing-dependent plasticity, can be accounted for by calcium-dependent models of synaptic plasticity. However, recent results in which synaptic plasticity is induced by multi-spike protocols cannot simply be accounted for by linear superposition of plasticity due to spike pairs or by existing ca...

The minimum bounding sphere of a set of data, defined as the smallest sphere enclosing the data, was first used by Scholkopf et al. to estimate the VC-dimension of support vector classifiers and later applied by Tax and Duin to data domain description. Given a set of data, the minimum bounding sphere of each class can be computed by solving a quadr...

In recent years, support vector machines (SVMs) have become a popu- lar tool for pattern recognition and machine learning. Training a SVM involves solving a constrained quadratic programming problem, which requires large memory and enormous amounts of training time for large- scale problems. In contrast, the SVM decision function is fully deter- mi...

In this work we consider the Bayesian Integrate And Shift (BIAS) model for learning object categories and test its performance on learning and recognizing different object categories from real-world images. In contrast to conventional learning algorithms that require hundreds or thousands of training examples, we show that our system can learn a ne...

Abstract The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. However, it faces serious chal- lenges when patterns of different classes overlap in some regions in the feature space. In the past, many researchers developed various adaptive or discriminant metrics to improve its performance. In thi...

In this work we explore the dependence of the Bayesian Integrate And Shift (BIAS) learning algorithm on various parameters associated with designing the retina-like distribution of the receptive fields. The parameters that we consider are: the rate of increase of the sizes of the receptive fields, the overlap among the receptive fields, the size of...

In this work we present a biologically inspired algorithm for learning object categories that uses Bayesian inference to integrate information within and across fixations. In our model, an object is represented as a collection of features of specific classes arranged at specific locations with respect to the location of the fixation point. Even tho...

Although spike-timing-dependent plasticity (STDP) is well characterized when pre- and postsynaptic spikes are paired with a given time lag, how this generalizes for more complex spike-trains is unclear. Recent experiments demonstrate that contributions to synaptic plasticity from different spike pairs within a spike train do not add linearly. In th...

The k-nearest-neighbor rule is one of the most attractive pattern classification algorithms. In practice, the choice of k is determined by the cross-validation method. In this work, we propose a new method for neighborhood size selection that is based on the concept of statistical confidence. We define the confidence associated with a decision that...

In this paper we present a minimum sphere covering ap- proach to pattern classification that seeks to construct a minimum number of spheres to represent the training data and formulate it as an integer programming problem. Us- ing soft threshold functions, we further derive a linear pro- gramming problem whose solution gives rise to radial ba- sis...

Some of the most important problems of computer vision are feature extraction and subsequent localization of those features in a new image. Since it is computationally prohibitive to search for the features over all possible locations and scales, it is necessary to design an algorithm that can selectively focus on and process information from only...

In this paper we present an integer programming formulation of the minimum sphere covering problem that seeks to construct a minimum number of spheres to represent the training data. Using soft threshold functions, we further derive a linear programming problem whose solution gives rise to radial basis function classifiers and sigmoid function clas...

We prove the conjectures of Michael Britt and Lillie Fradin concerning sums of consecutive integers. Let n + (n + 1) + ... + (n + j) = (j+1)(2n+j)/2 ≡ (n, j); n ≥ 1, j ≥ 1. Conjecture I. (n, j) does not contain numbers of the form 2 N. Proof. 2 N has no prime factors other than 2. (n, j) = 1/2 (j + 1) (2n + j) has at least one prime factor larger t...

Previous sphere-based classification algorithms usually need a number of spheres in order to achieve good classification performance. In this paper, inspired by the support vector machines for classifica- tion and the support vector data description method, we present a new method for constructing single spheres that separate data with the max- imu...

The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. It can be interpreted as an empirical Bayes' classifier based on the estimated a posteriori probabilities from the k-nearest neighbors. The performance of the k-nearest neighbor rule relies on the locally constant a posteriori probability assum...

Biosonar animals have a remarkably accurate and noise tolerant sonar. Some bats use their auditory system to achieve full 3D navigation capabilities and prey discrimination. They reach a resolution in the sub-millimeter range. Likewise, some dolphins utilize their auditory system to achieve a combination of 3D navigation and internal object examina...

The k-nearest neighbor rule is one of the most attractive pattern classification algorithms. In practice, the value of k is usually determined by the cross-validation method. In this work, we propose a new method that locally determines the number of nearest neighbors based on the concept of statistical confidence. We define the confidence associat...

In this work we present the computational algorithm that combines perceptual and cognitive information during the visual search for object features. The algorithm is initially driven purely by the bottom- up information but during the recognition process it becomes more con- strained by the top-down information. Furthermore, we propose a con- crete...

In recent years, support vector machines (SVMs) have be- come a popular tool for pattern recognition and machine learning. Train- ing a SVM involves solving a constrained quadratic programming prob- lem, which requires large memory and enormous amounts of training time for large-scale problems. In contrast, the SVM decision function is fully determ...

