Hley plo EARLY ONLINE RELEASE (2010)
Vered Kunik, Yasmine Meroz, Zach Solan, Ben Sandbank, Uri Weingart, David Horn
This is a provisional PDF of the author-produced electronic version of a manuscript that has been accepted for publication. Although this article has been peer-reviewed, it was posted immediately...
Data mining of enzymes using specific peptides (2009)
Weingart, Uri, Lavi, Yair, Horn, David
Abstract Background Predicting the function of a protein from its sequence is a long-standing challenge of bioinformatic research, typically addressed using either sequence-similarity or...
Data mining of protein families using common peptides. (2009)
Assaf Gottlieb, Uri Weingart, David Horn
Predicting the function of a protein from its sequence is typically addressed by either sequence-similarity or sequence motifs. Using the latter, we perform supervised motif extraction from protein...
It has recently been shown (Kunik et al., PLOS Comput Biol 2007;3(8):e167) that the occurrence of specific peptides (SPs) on sequences of enzymes allows for accurate EC classification of enzymes. We...
ARTICLE Communicated by Mikhail Tsodyks Memory Maintenance via Neuronal Regulation (2009)
Since their conception half a century ago, Hebbian cell assemblies have become a basic term in the neurosciences, and the idea that learning takes place through synaptic modifications has been...
Itai Sharon, Shani Tzahor, Shannon Williamson, Michael Shmoish, Dikla Man-aharonovich, Douglas B Rusch, ...
www.nature.com/ismej
Genomic DNA k-mer spectra: models and modalities (2009)
Chor, Benny, Horn, David, Goldman, Nick, Levy, Yaron, Massingham, Tim
Abstract Background The empirical frequencies of DNA k -mers in whole genome sequences provide an interesting perspective on genomic complexity, and the availability of large segments of genomic...
Dynamic quantum clustering: a method for visual exploration of structures in data (2009)
Weinstein, Marvin, Horn, David
A given set of data-points in some feature space may be associated with a Schrodinger equation whose potential is determined by the data. This is known to lead to good clustering solutions. Here we...
DNA breaks as triggers for antigenic variation in African trypanosomes (2009)
Alsford, Sam, Horn, David, Glover, Lucy
Abstract The DNA repair machinery has been co-opted for antigenic variation in African trypanosomes. New work directly demonstrates that a double-strand break initiates a switch in the expressed...
Common peptides shed light on evolution of Olfactory Receptors (2009)
Gottlieb, Assaf, Olender, Tsviya, Lancet, Doron, Horn, David
Abstract Background Olfactory Receptors (ORs) form the largest multigene family in vertebrates. Their evolution and their expansion in the vertebrate genomes was the subject of many studies. In this...
Benny Chor, David Horn, Yaron Levy, Nick Goldman, Tim Massingham, Benny Chor, ...
This Provisional PDF corresponds to the article as it appeared upon acceptance. Copyedited and fully formatted PDF and full text (HTML) versions will be made available soon. Genomic DNA k-mer...
Support Vector Clustering Asa Ben-Hur (2008)
Biowulf Technologies, David Horn, Hava T. Siegelmann, Vladimir Vapnik, Nello Critianini, John Shawe-taylor, ...
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
Support Vector Clustering Asa Ben-Hur (2008)
Biowulf Technologies, David Horn, Hava T. Siegelmann, Vladimir Vapnik, Nello Critianini, John Shawe-taylor, ...
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
Clustering Algorithms Optimizer: A Framework for Large Datasets (2008)
Roy Varshavsky, David Horn, Michal Linial
Abstract. Clustering algorithms are employed in many bioinformatics tasks, including categorization of protein sequences and analysis of gene-expression data. Although these algorithms are routinely...
BIOINFORMATICS Information Extraction Novel Unsupervised Feature Filtering of Biological Data (2008)
Roy Varshavsky, Assaf Gottlieb, Michal Linial, David Horn
Motivation: Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsuper-vised methods are scarce. Examples are using the variance of data...
Data mining of protein families using common peptides (2008)
Assaf Gottlieb, Uri Weingart, David Horn
Predicting the function of a protein from its sequence is typically addressed using sequence-similarity. Here we propose a motif-based approach, using supervised motif extraction from protein...
