Pub Date : 2016-05-24DOI: 10.4108/EAI.3-12-2015.2262441
A. Parker, H. Bridge, G. Coullon
The spatial distribution of signals from magnetic resonance imaging (MRI) using measures of Blood Oxygen Level Dependent (BOLD) activations presents a fundamental limit on the ability of MRI to resolve the neural signals from the brain. Here we show that the multiple samples of low-level BOLD activity comprise a form of neural “imaging dust” with distinct spatial characteristics. We apply the distance-dependent measurement of variance to spatial maps of BOLD signals to deliver a new approach to estimating the empirical point-spread function (PSF) of MRI. We show that these new estimates are similar to earlier measures of the PSF of high field 7-T imaging, but deliver the advantage that they are specific to each individual tested in a single scanning session. We explore various potential applications of this approach.
{"title":"Spatial scale of correlated signals in 7T BOLD imaging","authors":"A. Parker, H. Bridge, G. Coullon","doi":"10.4108/EAI.3-12-2015.2262441","DOIUrl":"https://doi.org/10.4108/EAI.3-12-2015.2262441","url":null,"abstract":"The spatial distribution of signals from magnetic resonance imaging (MRI) using measures of Blood Oxygen Level Dependent (BOLD) activations presents a fundamental limit on the ability of MRI to resolve the neural signals from the brain. Here we show that the multiple samples of low-level BOLD activity comprise a form of neural “imaging dust” with distinct spatial characteristics. We apply the distance-dependent measurement of variance to spatial maps of BOLD signals to deliver a new approach to estimating the empirical point-spread function (PSF) of MRI. We show that these new estimates are similar to earlier measures of the PSF of high field 7-T imaging, but deliver the advantage that they are specific to each individual tested in a single scanning session. We explore various potential applications of this approach.","PeriodicalId":415083,"journal":{"name":"International Conference on Bio-inspired Information and Communications Technologies","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127527467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-24DOI: 10.4108/EAI.3-12-2015.2262521
Shun Zhang, R. Guy, Juan Carlos del Alamo
In this work, we jointly measure the intracellular flow, traction stresses and free intracellular calcium level in physarum microplasmodium during amoeboid locomotion. Our mea- surements relates, for the first time, the intracellular mass transportation, the forces applied on the substrate and the signal of [Ca2+]i with high resolution in both time and space.
{"title":"Coordinations of Intracellular Flow, Calcium Signal and Cellular Contraction in Migrating Physarum","authors":"Shun Zhang, R. Guy, Juan Carlos del Alamo","doi":"10.4108/EAI.3-12-2015.2262521","DOIUrl":"https://doi.org/10.4108/EAI.3-12-2015.2262521","url":null,"abstract":"In this work, we jointly measure the intracellular flow, traction stresses and free intracellular calcium level in physarum microplasmodium during amoeboid locomotion. Our mea- surements relates, for the first time, the intracellular mass transportation, the forces applied on the substrate and the signal of [Ca2+]i with high resolution in both time and space.","PeriodicalId":415083,"journal":{"name":"International Conference on Bio-inspired Information and Communications Technologies","volume":"423 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123144844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-24DOI: 10.4108/EAI.3-12-2015.2262486
Dean A. Pospisil, Anitha Pasupathy, W. Bair
Hierarchical neural nets are currently the highest performing general purpose image recognition computer algorithms. Their design is loosely inspired by the neural architecture of the ventral visual pathway in the primate brain, which is believed to underlie the perception of form and the ability to recognize objects. The exact tuning of artificial neural units within an HNN, however, is not prescribed from known biology, but is trained using a performance-based learning algorithm. In evaluating whether HNNs are ripe for further bio-inspired performance improvements, it is of interest to test whether the response properties in the intermediate layers of the HNN approximate those of the ventral visual stream. We therefore characterized units within a popular HNN with a set of visual stimuli that has been employed by neurophysiologists to successfully characterize the shape-tuning properties of neurons in the intermediate visual cortical area V4 of the ventral stream. We found that the tuning and fits of a small but significant number of units in the HNN were strikingly similar to those of some V4 neurons for our simple set of test shapes. There tended to be more such units in the deeper layers of the HNN. We discuss implications of our results to the encoding of curvature in the primate brain and propose ways to further characterize V4-like shape tuning in HNNs.
