scholarly journals A clustering neural network model of insect olfaction

2017 ◽  
Author(s):  
Cengiz Pehlevan ◽  
Alexander Genkin ◽  
Dmitri B. Chklovskii
Keyword(s):  
Supervised Classification ◽  
Clustering Algorithm ◽  
Inhibitory Interneuron ◽  
Small Population ◽  
Projection Neurons ◽  
Cluster Centroid ◽  
Insect Olfaction ◽  
Online Clustering ◽  
Computational Purpose ◽  
Dense Representation

AbstractA key step in insect olfaction is the transformation of a dense representation of odors in a small population of neurons - projection neurons (PNs) of the antennal lobe - into a sparse representation in a much larger population of neurons -Kenyon cells (KCs) of the mushroom body. What computational purpose does this transformation serve? We propose that the PN-KC network implements an online clustering algorithm which we derive from the k-means cost function. The vector of PN-KC synaptic weights converging onto a given KC represents the corresponding cluster centroid. KC activities represent attribution indices, i.e. the degree to which a given odor presentation is attributed to each cluster. Remarkably, such clustering view of the PN-KC circuit naturally accounts for several of its salient features. First, attribution indices are nonnegative thus rationalizing rectification in KCs. Second, the constraint on the total sum of attribution indices for each presentation is enforced by a Lagrange multiplier identified with the activity of a single inhibitory interneuron reciprocally connected with KCs. Third, the soft-clustering version of our algorithm reproduces observed sparsity and overcompleteness of the KC representation which may optimize supervised classification downstream.

Recognition of Online Handwritten Bangla Characters Using Supervised and Unsupervised Learning Approaches

2020 ◽  
Vol 10 (3) ◽  
pp. 18-30
Author(s):  
Prosenjit Mukherjee ◽  
Shibaprasad Sen ◽  
Kaushik Roy ◽  
Ram Sarkar
Keyword(s):  
Character Recognition ◽  
Supervised Classification ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Recognition Accuracy ◽  
Learning Approaches ◽  
Current Experiment ◽  
Unsupervised Approach ◽  
Mean Structure ◽  
Euclidian Distances

This paper explores the domain of online handwritten Bangla character recognition by stroke-based approach. The component strokes of a character sample are recognized firstly and then characters are constructed from the recognized strokes. In the current experiment, strokes are recognized by both supervised and unsupervised approaches. To estimate the features, images of all the component strokes are superimposed. A mean structure has been generated from this superimposed image. Euclidian distances between pixel points of a stroke sample and mean stroke structure are considered as features. For unsupervised approach, K-means clustering algorithm has been used whereas six popular classifiers have been used for supervised approach. The proposed feature vector has been evaluated on 10,000-character database and achieved 90.69% and 97.22% stroke recognition accuracy in unsupervised (using K-means clustering) and supervised way (using MLP [multilayer perceptron] classifier). This paper also discusses about merit and demerits of unsupervised and supervised classification approaches.

scholarly journals Segmentation of image based on k-means and modified subtractive clustering

2021 ◽  
Vol 22 (3) ◽  
pp. 1396
Author(s):  
Simon Tongbram ◽  
Benjamin A. Shimray ◽  
Loitongbam Surajkumar Singh
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Medical Science ◽  
Cluster Center ◽  
Subtractive Clustering ◽  
Cluster Centroid ◽  
Segmentation Methods ◽  
Segmentation Approach ◽  
Data Points

Image segmentation has widespread applications in medical science, for example, classification of different tissues, identification of tumors, estimation of tumor size, surgery planning, and atlas matching. Clustering is a widely implemented unsupervised technique used for image segmentation mainly because of its simplicity and fast computation. However, the quality and efficiency of clustering-based segmentation is highly depended on the initial value of the cluster centroid. In this paper, a new hybrid segmentation approach based on k-means clustering and modified subtractive clustering is proposed. K-means clustering is a very efficient and powerful algorithm but it requires initialization of cluster centroid. And, the consistency of the clustering outcomes of k-means algorithm depends on the initial selection of the cluster center. To overcome this drawback, a modified subtractive clustering algorithm based on distance relations between cluster centers and data points is proposed which finds a more accurate cluster centers compared to the conventional subtractive clustering. These cluster centroids obtained from the modified subtractive clustering are used in k-means algorithm for segmentation of the image. The proposed method is compared with other existing conventional segmentation methods by using several synthetic and real images and experimental finding validates the superiority of the proposed method.