BIOINFORMATICS Novel Unsupervised Feature Filtering of Biological Data (2008)
Roy Varshavsky, Assaf Gottlieb, Michal Linial, David Horn
doi:10.1093/bioinformatics/btl214
Vol. 23 ISMB/ECCB 2007, pages i440–i449 BIOINFORMATICS doi:10.1093/bioinformatics/btm183 (2008)
Liat Segal, Michal Lapidot, Zach Solan, Yitzhak Pilpel, David Horn
Nucleotide variation of regulatory motifs may lead to distinct expression patterns
Clustering Algorithms Optimizer – A Framework for Large Datasets (2008)
Roy Varshavsky, David Horn, Michal Linial
Abstract. Clustering algorithms are employed in many bioinformatics tasks, including classification of protein sequences and analysis of gene-expression data. Although these algorithms are routinely...
Rich Syntax from a Raw Corpus: Unsupervised Does It (2008)
Shimon Edelman, Zach Solan, David Horn
We compare our model of unsupervised learning of linguistic structures, ADIOS [1], to some recent work in computational linguistics and in grammar theory. Our approach resembles the Construction...
Support Vector Clustering Asa Ben-Hur (2008)
Biowulf Technologies, David Horn, Hava T. Siegelmann, Vladimir Vapnik, Nello Critianini, John Shawe-taylor, ...
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
Roy Varshavsky, Assaf Gottlieb, Michal Linial, David Horn
Motivation: Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data...
Clustering Algorithms Optimizer: A Framework for Large Datasets (2008)
Roy Varshavsky, David Horn, Michal Linial
Abstract. Clustering algorithms are employed in many bioinformatics tasks, including categorization of protein sequences and analysis of gene-expression data. Although these algorithms are routinely...
www.elsevier.com/locate/physa (2008)
Physica A, Clusteringvia Hilbert Space, David Horn
We discuss novel clusteringmethods that are based on mappingdata points to a Hilbert space by means of a Gaussian kernel. The rst method, support vector clustering(SVC), searches for the smallest...
Syntactic Structures in Languages and Biology (2008)
Both natural languages and cell biology make use of one-dimensional encryption. Their investigation calls for syntactic deciphering of the text and semantic understanding of the resulting structures....
Dynamic Proximity of Spatio-Temporal Sequences (2008)
David Horn, Gideon Dror, Brigitte Quenet
Abstract—Recurrent networks can generate spatio-temporal neural sequences of very large cycles, having an apparent random behavior. Nonetheless a proximity measure between these sequences may be...
Can Dynamic Neural Filters Produce Pseudo-Random Sequences? (2008)
Abstract. Dynamic neural filters (DNFs) are recurrent networks of binary neurons. Under proper conditions of their synaptic matrix they are known to generate exponentially large cycles. We show that...
Memory Capacity of Balanced Networks (2008)
Yuval Aviel, David Horn, Moshe Abeles
We study the problem of memory capacity in balanced networks of spiking neurons. Associative memories are represented by either synfire chains (SFC) or Hebbian cell assemblies (HCA). Both can be...
Zach Solan, David Horn, Shimon Edelman
The distributional principle according to which morphemes that occur in identical contexts belong, in some sense, to the same category [1] has been advanced as a means for extracting syntactic...
Zach Solan, David Horn, Shimon Edelman
The distributional principle according to which morphemes that occur in identical contexts belong, in some sense, to the same category [1] has been advanced as a means for extracting syntactic...
Glover, Lucy, McCulloch, Richard, Horn, David
Genetic diversity in fungi and mammals is generated through mitotic double-strand break-repair (DSBR), typically involving homologous recombination (HR) or non-homologous end joining (NHEJ)....
Since its conception half a century ago, Hebbian learning has become a fundamental paradigm in the neurosciences. The idea that neurons that re together wire together has become fairly well...
David Horn, Remi Dubois, Brigitte Quenet, Gerard Dreyfus
We introduce the concept of a Minimal Neural Model allowing for the description of a given set of data. The example that we have in mind is that of the spatio-temporal olfaction coding found by Wehr...