{"title":"Comparing the brain's representation of shape to that of a deep convolutional neural network","authors":"Dean A. Pospisil, Anitha Pasupathy, W. Bair","doi":"10.4108/EAI.3-12-2015.2262486","DOIUrl":"https://doi.org/10.4108/EAI.3-12-2015.2262486","url":null,"abstract":"Hierarchical neural nets are currently the highest performing \u0000 \u0000general purpose image recognition computer algorithms. Their \u0000 \u0000design is loosely inspired by the neural architecture of the ventral \u0000 \u0000visual pathway in the primate brain, which is believed to underlie \u0000 \u0000the perception of form and the ability to recognize objects. The \u0000 \u0000exact tuning of artificial neural units within an HNN, however, is \u0000 \u0000not prescribed from known biology, but is trained using a \u0000 \u0000performance-based learning algorithm. In evaluating whether \u0000 \u0000HNNs are ripe for further bio-inspired performance \u0000 \u0000improvements, it is of interest to test whether the response \u0000 \u0000properties in the intermediate layers of the HNN approximate \u0000 \u0000those of the ventral visual stream. We therefore characterized \u0000 \u0000units within a popular HNN with a set of visual stimuli that has \u0000 \u0000been employed by neurophysiologists to successfully characterize \u0000 \u0000the shape-tuning properties of neurons in the intermediate visual \u0000 \u0000cortical area V4 of the ventral stream. We found that the tuning \u0000 \u0000and fits of a small but significant number of units in the HNN \u0000 \u0000were strikingly similar to those of some V4 neurons for our simple \u0000 \u0000set of test shapes. There tended to be more such units in the \u0000 \u0000deeper layers of the HNN. We discuss implications of our results \u0000 \u0000to the encoding of curvature in the primate brain and propose \u0000 \u0000ways to further characterize V4-like shape tuning in HNNs.","PeriodicalId":415083,"journal":{"name":"International Conference on Bio-inspired Information and Communications Technologies","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129596556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-24DOI: 10.4108/EAI.3-12-2015.2262350
Aydin Saribudak, Yiyu Dong, J. Hsieh, M. U. Uyar
In this paper, we analyze digitized images of Hematoxylin-Eosin (H&E) slides equipped with tumorous tissues from patient derived xenograft models to build our bio-inspired computation method, namely Personalized Relevance Parameterization of Spatial Randomness (PReP-SR). Applying spatial pattern analysis techniques of quadrat counts, kernel estimation and nearest neighbor functions to the images of the H&E samples, slide-specific features are extracted to examine the hypothesis that existence of dependency of nuclei positions possesses information of individual tumor characteristics. These features are then used as inputs to PReP-SR to compute tumor growth parameters for exponential-linear model. Differential evolution algorithms are developed for tumor growth parameter computations, where a candidate vector in a population consists of size selection indices for spatial evaluation and weight coefficients for spatial features and their correlations. Using leave-one-out-cross-validation method, we showed that, for a set of H&E slides from kidney cancer patient derived xenograft models, PReP-SR generates personalized model parameters with an average error rate of 13.58%. The promising results indicate that bio-inspired computation techniques may be useful to construct mathematical models with patient specific growth parameters in clinical systems.
