Waldemar Swiercz | University of Colorado, Boulder (original) (raw)

Papers by Waldemar Swiercz

Research paper thumbnail of Progressive NKCC1-Dependent Neuronal Chloride Accumulation during Neonatal Seizures

Journal of Neuroscience, 2010

Seizures induce excitatory shifts in the reversal potential for GABA A receptor-mediated response... more Seizures induce excitatory shifts in the reversal potential for GABA A receptor-mediated responses, which may contribute to the intractability of electroencephalographic seizures and preclude the efficacy of widely-used GABAergic anticonvulsants such as phenobarbital. We now report that in intact hippocampi prepared from neonatal rats and transgenic mice expressing Clomeleon, recurrent seizures progressively increase the intracellular chloride concentration ([Cl − ] i ) assayed by Clomeleon imaging and invert the net effect of GABA A receptor activation from inhibition to excitation assayed by the frequency of action potentials and intracellular Ca 2+ transients. These changes correlate with increasing frequency of seizure-like events and reduction in phenobarbital efficacy. The Na + -K + -2Cl − (NKCC1) co-transporter blocker bumetanide inhibited seizure-induced neuronal Cl − accumulation and the consequent facilitation of recurrent seizures. Our results demonstrate a novel mechanism by which seizure activity leads to [Cl − ] i accumulation, thereby increasing the probability of subsequent seizures. This provides a potential mechanism for the early crescendo phase of neonatal seizures.

Research paper thumbnail of Spiking Neurons in Clustering of Diabetic Retinopathy Data

Hybrid Intelligent Systems, 2002

Research paper thumbnail of Validating Models of Epilepsy

Computational Neuroscience in Epilepsy, 2008

Research paper thumbnail of Network Control Mechanisms—Synaptogenesis and Epilepsy Development

Neuronal Networks in Brain Function, CNS Disorders, and Therapeutics, 2014

Research paper thumbnail of Development of Spontaneous Recurrent Seizures after Kainate-Induced Status Epilepticus

Journal of Neuroscience, 2009

Acquired epilepsy (i.e., after an insult to the brain) is often considered to be a progressive di... more Acquired epilepsy (i.e., after an insult to the brain) is often considered to be a progressive disorder, and the nature of this hypothetical progression remains controversial. Antiepileptic drug treatment necessarily confounds analyses of progressive changes in human patients with acquired epilepsy. Here, we describe experiments testing the hypothesis that development of acquired epilepsy begins as a continuous process of increased seizure frequency (i.e., proportional to probability of a spontaneous seizure) that ultimately plateaus. Using nearly continuous surface cortical and bilateral hippocampal recordings with radiotelemetry and semiautomated seizure detection, the frequency of electrographically recorded seizures (both convulsive and nonconvulsive) was analyzed quantitatively for approximately 100 d after kainate-induced status epilepticus in adult rats. The frequency of spontaneous recurrent seizures was not a step function of time (as implied by the "latent period"); rather, seizure frequency increased as a sigmoid function of time. The distribution of interseizure intervals was nonrandom, suggesting that seizure clusters (i.e., short interseizure intervals) obscured the early stages of progression, and may have contributed to the increase in seizure frequency. These data suggest that (1) the latent period is the first of many long interseizure intervals and a poor measure of the time frame of epileptogenesis, (2) epileptogenesis is a continuous process that extends much beyond the first spontaneous recurrent seizure, (3) uneven seizure clustering contributes to the variability in occurrence of epileptic seizures, and (4) the window for antiepileptogenic therapies aimed at suppressing acquired epilepsy probably extends well past the first clinical seizure.