Brigitte Quenet, Gérard Dreyfus, David Horn
The electrophysiological data recorded in the glomerular stage of the insect olfactory pathway show both a coherent global oscillating behavior of the neurons of this stage - carrier waveform? -, and...
Complex Dynamics of Neuronal Thresholds (2007)
We study an integrate and fire model of an adapting neuron. Using two dynamic thresholds we account for complex long term behavior of single neurons under periodic pulsed inputs. We find that the...
Neuronal Regulation vs Synaptic Unlearning in Memory Maintenance Mechanisms (2007)
David Horn, Nir Levy, Eytan Ruppin
Hebbian learning, the paradigm of memory formation, needs further mechanisms to guarantee creation and maintenance of a viable memory system. One such proposed mechanism is Hebbian unlearning, a...
Momentum reconstruction of particles in the forward muon trigger system of the ATLAS detector (2007)
Gideon Dror, Erez Etzion, David Horn
We devise a feed forward neural network which identifies the charge and momentum of muons in the forward trigger system of the ATLAS detector. We use second order learning methods to train the...
Rich Syntax from a Raw Corpus: Unsupervised Does It (2007)
Shimon Edelman, Zach Solan, David Horn
We compare our model of unsupervised learning of linguistic structures, ADIOS [1], to some recent work in computational linguistics and in grammar theory. Our approach resembles the Construction...
Zach Solan, David Horn, Shimon Edelman
The distributional principle according to which morphemes that occur in identical contexts belong, in some sense, to the same category [1] has been advanced as a means for extracting syntactic...
Zach Solan, David Horn, Shimon Edelman
We examined the role of fitness, commonly assumed without proof to be conferred by the mastery of language, in shaping the dynamics of language evolution. To that end, we introduced island migration...
Neuronal Regulation vs Synaptic Unlearning in Memory Maintenance Mechanisms (2007)
Hebbian learning, the paradigm of memory formation, needs further mechanisms to guarantee creation and maintenance of a viable memory system. One such additional mechanism is Hebbian unlearning, a...
Zach Solan, David Horn, Shimon Edelman
The distributional principle according to which morphemes that occur in identical contexts belong, in some sense, to the same category [1] has been advanced as a means for extracting syntactic...
Asa Ben-Hur Asa, Biowulf Technologies, David Horn, Hava T. Siegelmann, Vladimir Vapnik, Nello Critianini, ...
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
Support Vector Clustering Asa Ben-Hur (2007)
Biowulf Technologies, David Horn, Hava T. Siegelmann, Vladimir Vapnik, Nello Critianini, John Shawe-taylor, ...
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
Rich Syntax from a Raw Corpus: (2007)
Unsupervised Does It, Shimon Edelman, Zach Solan, David Horn
We compare our model of unsupervised learning of linguistic structures, ADIOS [1], to some recent work in computational linguistics and in grammar theory. Our approach resembles the Construction...
Functional Representation of Enzymes by Specific Peptides (2007)
Vered Kunik, Yasmine Meroz, Zach Solan, Ben Sandbank, Uri Weingart, Eytan Ruppin, ...
Predicting the function of a protein from its sequence is a long-standing goal of bioinformatic research. While sequence similarity is the most popular tool used for this purpose, sequence motifs may...
Nucleotide variation of regulatory motifs may lead to distinct expression patterns (2007)
Segal, Liat, Lapidot, Michal, Solan, Zach, Ruppin, Eytan, Pilpel, Yitzhak, Horn, David
Motivation: Current methodologies for the selection of putative transcription factor binding sites (TFBS) rely on various assumptions such as over-representation of motifs occurring on gene...
A multiplayer gaming system using bluetooth for Windows Mobile / (2007)
Final year project -- University of Leeds (School of Computing Studies), 2006/2007.
Gene Expression, Roy Varshavsky, Assaf Gottlieb, David Horn, Michal Linial
meeting the challenges of biological data
Glover, Lucy, Alsford, Sam, Beattie, Caroline, Horn, David
Eukaryotic chromosomes are capped with telomeres which allow complete chromosome replication and prevent the ends from being recognized by the repair machinery. The African trypanosome, Trypanosoma...