scholarly journals “Modulation of apoptosis controls inhibitory interneuron number in the cortex”

2017 ◽  
Author(s):  
Myrto Denaxa ◽  
Guilherme Neves ◽  
Adam Rabinowitz ◽  
Sarah Kemlo ◽  
Petros Liodis ◽  
...  
Keyword(s):  
Physiological State ◽  
Inhibitory Interneuron ◽  
Projection Neurons ◽  
Activity Levels ◽  
Cortical Interneurons ◽  
A Cell ◽  
Cell Type Specific ◽  
Excitatory Projection ◽  
Cortical Excitation ◽  
The Right

AbstractCortical networks are composed of excitatory projection neurons and inhibitory interneurons. Finding the right balance between the two is important for controlling overall cortical excitation and network dynamics. However, it is unclear how the correct number of cortical interneurons (CIs) is established in the mammalian forebrain. CIs are generated in excess from basal forebrain progenitors and their final numbers are adjusted via an intrinsically determined program of apoptosis that takes place during an early postnatal window. Here, we provide evidence that the extent of CI apoptosis during this critical period is plastic, cell type specific and can be reduced in a cell autonomous manner by acute increases in neuronal activity. We propose that the physiological state of the emerging neural network controls the activity levels of local CIs to modulate their numbers in a homeostatic manner.

scholarly journals Neocortical Projection Neurons Instruct Inhibitory Interneuron Circuit Development in a Lineage-Dependent Manner

Neuron ◽  
2019 ◽  
Vol 102 (5) ◽  
pp. 960-975.e6 ◽  
Author(s):  
Jason C. Wester ◽  
Vivek Mahadevan ◽  
Christopher T. Rhodes ◽  
Daniela Calvigioni ◽  
Sanan Venkatesh ◽  
...  
Keyword(s):  
Inhibitory Interneuron ◽  
Projection Neurons ◽  
Dependent Manner ◽  
Circuit Development

A Text Categorization Method Based on SVM and Improved K-Means

2013 ◽  
Vol 427-429 ◽  
pp. 2449-2453
Author(s):  
Rong Ze Xia ◽  
Yan Jia ◽  
Hu Li
Keyword(s):  
Support Vector Machine ◽  
Vector Space ◽  
High Performance ◽  
Supervised Classification ◽  
Text Categorization ◽  
Clustering Algorithm ◽  
Vector Space Model ◽  
Classification Method ◽  
Support Vector ◽  
Space Model

Traditional supervised classification method such as support vector machine (SVM) could achieve high performance in text categorization. However, we should first hand-labeled the samples before classifying. Its a time-consuming task. Unsupervised method such as k-means could also be used for handling the text categorization problem. However, Traditional k-means could easily be affected by several isolated observations. In this paper, we proposed a new text categorization method. First we improved the traditional k-means clustering algorithm. The improved k-means is used for clustering vectors in our vector space model. After that, we use the SVM to categorize vectors which are preprocessed by improved k-means. The experiments show that our algorithm could out-perform the traditional SVM text categorization method.

scholarly journals Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imagery

2021 ◽  
pp. 1-14
Author(s):  
Rebecca L. Dell ◽  
Alison F. Banwell ◽  
Ian C. Willis ◽  
Neil S. Arnold ◽  
Anna Ruth W. Halberstadt ◽  
...  
Keyword(s):  
Supervised Classification ◽  
Clustering Algorithm ◽  
Google Earth ◽  
Expert Elicitation ◽  
Future Research ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Air Content