Research paper thumbnail of Networks of spiking neurons in modeling and problem solving

Neurocomputing, 2004

Keywords: networks of spiking neurons, integrate-and-fire neuron model, diabetic retinopathy, clu... more Keywords: networks of spiking neurons, integrate-and-fire neuron model, diabetic retinopathy, clustering, brain modeling

Research paper thumbnail of Effects of Synaptic Depression and Recovery on Synchronous Network Activity

Journal of Clinical Neurophysiology, 2007

The output of an artificial neural network of spiking neurons linked by glutamatergic synapses su... more The output of an artificial neural network of spiking neurons linked by glutamatergic synapses subject to use-dependent depression was compared with physiologic data obtained from rat hippocampal area CA3 in vitro. The authors evaluated how network burst initiation and termination was affected by activity-dependent depression and recovery under a variety of experimental conditions including neuronal membrane depolarization, altered glutamate release probability, the strength of synaptic inhibition, and long-term potentiation and long-term depression of recurrent glutamatergic synapses. The results of computational experiments agreed with the in vitro data and support the idea that synaptic properties, including activity-dependent depression and recovery, play important roles in the timing and duration of spontaneous bursts of network activity. This validated network model is useful for experiments that are not feasible in vitro, and makes possible the investigation of two-dimensional aspects of burst propagation and termination.

Research paper thumbnail of Recognition of Partially Occluded and Rotated Images With a Network of Spiking Neurons

IEEE Transactions on Neural Networks, 2000

In this paper, we introduce a novel system for recognition of partially occluded and rotated imag... more In this paper, we introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The proposed network is shown to provide satisfactory predictive performance given that the number of the recognition neurons and synaptic connections are adjusted to the size of the input image. Comparison of synaptic plasticity activity rule (SAPR) and spike timing dependant plasticity rules, which are used to learn connections between the spiking neurons, indicates that the former gives better results and thus the SAPR rule is used. Test results show that the proposed network performs better than a recognition system based on support vector machines.

Research paper thumbnail of A New Synaptic Plasticity Rule for Networks of Spiking Neurons

IEEE Transactions on Neural Networks, 2006

In this paper, we describe a new Synaptic Plasticity Activity Rule (SAPR) developed for use in ne... more In this paper, we describe a new Synaptic Plasticity Activity Rule (SAPR) developed for use in networks of spiking neurons. Such networks can be used for simulations of physiological experiments as well as for other computations like image analysis. Most synaptic plasticity rules use artificially defined functions to modify synaptic connection strengths. In contrast, our rule makes use of the existing postsynaptic potential values to compute the value of adjustment. The network of spiking neurons we consider consists of excitatory and inhibitory neurons. Each neuron is implemented as an integrate-and-fire model that accurately mimics the behavior of biological neurons. To test performance of our new plasticity rule we designed a model of a biologically-inspired signal processing system, and used it for object detection in eye images of diabetic retinopathy patients, and lung images of cystic fibrosis patients. The results show that the network detects the edges of objects within an image, essentially segmenting it. Our ultimate goal, however, is not the development of an image segmentation tool that would be more efficient than nonbiological algorithms, but developing a physiologically correct neural network model that could be applied to a wide range of neurological experiments. We decided to validate the SAPR by using it in a network of spiking neurons for image segmentation because it is easy to visually assess the results. An important thing is that image segmentation is done in an entirely unsupervised way.

Research paper thumbnail of A Candidate Mechanism Underlying the Variance of Interictal Spike Propagation

The Journal of neuroscience : the official journal of the Society for Neuroscience, Jan 29, 2012

Synchronous activation of neural networks is an important physiological mechanism, and dysregulat... more Synchronous activation of neural networks is an important physiological mechanism, and dysregulation of synchrony forms the basis of epilepsy. We analyzed the propagation of synchronous activity through chronically epileptic neural networks. Electrocorticographic recordings from epileptic patients demonstrate remarkable variance in the pathways of propagation between sequential interictal spikes (IISs). Calcium imaging in chronically epileptic slice cultures demonstrates that pathway variance depends on the presence of GABAergic inhibition and that spike propagation becomes stereotyped following GABA receptor blockade. Computer modeling suggests that GABAergic quenching of local network activations leaves behind regions of refractory neurons, whose late recruitment forms the anatomical basis of variability during subsequent network activation. Targeted path scanning of slice cultures confirmed local activations, while ex vivo recordings of human epileptic tissue confirmed the depend...