{"title":"Bio-inspired Computation Approach for Tumor Growth with Spatial Randomness Analysis of Kidney Cancer Xenograft Pathology Slides","authors":"Aydin Saribudak, Yiyu Dong, J. Hsieh, M. U. Uyar","doi":"10.4108/EAI.3-12-2015.2262350","DOIUrl":"https://doi.org/10.4108/EAI.3-12-2015.2262350","url":null,"abstract":"In this paper, we analyze digitized images of Hematoxylin-Eosin \u0000 \u0000(H&E) slides equipped with tumorous tissues from patient derived xenograft models to build our bio-inspired computation method, namely Personalized Relevance Parameterization of Spatial Randomness (PReP-SR). Applying spatial pattern analysis techniques of quadrat counts, kernel estimation and nearest neighbor functions to the images of the H&E samples, slide-specific features are extracted to examine the hypothesis that existence of dependency of nuclei positions possesses information of individual tumor characteristics. \u0000 \u0000These features are then used as inputs to PReP-SR to compute tumor growth parameters for exponential-linear model. Differential evolution \u0000 \u0000algorithms are developed for tumor growth parameter computations, where a candidate vector in a population consists of size selection indices for spatial evaluation and weight coefficients for spatial features and their correlations. Using leave-one-out-cross-validation method, we showed that, for a set of H&E slides from kidney cancer patient derived xenograft models, PReP-SR generates \u0000 \u0000personalized model parameters with an average error rate of \u0000 \u000013.58%. The promising results indicate that bio-inspired computation techniques may be useful to construct mathematical models with patient specific growth parameters in clinical systems.","PeriodicalId":415083,"journal":{"name":"International Conference on Bio-inspired Information and Communications Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129712043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-24DOI: 10.4108/EAI.3-12-2015.2262481
Jonghyun Lee, Christina Oettmeier, H. Döbereiner
We describe a new growth pattern where globular moving fragments are formed instead of networks.
我们描述了一种新的增长模式，其中球状移动碎片形成，而不是网络。
{"title":"Growth pattern of Physarum polycephalum during starvation","authors":"Jonghyun Lee, Christina Oettmeier, H. Döbereiner","doi":"10.4108/EAI.3-12-2015.2262481","DOIUrl":"https://doi.org/10.4108/EAI.3-12-2015.2262481","url":null,"abstract":"We describe a new growth pattern where \u0000 \u0000globular moving fragments are formed instead of networks.","PeriodicalId":415083,"journal":{"name":"International Conference on Bio-inspired Information and Communications Technologies","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123834346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-24DOI: 10.4108/EAI.3-12-2015.2262531
M. Haupt, M. Hauser
The slime mold Physarum polycephalum forms an extended network used for transportation of protoplasm through the cell. Although the flow is always laminar, protoplasmic particles are efficiently distributed within the cell. To elucidate how mixing is achieved in such a microfluidic system, we performed PIV experiments and follow tracers in the flow fields. This work was presented at PhysNet 2015
{"title":"Efficient mixing of protoplasm in tubular network of the slime mould Physarum polycephalum","authors":"M. Haupt, M. Hauser","doi":"10.4108/EAI.3-12-2015.2262531","DOIUrl":"https://doi.org/10.4108/EAI.3-12-2015.2262531","url":null,"abstract":"The slime mold Physarum polycephalum forms an extended network used for transportation of protoplasm through the cell. Although the flow is always laminar, protoplasmic particles are efficiently distributed within the cell. To elucidate how mixing is achieved in such a microfluidic system, we performed PIV experiments and follow tracers in the flow fields. This work was presented at PhysNet 2015","PeriodicalId":415083,"journal":{"name":"International Conference on Bio-inspired Information and Communications Technologies","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114353281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-24DOI: 10.4108/EAI.3-12-2015.2262498
Xinhua Zhang, Garrett T. Kenyon
Sparse coding is a widely-used technique for learning an overcomplete basis set from unlabeled image data. We hypothesize that as the size of the image patch spanned by each basis vector increases, the resulting dictionary should encompass a broader range of spatial scales, including more features that better discriminate between object classes. Previous efforts to measure the effects of patch size on image classification performance were confounded by the difficulty of maintaining a given level of overcompleteness as the patch size is increased. Here, we employ a type of deconvolutional network in which overcompleteness is independent of patch size. Based on image classification results on the CIFAR10 database, we find that optimizing our deconvolutional network for sparse reconstruction leads to improved classification performance as a function of the number of training epochs. Different from previous reports, we find that enforcing a certain degree of sparsity improves classification performance. We also find that classification performance improves as both the number of learned features (dictionary size) and the size of the image patch spanned by each feature (patch size) are increased, ultimately the best published results for sparse autoencoders