Abstract Surface meltwater is becoming increasingly widespread on Antarctic ice shelves. It is stored within surface ponds and streams, or within firn pore spaces, which may saturate to form slush. Slush can reduce firn air content, increasing an ice-shelf's vulnerability to break-up. To date, no study has mapped the changing extent of slush across ice shelves. Here, we use Google Earth Engine and Landsat 8 images from six ice shelves to generate training classes using a k-means clustering algorithm, which are used to train a random forest classifier to identify both slush and ponded water. Validation using expert elicitation gives accuracies of 84% and 82% for the ponded water and slush classes, respectively. Errors result from subjectivity in identifying the ponded water/slush boundary, and from inclusion of cloud and shadows. We apply our classifier to the Roi Baudouin Ice Shelf for the entire 2013–20 Landsat 8 record. On average, 64% of all surface meltwater is classified as slush and 36% as ponded water. Total meltwater areal extent is greatest between late January and mid-February. This highlights the importance of mapping slush when studying surface meltwater on ice shelves. Future research will apply the classifier across all Antarctic ice shelves.

High Pressure Air Compressor Condition Monitoring Using Unsupervised Pattern Classification Based on Projection Networks

2005 ◽  
Author(s):  
C. James Li ◽  
C. Jansuwan
Keyword(s):  
High Pressure ◽  
Supervised Classification ◽  
Clustering Algorithm ◽  
Unsupervised Classification ◽  
Single Class ◽  
Air Compressor ◽  
Classifier System ◽  
Discharge Valve ◽  
Air Compressors ◽  
Projection Network

High pressure air compressors (HPAC) are a high maintenance machine for they break down more often than expected and they serve critical roles. This study established the utility of an unsupervised pattern classifier system integrating a clustering algorithm based on DBSCAN and a dynamic classifier based on projection network to classify the condition of a 4-stage high pressure air compressor. The clustering algorithm is used to form clusters from un-labeled data and eliminate outliers. Subsequently, a system of projection networks is established to recognize all the significant clusters. The compressor data is consisted of pressures and temperatures at all four stages taken under various conditions including different baseline conditions, 3rd stage suction valve fault, 3rd stage discharge valve fault, and cylinder pitting and corrosion. The clustering algorithm was able to form clusters that each individually contains data mostly from a single class, and the projection network was able to differentiate these clusters and therefore classify the condition of the compressor correctly about 94% of the time. The ability of unsupervised classification does come with a price of lower classification accuracy. It was about 5% lower than what was accomplished by supervised classification.

Analysis of Incremental Cluster Validity for Big Data Applications

2018 ◽  
Vol 26 (Suppl. 2) ◽  
pp. 47-62 ◽  
Author(s):  
Omar A. Ibrahim ◽  
Yiqing Wang ◽  
James M. Keller
Keyword(s):  
Clustering Algorithm ◽  
Distance Measure ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
Internal Validity ◽  
Streaming Data ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Online Clustering ◽  
Validity Indices

Online clustering has attracted attention due to the explosion of ubiquitous continuous sensing. Streaming clustering algorithms need to look for new structures and adapt as the data evolves, such that outliers are detected, and that new emerging clusters are automatically formed. The performance of a streaming clustering algorithm needs to be monitored over time to understand the behavior of the streaming data in terms of new emerging clusters and number of outlier data points. Small datasets with 2 or 3 dimensions can be monitored by plotting the clustering results as data evolves. However, as the size and dimensions of streaming data increase, plotting the clustering result becomes unfeasible. Therefore, incremental internal Validity Indices (iCVIs) could be applied for monitoring the performance of an online clustering algorithm. In this paper, we study the internal incremental Davies-Bouldin (iDB) cluster validity index in the context of big streaming data analysis. Also, we study the effect of large number of samples on the values of the iCVI (iDB). Finally, we propose a way to project streaming data into a lower space for cases where the distance measure does not perform as expected in the high dimensional space.

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