Research paper thumbnail of Automated CT Scan Scores of Bronchiectasis and Air Trapping in Cystic Fibrosis

CHEST Journal, 2014

Computer analysis of high-resolution CT (HRCT) scans may improve the assessment of structural lun... more Computer analysis of high-resolution CT (HRCT) scans may improve the assessment of structural lung injury in children with cystic fibrosis (CF). The goal of this cross-sectional pilot study was to validate automated, observer-independent image analysis software to establish objective, simple criteria for bronchiectasis and air trapping. HRCT scans of the chest were performed in 35 children with CF and compared with scans from 12 disease control subjects. Automated image analysis software was developed to count visible airways on inspiratory images and to measure a low attenuation density (LAD) index on expiratory images. Among the children with CF, relationships among automated measures, Brody HRCT scanning scores, lung function, and sputum markers of inflammation were assessed. The number of total, central, and peripheral airways on inspiratory images and LAD (%) on expiratory images were significantly higher in children with CF compared with control subjects. Among subjects with CF, peripheral airway counts correlated strongly with Brody bronchiectasis scores by two raters (r=0.86, P<.0001; r=0.91, P<.0001), correlated negatively with lung function, and were positively associated with sputum free neutrophil elastase activity. LAD (%) correlated with Brody air trapping scores (r=0.83, P<.0001; r=0.69, P<.0001) but did not correlate with lung function or sputum inflammatory markers. Quantitative airway counts and LAD (%) on HRCT scans appear to be useful surrogates for bronchiectasis and air trapping in children with CF. Our automated methodology provides objective quantitative measures of bronchiectasis and air trapping that may serve as end points in CF clinical trials.

Research paper thumbnail of Semantic mapping of XML tags using inductive machine learning

In today's data-centric world many applications rely on data that comes from multitude of differe... more In today's data-centric world many applications rely on data that comes from multitude of different sources. To integrate that data two major operations are performed: finding semantic mapping between data sources, and transforming structure of the data sources. One of the well-established standards for storing and sharing structured and semantically described data is XML. This paper describes system called XMapper, which is used to generate semantic mapping between two XML sources that describe instances from the same domain. The described system is novel in two ways. It uses only stand-alone XML documents (without DTD or XML schema documents) to generate the mappings. It also utilizes machine learning to improve accuracy of such mappings for difficult domains. Several experiments that use artificial and real-life domains described by XML documents are used to test the proposed system. The results show that mappings generated by the XMapper are highly accurate for both types of XML sources. The generated mappings can be used by a data integration system to automatically merge content of XML data sources to provide unified information for a data processing application.

Research paper thumbnail of Progressive NKCC1-Dependent Neuronal Chloride Accumulation during Neonatal Seizures

The Journal of Neuroscience the Official Journal of the Society For Neuroscience, Sep 1, 2010

Seizures induce excitatory shifts in the reversal potential for GABA A receptor-mediated response... more Seizures induce excitatory shifts in the reversal potential for GABA A receptor-mediated responses, which may contribute to the intractability of electroencephalographic seizures and preclude the efficacy of widely-used GABAergic anticonvulsants such as phenobarbital. We now report that in intact hippocampi prepared from neonatal rats and transgenic mice expressing Clomeleon, recurrent seizures progressively increase the intracellular chloride concentration ([Cl − ] i ) assayed by Clomeleon imaging and invert the net effect of GABA A receptor activation from inhibition to excitation assayed by the frequency of action potentials and intracellular Ca 2+ transients. These changes correlate with increasing frequency of seizure-like events and reduction in phenobarbital efficacy. The Na + -K + -2Cl − (NKCC1) co-transporter blocker bumetanide inhibited seizure-induced neuronal Cl − accumulation and the consequent facilitation of recurrent seizures. Our results demonstrate a novel mechanism by which seizure activity leads to [Cl − ] i accumulation, thereby increasing the probability of subsequent seizures. This provides a potential mechanism for the early crescendo phase of neonatal seizures.