Glover, Lucy, Alsford, Sam, Beattie, Caroline, Horn, David
Eukaryotic chromosomes are capped with telomeres which allow complete chromosome replication and prevent the ends from being recognized by the repair machinery. The African trypanosome, Trypanosoma...
Unsupervised feature selection under perturbations: meeting the challenges of biological data (2007)
Varshavsky, Roy, Gottlieb, Assaf, Horn, David, Linial, Michal
Motivation: Feature selection methods aim to reduce the complexity of data and to uncover the most relevant biological variables. In reality, information in biological datasets is often incomplete as...
Glover, Lucy, Alsford, Sam, Beattie, Caroline, Horn, David
Eukaryotic chromosomes are capped with telomeres which allow complete chromosome replication and prevent the ends from being recognized by the repair machinery. The African trypanosome, Trypanosoma...
Glover, Lucy, Alsford, Sam, Beattie, Caroline, Horn, David
Eukaryotic chromosomes are capped with telomeres which allow complete chromosome replication and prevent the ends from being recognized by the repair machinery. The African trypanosome, Trypanosoma...
Orchard, Sandra, Apweiler, Rolf, Barkovich, Robert, Field, Dawn, Garavelli, John S., Horn, David, ...
The theme of the third annual Spring workshop of the HUPO-PSI was proteomics and beyond and its underlying goal was to reach beyond the boundaries of the proteomics community to interact with groups...
Novel Unsupervised Feature Filtering of Biological Data (2006)
Varshavsky, Roy, Gottlieb, Assaf, Linial, Michal, Horn, David
Motivation: Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data...
D: COMPACT: A Comparative Package for Clustering Assessment (2005)
Roy Varshavsky, Michal Linial, David Horn
Abstract. There exist numerous algorithms that cluster data-points from largescale genomic experiments such as sequencing, gene-expression and proteomics. Such algorithms may employ distinct...
Motif extraction and protein classification (2005)
Vered Kunik, Zach Solan, Shimon Edelman, David Horn
We introduce an unsupervised method for extracting meaningful motifs from biological sequence data. This de novo motif extraction (MEX) algorithm is data driven, finding motifs that are not...
In vitro neuronal networks are known to fire in Synchronized Bursting Events (SBEs), with characteristic temporal width of 100 ms. We treat these events as the principal data atoms of the network....
Learning Syntactic Constructions from Raw Corpora (2005)
Shimon Edelman, Zach Solan, David Horn, Eytan Ruppin
... a lexicon populated by units of various sizes, as envisaged by (Langacker, 1987). Constructions may be specified completely, as in the case of simple morphemes or idioms such as take it to the...
Zach Solan, David Horn, Shimon Edelman, Structured Graph
Consider a corpus of m sentences (sequences) of variable length, each expressed in terms of a lexicon of finite size N. The sentences in the corpus correspond to m different paths in a pseudograph (a...
Some Tests of an Unsupervised Model of Language Acquisition (2004)
Bo Pedersen And, Bo Pedersen, Shimon Edelman, Zach Solan, David Horn
We outline an unsupervised language acquisition algorithm and offer some psycholinguistic support for a model based on it. Our approach resembles the Construction Grammar in its general philosophy,...
Unsupervised language acquisition: syntax from plain corpus (2004)
David Horn, Zach Solan, Shimon Edelman
We describe results of a novel algorithm for grammar induction from a large corpus. The ADIOS (Automatic DIstillation of Structure) algorithm searches for significant patterns, chosen according to...
Some Tests of an Unsupervised Model of Language Acquisition (2004)
Bo Pedersen, Shimon Edelman, Zach Solan, David Horn
We outline an unsupervised language acquisition algorithm and offer some psycholinguistic support for a model based on it. Our approach resembles the Construction Grammar in its general philosophy,...
Unsupervised efficient learning and representation of language structure (2003)
Zach Solan, David Horn, Shimon Edelman
We describe a linguistic pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of corpus data. This is achieved by compactly coding recursively...
Unsupervised efficient learning and representation of language structure (2003)
Zach Solan, David Horn, Shimon Edelman
We describe a linguistic pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of corpus data. This is achieved by compactly coding recursively...