In many regions of the brain, including the mammalian cortex, the strength of synaptic transmission can be bidirectionally regulated by cortical activity (synaptic plasticity). One line of evidence indicates that long-term synaptic potentiation (LTP) and long-term synaptic depression (LTD), correlate with the phosphorylation/dephosphorylation of si...

This work studies the dynamics of a gene expression time series network. The network, which is obtained from the correlation of gene expressions, exhibits global dynamic properties that emerge after a cell state perturbation. The main features of this network appear to be more robust when compared with those obtained with a network obtained from a...

In this work we introduce a probabilistic model that utilizes spatial contextual information to aid recognition when dealing with ambiguous segmentations of handwritten patterns. The recognition problem is formulated as an optimization problem in a Bayesian framework by explicitly conditioning on the spatial configuration of the letters. As a conse...

The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. It can be interpreted as an empirical Bayes classifier based on the estimated a posteriori probabil- ities from the k nearest neighbors. The performance of the k-nearest neighbor rule relies on the locally constant a posteriori probability as-...

Sonar systems are a key element in harbor security applications. One of the major challenges in their utilization is the presence of high levels of noise and cluster. In this paper, we analyze the distribution of errors in the time-delay estimation for a sonar system using a train of pulses and show that a robust fusion of echoes from multiple ping...

Modifications in the strengths of synapses are thought to underlie memory, learning, and development of cortical circuits. Many cellular mechanisms of synaptic plasticity have been investigated in which differential elevations of postsynaptic calcium concentrations play a key role in determining the direction and magnitude of synaptic changes. We h...

A common characteristic of most artificial recognition systems in use today is that they are bottom up approaches, meaning that high-level modules do not influence processing of low-level modules. However, there are some problems and ambiguities at the level of sensory processing, and preprocessing of the signal, that cannot be resolved without tak...

In this work, we present a context-based model for tracking object features. More specifically, the context is defined as a collection of features within the local region that surrounds the feature that is being tracked. The model does not rely on any knowledge about the object, and therefore the collection of contextual features in one frame is ju...

Using the theory of optimal receivers, the range accuracy of echolocating systems can be expressed as a function of pulse bandwidth and SNR through the well-known Woodward equation. That equation, however, was developed in the limit of very high SNRs and assumes that the correct peak of the cross-correlation function is known a priori. We show that...

In this work we present a system for detection of objects from video streams based on properties of human vision such as saccadic eye movements and selective attention. An object, in this application a car, is represented as a collection of features (horizontal and vertical edges) arranged at specific spatial locations with respect to the position...

Time-delay estimation accuracy in echolocating systems decays for increasing levels of noise until a breakpoint is reached, after which accuracy deteriorates by several orders of magnitude. In this paper we present a robust fusion of time-delay estimates from multiple pings that significantly reduces the signal-to-noise ratio corresponding to the a...

Using the theory of optimal receivers the range accuracy of echolocating systems can be expressed as a function of receiver bandwidth and signal-to-noise ratio through the well-known Woodward equation. That equation however was developed in the limit of very high signal-to-noise ratios, and assumes that the correct peak of the crosscorrelation func...

A unified, biophysically motivated Calcium-Dependent Learning model has been shown to account for various rate-based and spike time-dependent paradigms for inducing synaptic plasticity. Here, we investigate the properties of this model for a multi-synapse neuron that receives inputs with different spike-train statistics. In addition, we present a p...

The sign and magnitude of bi-directional synaptic plasticity have been shown to depend on: the rate of presynaptic stimulation, the level of postsynaptic depolarization, and the precise relative timing between pre and postsynaptic spikes. It has been proposed that these different induction paradigms can coexist, and be accounted for by a single lea...

True Genius The Life and Science of John Bardeen. Lillian Hoddeson and Vicki Daitch. Joseph Henry Press (National Academies Press), Washington, DC, 2002. 479 pp. $27.95, C$37.95. ISBN 0-309-08408-3. Hoddeson and Daitch offer an account of the life and science of the researcher who won two Nobel Prizes in physics for his contributions to the invent...

Different mechanisms that could form the molecular basis for bi-directional synaptic plasticity have been identified experimentally and corresponding biophysical models can be constructed. However, such models are complex and therefore it is hard to deduce their consequences to compare them to existing abstract models of synaptic plasticity. In thi...

In this paper, we introduce a network for pat-tern classification, referred to as Locally Confident Network (LCN), that learns object categories by partitioning the feature space into local regions with maximum confidence levels. We show that the probability of decision error over a region is a decreasing function of its confidence measure. Thus th...

In this work we present a system for detection of objects from video streams based on properties of human vision such as saccadic eye movements and selective attention. An object, in this application a car, is represented as a collection of features (horizontal and vertical edges) arranged at specific spatial locations with respect to the position...