Research paper thumbnail of Progressive NKCC1-Dependent Neuronal Chloride Accumulation during Neonatal Seizures

Journal of Neuroscience, 2010

Seizures induce excitatory shifts in the reversal potential for GABA A receptor-mediated response... more Seizures induce excitatory shifts in the reversal potential for GABA A receptor-mediated responses, which may contribute to the intractability of electroencephalographic seizures and preclude the efficacy of widely-used GABAergic anticonvulsants such as phenobarbital. We now report that in intact hippocampi prepared from neonatal rats and transgenic mice expressing Clomeleon, recurrent seizures progressively increase the intracellular chloride concentration ([Cl − ] i ) assayed by Clomeleon imaging and invert the net effect of GABA A receptor activation from inhibition to excitation assayed by the frequency of action potentials and intracellular Ca 2+ transients. These changes correlate with increasing frequency of seizure-like events and reduction in phenobarbital efficacy. The Na + -K + -2Cl − (NKCC1) co-transporter blocker bumetanide inhibited seizure-induced neuronal Cl − accumulation and the consequent facilitation of recurrent seizures. Our results demonstrate a novel mechanism by which seizure activity leads to [Cl − ] i accumulation, thereby increasing the probability of subsequent seizures. This provides a potential mechanism for the early crescendo phase of neonatal seizures.

Research paper thumbnail of Spiking Neurons in Clustering of Diabetic Retinopathy Data

Hybrid Intelligent Systems, 2002

Research paper thumbnail of Validating Models of Epilepsy

Computational Neuroscience in Epilepsy, 2008

Research paper thumbnail of Network Control Mechanisms—Synaptogenesis and Epilepsy Development

Neuronal Networks in Brain Function, CNS Disorders, and Therapeutics, 2014

Research paper thumbnail of Development of Spontaneous Recurrent Seizures after Kainate-Induced Status Epilepticus

Journal of Neuroscience, 2009

Acquired epilepsy (i.e., after an insult to the brain) is often considered to be a progressive di... more Acquired epilepsy (i.e., after an insult to the brain) is often considered to be a progressive disorder, and the nature of this hypothetical progression remains controversial. Antiepileptic drug treatment necessarily confounds analyses of progressive changes in human patients with acquired epilepsy. Here, we describe experiments testing the hypothesis that development of acquired epilepsy begins as a continuous process of increased seizure frequency (i.e., proportional to probability of a spontaneous seizure) that ultimately plateaus. Using nearly continuous surface cortical and bilateral hippocampal recordings with radiotelemetry and semiautomated seizure detection, the frequency of electrographically recorded seizures (both convulsive and nonconvulsive) was analyzed quantitatively for approximately 100 d after kainate-induced status epilepticus in adult rats. The frequency of spontaneous recurrent seizures was not a step function of time (as implied by the "latent period"); rather, seizure frequency increased as a sigmoid function of time. The distribution of interseizure intervals was nonrandom, suggesting that seizure clusters (i.e., short interseizure intervals) obscured the early stages of progression, and may have contributed to the increase in seizure frequency. These data suggest that (1) the latent period is the first of many long interseizure intervals and a poor measure of the time frame of epileptogenesis, (2) epileptogenesis is a continuous process that extends much beyond the first spontaneous recurrent seizure, (3) uneven seizure clustering contributes to the variability in occurrence of epileptic seizures, and (4) the window for antiepileptogenic therapies aimed at suppressing acquired epilepsy probably extends well past the first clinical seizure.