Modeling Neural Spatiotemporal Behavior (2003)
David Horn, Davidhorn A, Brigitte Quenet, Gideon Dror, Orit Kliper
We study some aspects of thedynami neural #lter (DNF), a recurrent network that produces spatiesdzzzxk sequencesi reactie to sets of constantinstan The biSSzzTdf motizzTdf for thi study came from...
The Dynamic Neural Filter: A Binary Model of Spatiotemporal Coding (2003)
We describe and discuss the properties of binary neural network that can serve as a dynamic neural filter...
Faculty Of Exact, Zach Solan, David Horn, Shimon Edelman
We describe a linguistic pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of corpus data. This is achieved by compactly coding recursively...
Synfire Waves in Small Balanced Networks (2003)
Yuval Aviel David, David Horn, Moshe Abeles
We study the problem of mixing rate and temporal codes in the same population at the same time. We use a balanced network, known to act well as a rate code model, and embed in it synfire chain...
Unsupervised Context Sensitive Language (2003)
Acquisition From Large, Zach Solan, David Horn, Shimon Edelman
We describe a pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of linguistic structures from a plain natural-language corpus. This paper addresses...
Novel clustering algorithm for microarray expression data in a truncated SVD space (2003)
Motivation: This paper introduces the application of a novel clustering method to microarray expression data. Its first stage involves compression of dimensions that can be achieved by applying SVD...
Fast X-ray and beamlet transforms for three-dimensional data (2002)
David L. Donoho, Ofer Levi, Raphy Coifman, David Horn, Peter Jones, Xiaoming Huo, ...
Three-dimensional volumetric data are becoming increasingly available in a wide range of scientific and technical disciplines. With the right tools, we can expect such data to yield valuable insights...
The method of quantum clustering (2001)
We propose a novel clustering method that is an extension of ideas inherent to scale-space clustering and support-vector clustering. Like the latter, it associates every data point with a vector in...
Support vector clustering (2001)
Asa Ben-hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
The method of quantum clustering (2001)
We propose a novel clustering method that is an extension of ideas inherent to scale-space clustering and support-vector clustering. Like the latter, it associates every data point with a vector in...
Distributed synchrony in a cell assembly of spiking neurons (2001)
Nir Levy, David Horn, Isaac Meilijson
We investigate the formation of a Hebbian cell assembly of spiking neurons, using a temporal synaptic learning curve that is based on recent experimental ndings. It includes potentiation for short...
A support vector method for clustering (2001)
Asa Ben-hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to define a sphere...
A Support Vector Method for Hierarchical Clustering (2001)
Asa Ben-hur, David Horn, Hava Siegelmann, Vladimir Vapnik
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to define a sphere...
The method of quantum clustering (2001)
We propose a novel clustering method that is an extension of ideas inherent to scale-space clustering and support-vector clustering. Like the latter, it associates every data point with a vector in...
A support vector method for clustering (2001)
Asa Ben-hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to define a sphere...
Temporal Coding In An Olfactory Oscillatory Model (2000)
Brigitte Quenet, David Horn, Gérard DREYFUS
We propose a model of the glomerular stage of the insect olfactory pathway that exhibits coding of inputs through spatio-temporal patterns of the type observed experimentally in locust. Making use of...
Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly (2000)
David Horn Nir, David Horn, Nir Levy, Isaac Meilijson
We investigate the behavior of a Hebbian cell assembly of spiking neurons formed via a temporal synaptic learning curve. This learning function is based on recent experimental ndings. It includes...
Distributed Synchrony in a Hebbian Cell Assembly of Spiking Neurons (2000)
Nir Levy, David Horn, Isaac Meilijson, Eytan Ruppin
We investigate the formation of a Hebbian cell assembly of spiking neurons, using a temporal synaptic learning curve that is based on recent experimental findings. It includes potentiation for short...
Associative memory in a multi-modular network (1999)
Recent imaging studies suggest that object knowledge is stored in the brain as a distributed network of many cortical areas. Motivated by these observations, we study a multi-modular associative...
Associative memory in a multi-modular network (1999)
Recent imaging studies suggest that object knowledge is stored in the brain as a distributed network of many cortical areas. Motivated by these observations, we study a multi-modular associative...