Synapses in the brain are bidirectionally modifiable, but the routes of induction are diverse. In various experimental paradigms, N-methyl-d-aspartate receptor-dependent long-term depression and long-term potentiation have been induced selectively by varying the membrane potential of the postsynaptic neurons during presynaptic stimulation of a cons...

Abstract We consider the origin of the high-dimensional input space as a variable which can be opti- mized before or during neuronal learning. This set of variables acts as a translation on the input space in order to 2nd an optimal origin, and can be seen as an adaptive data preprocessing, included in a more general learning rule. In this framewor...

In many regions of the brain, including the mammalian cortex, the magnitude and direction of activity-dependent changes in synaptic strength depend on the frequency of presynaptic stimulation (synaptic plasticity), as well as the history of activity at those synapses (metaplasticity). We present a model of a molecular mechanism of bidirectional syn...

this paper we present a novel mathematical embodiment of bidirectional synaptic plasticity that is able to explain diverse induction protocols with a fixed set of parameters. The key assumptions and consequences of the model can be tested experimentally; further, it provides the foundation for a unified theory of NMDA receptor-dependent synaptic pl...

In this work, we introduce an Interactive Parts (IP) model as an alternative to Hidden Markov Models (HMMs). We tested both models on a database of on-line cursive script. We show that implementations of HMMs and the IP model, in which all letters are assumed to have the same average width, give comparable results.

We introduce an architecture for object segmentation/recognition that overcomes some limitations of classical neural networks by utilizing contextual information. An important characteristic of our model is that recognition is treated as a process of discovering a pattern rather than a one-time comparison between a pattern and a stored template. Ou...

It is generally accepted that some of the problems and ambi- guities at the low level of processing can not be resolved without taking into account contextual expectations. However, in most recognition sys- tems in use today, preprocessing is done using only bottom-up informa- tion. In this work we present a working system that is inspired by human...

The receptive fields for simple cells in visual cortex show a strong preference for edges of a particular orientation and display adjacent excitatory and inhibitory subfields. These subfields are projections from ON-center and OFF-center lateral geniculate nucleus cells, respectively. Here we present a single-cell model using ON and OFF channels, a...

Visual cortical simple cells show a strong preference for edges of a particular orientation[Hubel and Wiesel, 1962]. The receptive field of the cortical cell shows adjacent excitatory and inhibitory subfields, which are projections from ON-center and OFF-center LGN cells, respectively[Reid and Alonso, 1995]. Here we present a single cell model usin...

We study several statistically and biologically motivated learning rules using the same visual environment and neuronal architecture. This allows us to concentrate on the feature extraction and neuronal coding properties of these rules. We find that the quadratic form of the BCM rule behaves in a manner similar to a kurtosis maximization rule when...

Receptive fields in the visual cortex can be altered by changing the visual environment, as has been shown many times in deprivation experiments. In this paper we simulate this set of experiments using two di#erent models of cortical plasticity, BCM and PCA. The visual environment used is composed of natural images for open eye and of noise for clo...

Most simple and complex cells in the cat striate cortex are both orientation and direction selective. In this article we use single-cell learning rules to develop both orientation and direction selectivity in a natural scene environment. We show that a simple principal component analysis rule is inadequate for developing direction selectivity, but...

In the visual cortex of the cat and ferret, it is established that maturation of orientation selectivity is shaped by experience-dependent plasticity. However, recent experiments indicate that orientation maps are remarkably stable and experience-independent. We present a model to account for these seemingly paradoxical results. In this model, a sc...

In the visual cortex of the cat and ferret, it is established that orientation selectivity is the consequence of experience-dependent plasticity. Recent experiments, however, indicate that the layout of orientation maps is remarkably stable and experienceindependent. We present a model to account for these seemingly paradoxical results. In this mod...

In this work we re-analyze the objective function formulation of the BCM theory of visual cortical plasticity and introduce a new formalism that permits analysis of a laterally connected network of nonlinear neurons. This formulation provides a powerful method for the fixed point analysis and also the explicit calculation of these points in various...

A two-eye visual environment is used in training a network of BCM neurons. We study the effect of misalignment between the synaptic density functions from the two eyes, on the formation of orientation selectivity and ocular dominance in a lateral inhibition network. The visual environment we use is composed of natural images. We show that for the B...

In this paper, we present an objective function formulation of the BCM theory of visual cortical plasticity that permits us to demonstrate the connection between the unsupervised BCM learning procedure and various statistical methods, in particular, that of Projection Pursuit.

A short account is given of the BCM theory of synaptic plasticity: assumptions, consequences, comparison with experiment and statistical properties. In addition a framework for comparison with other theoretical ideas is presented. 1 Introduction Because of its great complexity, visual cortex would not seem to be an auspicious region of the brain to...

We introduce a new method for obtaining the fixed points for neurons that follow the BCM learning rule. The new formalism, which is based on the objective function formulation, permits analysis of a laterally connected network of nonlinear neurons and allows explicit calculation of the fixed points under various network conditions. We show that the...