Research paper thumbnail of Networks of spiking neurons in modeling and problem solving

Neurocomputing, 2004

Keywords: networks of spiking neurons, integrate-and-fire neuron model, diabetic retinopathy, clu... more Keywords: networks of spiking neurons, integrate-and-fire neuron model, diabetic retinopathy, clustering, brain modeling

Research paper thumbnail of Effects of Synaptic Depression and Recovery on Synchronous Network Activity

Journal of Clinical Neurophysiology, 2007

The output of an artificial neural network of spiking neurons linked by glutamatergic synapses su... more The output of an artificial neural network of spiking neurons linked by glutamatergic synapses subject to use-dependent depression was compared with physiologic data obtained from rat hippocampal area CA3 in vitro. The authors evaluated how network burst initiation and termination was affected by activity-dependent depression and recovery under a variety of experimental conditions including neuronal membrane depolarization, altered glutamate release probability, the strength of synaptic inhibition, and long-term potentiation and long-term depression of recurrent glutamatergic synapses. The results of computational experiments agreed with the in vitro data and support the idea that synaptic properties, including activity-dependent depression and recovery, play important roles in the timing and duration of spontaneous bursts of network activity. This validated network model is useful for experiments that are not feasible in vitro, and makes possible the investigation of two-dimensional aspects of burst propagation and termination.

Research paper thumbnail of Recognition of Partially Occluded and Rotated Images With a Network of Spiking Neurons

IEEE Transactions on Neural Networks, 2000

In this paper, we introduce a novel system for recognition of partially occluded and rotated imag... more In this paper, we introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The proposed network is shown to provide satisfactory predictive performance given that the number of the recognition neurons and synaptic connections are adjusted to the size of the input image. Comparison of synaptic plasticity activity rule (SAPR) and spike timing dependant plasticity rules, which are used to learn connections between the spiking neurons, indicates that the former gives better results and thus the SAPR rule is used. Test results show that the proposed network performs better than a recognition system based on support vector machines.

Research paper thumbnail of A New Synaptic Plasticity Rule for Networks of Spiking Neurons

IEEE Transactions on Neural Networks, 2006

In this paper, we describe a new Synaptic Plasticity Activity Rule (SAPR) developed for use in ne... more In this paper, we describe a new Synaptic Plasticity Activity Rule (SAPR) developed for use in networks of spiking neurons. Such networks can be used for simulations of physiological experiments as well as for other computations like image analysis. Most synaptic plasticity rules use artificially defined functions to modify synaptic connection strengths. In contrast, our rule makes use of the existing postsynaptic potential values to compute the value of adjustment. The network of spiking neurons we consider consists of excitatory and inhibitory neurons. Each neuron is implemented as an integrate-and-fire model that accurately mimics the behavior of biological neurons. To test performance of our new plasticity rule we designed a model of a biologically-inspired signal processing system, and used it for object detection in eye images of diabetic retinopathy patients, and lung images of cystic fibrosis patients. The results show that the network detects the edges of objects within an image, essentially segmenting it. Our ultimate goal, however, is not the development of an image segmentation tool that would be more efficient than nonbiological algorithms, but developing a physiologically correct neural network model that could be applied to a wide range of neurological experiments. We decided to validate the SAPR by using it in a network of spiking neurons for image segmentation because it is easy to visually assess the results. An important thing is that image segmentation is done in an entirely unsupervised way.

Research paper thumbnail of A Candidate Mechanism Underlying the Variance of Interictal Spike Propagation

The Journal of neuroscience : the official journal of the Society for Neuroscience, Jan 29, 2012

Synchronous activation of neural networks is an important physiological mechanism, and dysregulat... more Synchronous activation of neural networks is an important physiological mechanism, and dysregulation of synchrony forms the basis of epilepsy. We analyzed the propagation of synchronous activity through chronically epileptic neural networks. Electrocorticographic recordings from epileptic patients demonstrate remarkable variance in the pathways of propagation between sequential interictal spikes (IISs). Calcium imaging in chronically epileptic slice cultures demonstrates that pathway variance depends on the presence of GABAergic inhibition and that spike propagation becomes stereotyped following GABA receptor blockade. Computer modeling suggests that GABAergic quenching of local network activations leaves behind regions of refractory neurons, whose late recruitment forms the anatomical basis of variability during subsequent network activation. Targeted path scanning of slice cultures confirmed local activations, while ex vivo recordings of human epileptic tissue confirmed the depend...