Clustering with Spiking Neurons (1999)
Irit Opher, David Horn, Brigitte Quenet
We present a novel neural method for data clustering using temporal segmentation of spiking neurons. Our clustering algorithm relies only on distances between data points. Each point is associated...
Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly (1999)
David Horn, Nir Levy, Isaac Meilijson, Eytan Ruppin
We investigate the behavior of a Hebbian cell assembly of spiking neurons formed via a temporal synaptic learning curve. This learning function is based on recent experimental findings. It includes...
Collective Excitation Phenomena and their Applications (1999)
Introduction Spiking neurons are highly non-linear oscillators. As such they display collective behavior that may have important calculational manifestations. Synchronization between the firing of...
Memory maintenance via neuronal regulation (1998)
Since their conception half a century ago Hebbian cell assemblies have become a basic term in the Neurosciences, and the idea that learning takes place through synaptic modifications has been...
Associative Memory in a Multi-modular Network (1998)
Nir Levy, David Horn, Eytan Ruppin
Recent imaging studies suggest that object knowledge is stored in the brain as a distributed network of many cortical areas. Motivated by these observations, we study a multi-modular associative...
Multimodular Networks and Semantic Memory Impairments (1998)
David Horn, Nir Levy, Eytan Ruppin
We present a multi-modular approach to neural modeling of associative memory. By segregating between intra-modular and inter-modular synaptic transmission, and subjecting the latter to non-linear...
Probability Density Estimation using Entropy Maximization (1998)
We propose a method for estimating probability density functions and conditional density functions by training on data produced by such distributions. The algorithm employs new stochastic variables...
Memory Maintenance Via Neuronal Regulation (1998)
Since their conception half a century ago Hebbian cell assemblies have become a basic term in the Neurosciences, and the idea that learning takes place through synaptic modifications has been...
Vertex Identification in High Energy Physics Experiments (1998)
Gideon Dror, Halina Abramowicz, David Horn
In High Energy Physics experiments one has to sort through a high flux of events, at a rate of tens of MHz, and select the few that are of interest. In making this decision one relies on the location...
Vertex Identification in High Energy Physics Experiments (1998)
Gideon Dror, Halina Abramowicz, David Horn
In High Energy Physics experiments one has to sort through a high flux of events, at a rate of tens of MHz, and select the few that are of interest. One of the key factors in making this decision is...
Fast Temporal Encoding and Decoding (1998)
With Spiking Neurons, David Horn, Sharon Lev
We propose a simple theoretical structure of interacting integrate and fire neurons that can handle fast information processing, and may account for the fact that only a few neuronal spikes suffice...
Erkennen und Lokalisieren von Objekten in komplexer Umgebung anhand von Videobildern / (1997)
Zugl.: Augsburg, Univ., Diplomarbeit, 1996.
An Orientation Selective Neural Network for Pattern Identification (1997)
Halina Abramowicz, David Horn, Ury Naftaly, Carmit Sahar--pikielny
We present an algorithm for identifying linear patterns on a twodimensional lattice based on the concept of an orientation selective cell, a concept borrowed from neurobiology of vision. Constructing...
Solitary Waves of Integrate and Fire Neural Fields (1997)
Arrays of interacting identical neurons can develop coherent firing patterns, such as moving stripes that have been suggested as possible explanations of hallucinatory phenomena. Other known...
Multi-modular Associative Memory (1997)
Nir Levy, David Horn, Eytan Ruppin
Motivated by the findings of modular structure in the association cortex, we study a multi-modular model of associative memory that can successfully store memory patterns with different levels of...
Neural Computation Methods And Applications - Summary Talk of the AI Session, AIHENP96 (1997)
this paper is one of dead channel recovery. The case at hand was that one of the channels in their STIC luminosity monitor stopped working. Since it could not be replaced for a lengthy period, until...
Optimal Ensemble Averaging of Neural Networks (1997)
Ury Naftaly, Nathan Intrator, David Horn
Based on an observation about the different effect of ensemble averaging on the bias and variance portion of the prediction error, we discuss training methodologies for ensembles of networks. We...
Fast Temporal Encoding and Decoding with Spiking Neurons (1997)
We propose a simple theoretical structure of interacting integrate and fire neurons that can handle fast information processing, and may account for the fact that only a few neuronal spikes suffice...