{"title":"A Deconvolutional Strategy for Implementing Large Patch Sizes Supports Improved Image Classification","authors":"Xinhua Zhang, Garrett T. Kenyon","doi":"10.4108/EAI.3-12-2015.2262498","DOIUrl":"https://doi.org/10.4108/EAI.3-12-2015.2262498","url":null,"abstract":"Sparse coding is a widely-used technique for learning an overcomplete basis set from unlabeled image data. We hypothesize that as the size of the image patch spanned by each basis vector increases, the resulting dictionary should encompass a broader range of spatial scales, including more features that better discriminate between object classes. Previous efforts to measure the effects of patch size on image classification performance were confounded by the difficulty of maintaining a given level of overcompleteness as the patch size is increased. Here, we employ a type of deconvolutional network in which overcompleteness is independent of patch size. Based on image classification results on the CIFAR10 database, we find that optimizing our deconvolutional network for sparse reconstruction leads to improved classification performance as a function of the number of training epochs. Different from previous reports, we find that enforcing a certain degree of sparsity improves classification performance. We also find that classification performance improves as both the number of learned features (dictionary size) and the size of the image patch spanned by each feature (patch size) are increased, ultimately the best published results for sparse autoencoders","PeriodicalId":415083,"journal":{"name":"International Conference on Bio-inspired Information and Communications Technologies","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115252567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-24DOI: 10.4108/EAI.3-12-2015.2262423
Stewart Heitmann, B. Ermentrout
The majority of neurons in primary visual cortex respond preferentially to moving bars of light with a specific orientation and direction of motion. The directional selectivity of those neurons implies that their responses cannot be achieved with separate spatial and temporal neural processes. How cortical neurons achieve non-separable space-time responses is still a mystery. We present a mathematical model of visual cortex in which neurons are predisposed to traveling waves of activity in a given anatomical direction. Those neurons resonate vigorously with moving stimuli that have a similar space-time signature to the intrinsic traveling wave. Yet they are quiescent to stimulus motion in the opposite direction. The model demonstrates how direction-selectivity can arise from the spatiotemporal properties of intrinsic cortical activity without resort to explicit time delays.
{"title":"Propagating Waves as a Cortical Mechanism of Direction-Selectivity in V1 Motion Cells","authors":"Stewart Heitmann, B. Ermentrout","doi":"10.4108/EAI.3-12-2015.2262423","DOIUrl":"https://doi.org/10.4108/EAI.3-12-2015.2262423","url":null,"abstract":"The majority of neurons in primary visual cortex respond preferentially to moving bars of light with a specific orientation and direction of motion. The directional selectivity of those neurons implies that their responses cannot be achieved with separate spatial and temporal neural processes. How cortical neurons achieve non-separable space-time responses is still a mystery. We present a mathematical model of visual cortex in which neurons are predisposed to traveling waves of activity in a given anatomical direction. Those neurons resonate vigorously with moving stimuli that have a similar space-time signature to the intrinsic traveling wave. Yet they are quiescent to stimulus motion in the opposite direction. The model demonstrates how direction-selectivity can arise from the spatiotemporal properties of intrinsic cortical activity without resort to explicit time delays.","PeriodicalId":415083,"journal":{"name":"International Conference on Bio-inspired Information and Communications Technologies","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115028472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-24DOI: 10.4108/EAI.3-12-2015.2262394
W. Marwan
In this paper, we describe a Systems Genetics approach to the sporulation control network in Physarum polycephalum based on the time-resolved analysis of signaling events in individual plasmodial cells.
{"title":"A Systems Genetics Approach to the Sporulation Control Network in Physarum polycephalum","authors":"W. Marwan","doi":"10.4108/EAI.3-12-2015.2262394","DOIUrl":"https://doi.org/10.4108/EAI.3-12-2015.2262394","url":null,"abstract":"In this paper, we describe a Systems Genetics approach to the sporulation control network in Physarum polycephalum based on the time-resolved analysis of signaling events in individual plasmodial cells.","PeriodicalId":415083,"journal":{"name":"International Conference on Bio-inspired Information and Communications Technologies","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124696875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-24DOI: 10.4108/EAI.3-12-2015.2262474
Owen L. Lewis, R. Guy
{"title":"Investigation of peristaltic pumping as a cellular motility mechanism","authors":"Owen L. Lewis, R. Guy","doi":"10.4108/EAI.3-12-2015.2262474","DOIUrl":"https://doi.org/10.4108/EAI.3-12-2015.2262474","url":null,"abstract":"","PeriodicalId":415083,"journal":{"name":"International Conference on Bio-inspired Information and Communications Technologies","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129459912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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