Research paper thumbnail of Automated CT Scan Scores of Bronchiectasis and Air Trapping in Cystic Fibrosis

CHEST Journal, 2014

Computer analysis of high-resolution CT (HRCT) scans may improve the assessment of structural lun... more Computer analysis of high-resolution CT (HRCT) scans may improve the assessment of structural lung injury in children with cystic fibrosis (CF). The goal of this cross-sectional pilot study was to validate automated, observer-independent image analysis software to establish objective, simple criteria for bronchiectasis and air trapping. HRCT scans of the chest were performed in 35 children with CF and compared with scans from 12 disease control subjects. Automated image analysis software was developed to count visible airways on inspiratory images and to measure a low attenuation density (LAD) index on expiratory images. Among the children with CF, relationships among automated measures, Brody HRCT scanning scores, lung function, and sputum markers of inflammation were assessed. The number of total, central, and peripheral airways on inspiratory images and LAD (%) on expiratory images were significantly higher in children with CF compared with control subjects. Among subjects with CF, peripheral airway counts correlated strongly with Brody bronchiectasis scores by two raters (r=0.86, P<.0001; r=0.91, P<.0001), correlated negatively with lung function, and were positively associated with sputum free neutrophil elastase activity. LAD (%) correlated with Brody air trapping scores (r=0.83, P<.0001; r=0.69, P<.0001) but did not correlate with lung function or sputum inflammatory markers. Quantitative airway counts and LAD (%) on HRCT scans appear to be useful surrogates for bronchiectasis and air trapping in children with CF. Our automated methodology provides objective quantitative measures of bronchiectasis and air trapping that may serve as end points in CF clinical trials.

Research paper thumbnail of Semantic mapping of XML tags using inductive machine learning

In today's data-centric world many applications rely on data that comes from multitude of differe... more In today's data-centric world many applications rely on data that comes from multitude of different sources. To integrate that data two major operations are performed: finding semantic mapping between data sources, and transforming structure of the data sources. One of the well-established standards for storing and sharing structured and semantically described data is XML. This paper describes system called XMapper, which is used to generate semantic mapping between two XML sources that describe instances from the same domain. The described system is novel in two ways. It uses only stand-alone XML documents (without DTD or XML schema documents) to generate the mappings. It also utilizes machine learning to improve accuracy of such mappings for difficult domains. Several experiments that use artificial and real-life domains described by XML documents are used to test the proposed system. The results show that mappings generated by the XMapper are highly accurate for both types of XML sources. The generated mappings can be used by a data integration system to automatically merge content of XML data sources to provide unified information for a data processing application.

Research paper thumbnail of Progressive NKCC1-Dependent Neuronal Chloride Accumulation during Neonatal Seizures

The Journal of Neuroscience the Official Journal of the Society For Neuroscience, Sep 1, 2010

Seizures induce excitatory shifts in the reversal potential for GABA A receptor-mediated response... more Seizures induce excitatory shifts in the reversal potential for GABA A receptor-mediated responses, which may contribute to the intractability of electroencephalographic seizures and preclude the efficacy of widely-used GABAergic anticonvulsants such as phenobarbital. We now report that in intact hippocampi prepared from neonatal rats and transgenic mice expressing Clomeleon, recurrent seizures progressively increase the intracellular chloride concentration ([Cl − ] i ) assayed by Clomeleon imaging and invert the net effect of GABA A receptor activation from inhibition to excitation assayed by the frequency of action potentials and intracellular Ca 2+ transients. These changes correlate with increasing frequency of seizure-like events and reduction in phenobarbital efficacy. The Na + -K + -2Cl − (NKCC1) co-transporter blocker bumetanide inhibited seizure-induced neuronal Cl − accumulation and the consequent facilitation of recurrent seizures. Our results demonstrate a novel mechanism by which seizure activity leads to [Cl − ] i accumulation, thereby increasing the probability of subsequent seizures. This provides a potential mechanism for the early crescendo phase of neonatal seizures.