Optimal Ensemble Averaging of Neural Networks (1997)
Ury Naftaly Nathan, Nathan Intrator, David Horn
Based on an observation about the different effect of ensemble averaging on the bias and variance portion of the prediction error, we discuss training methodologies for ensembles of networks. We...
Neuronal-based synaptic compensation: A computational study in alzheimer's disease (1996)
In the framework of an associative memory model, we study the interplay between synaptic deletion and compensation, and memory deterioration, a clinical hallmark of Alzheimer's disease. Our...
Neuronal-Based Synaptic Compensation: A Computational Study in Alzheimer's Disease (1996)
In the framework of an associative memory model, we study the interplay between synaptic deletion and compensation, and memory deterioration, a clinical hallmark of Alzheimer's disease. Our...
An Orientation Selective Neural Network and its Application to Cosmic Muon Identification (1996)
Halina Abramowicz, David Horn, Ury Naftaly, Carmit Sahar--pikielny
We propose a novel method for identification of a linear pattern of pixels on a two-dimensional grid. Following principles employed by the visual cortex, we employ orientation selective neurons in a...
Pathogenesis of Schizophrenic Delusions and Hallucinations: A Neural Model (1996)
Ruppin, Eytan, Reggia, James A., Horn, David
We implement and study a computational model of Stevens' theory of the pathogenesis of schizophrenia. This theory hypothesizes that the onset of schizophrenia is associated with reactive synaptic...
Segmentation and Binding in Oscillatory Neural Systems (1995)
Segmentation and binding are cognitive operations which underlie the process of perception. They can be understood as taking place in the temporal domain, i.e. relying on features like simultaneity...
The Role of Inhibition in an Associative Memory Model of the Olfactory Bulb (1995)
Ofer Hendin, David Horn, Misha V. Tsodyks
The external plexiform layer is where the interactions between the mitral (excitatory) and granule (inhibitory) cells of the olfactory bulb (OB) take place. Two outstanding features of these...
Oscillatory Model of Short Term Memory (1992)
David Horn, Beverly Sackler, Marius Usher
We investigate a model in which excitatory neurons have dynamical thresholds which display both fatigue and potentiation. The fatigue property leads to oscillatory behavior. It is responsible for the...
Glover, Lucy, McCulloch, Richard, Horn, David
Genetic diversity in fungi and mammals is generated through mitotic double-strand break-repair (DSBR), typically involving homologous recombination (HR) or non-homologous end joining (NHEJ)....
A mechanism for cross-resistance to nifurtimox and benznidazole in trypanosomes
Wilkinson, Shane R., Taylor, Martin C., Horn, David, Kelly, John M., Cheeseman, Ian
Nifurtimox and benznidazole are the front-line drugs used to treat Chagas disease, the most important parasitic infection in the Americas. These agents function as prodrugs and must be activated...
Kawahara, Taemi, Siegel, T Nicolai, Ingram, Alexandra K, Alsford, Sam, Cross, George A M, Horn, David
Chromatin modification is important for virtually all aspects of DNA metabolism but little is known about the consequences of such modification in trypanosomatids, early branching protozoa of...
Associative Memory and Segmentation in an Oscillatory Neural Model of the Olfactory Bulb
Ofer Hendin, David Horn, Misha V. Tsodyks
We discuss the first few stages of olfactory processing in the framework of a compartmental neural network. Our model consists of inhibitory and excitatory formal neurons with dendrodendritic...
Common peptides shed light on evolution of Olfactory Receptors
Gottlieb, Assaf, Olender, Tsviya, Lancet, Doron, Horn, David
Genetic manipulation in African trypanosomes typically relies upon electroporation with chromosomal integration of DNA constructs by homologous recombination. Relatively little is known about...
DNA breaks as triggers for antigenic variation in African trypanosomes
Alsford, Sam, Horn, David, Glover, Lucy
Double-strand breaks initiate coat protein switching in African trypanosomes.
Genomic DNA k-mer spectra: models and modalities
Chor, Benny, Horn, David, Goldman, Nick, Levy, Yaron, Massingham, Tim
Tetrapods, unlike other organisms, have multimodal spectra of k-mers in their genomes