Abstract: The neurogenic potential of many herbal extracts used
in Indian medicine is hitherto unknown. Extracts derived from
Clitoria ternatea Linn have been used in Indian Ayurvedic system of
medicine as an ingredient of “Medhya rasayana", consumed for
improving memory and longevity in humans and also in treatment of
various neurological disorders. Our earlier experimental studies with
oral intubation of Clitoria ternatea aqueous root extract (CTR) had
shown significant enhancement of learning and memory in postnatal
and young adult Wistar rats. The present study was designed to
elucidate the in vitro effects of 200ng/ml of CTR on proliferation,
differentiation and growth of anterior subventricular zone neural
stem cells (aSVZ NSC-s) derived from prenatal and postnatal rat
pups. Results show significant increase in proliferation and growth of
neurospheres and increase in the yield of differentiated neurons of
aSVZ neural precursor cells (aSVZNPC-s) at 7 days in vitro when
treated with 200ng/ml of CTR as compared to age matched control.
Results indicate that CTR has growth promoting neurogenic effect on
aSVZ neural stem cells and their survival similar to neurotrophic
factors like Survivin, Neuregulin 1, FGF-2, BDNF possibly the basis
for enhanced learning and memory.
Abstract: This paper presents the results of enhancing images from a left and right stereo pair in order to increase the resolution of a 3D representation of a scene generated from that same pair. A new neural network structure known as a Self Delaying Dynamic Network (SDN) has been used to perform the enhancement. The advantage of SDNs over existing techniques such as bicubic interpolation is their ability to cope with motion and noise effects. SDNs are used to generate two high resolution images, one based on frames taken from the left view of the subject, and one based on the frames from the right. This new high resolution stereo pair is then processed by a disparity map generator. The disparity map generated is compared to two other disparity maps generated from the same scene. The first is a map generated from an original high resolution stereo pair and the second is a map generated using a stereo pair which has been enhanced using bicubic interpolation. The maps generated using the SDN enhanced pairs match more closely the target maps. The addition of extra noise into the input images is less problematic for the SDN system which is still able to out perform bicubic interpolation.
Abstract: MicroRNAs are an important class of gene expression
regulators that are involved in many biological processes including
embryogenesis. miR-125b is a conserved microRNA that is enriched
in the nervous system. We have previously reported the function of
miR-125b in neuronal differentiation of human cell lines. We also
discovered the function of miR-125b in regulating p53 in human and
zebrafish. Here we further characterize the brain defects in zebrafish
embryos injected with morpholinos against miR-125b. Our data
confirm the essential role of miR-125b in brain morphogenesis
particularly in maintaining the balance between proliferation, cell
death and differentiation. We identified lunatic fringe (lfng) as an
additional target of miR-125b in human and zebrafish and suggest
that lfng may mediate the function of miR-125b in neurogenesis.
Together, this report reveals new insights into the function of miR-
125b during neural development of zebrafish.
Abstract: In this paper, a model of self-organizing spiking neural networks is introduced and applied to mobile robot environment representation and path planning problem. A network of spike-response-model neurons with a recurrent architecture is used to create robot-s internal representation from surrounding environment. The overall activity of network simulates a self-organizing system with unsupervised learning. A modified A* algorithm is used to find the best path using this internal representation between starting and goal points. This method can be used with good performance for both known and unknown environments.
Abstract: Data mining uses a variety of techniques each of which
is useful for some particular task. It is important to have a deep
understanding of each technique and be able to perform sophisticated
analysis. In this article we describe a tool built to simulate a variation
of the Kohonen network to perform unsupervised clustering and
support the entire data mining process up to results visualization. A
graphical representation helps the user to find out a strategy to
optimize classification by adding, moving or delete a neuron in order
to change the number of classes. The tool is able to automatically
suggest a strategy to optimize the number of classes optimization, but
also support both tree classifications and semi-lattice organizations of
the classes to give to the users the possibility of passing from one
class to the ones with which it has some aspects in common.
Examples of using tree and semi-lattice classifications are given to
illustrate advantages and problems. The tool is applied to classify
macroeconomic data that report the most developed countries- import
and export. It is possible to classify the countries based on their
economic behaviour and use the tool to characterize the commercial
behaviour of a country in a selected class from the analysis of
positive and negative features that contribute to classes formation.
Possible interrelationships between the classes and their meaning are
also discussed.
Abstract: Security has been an important issue and concern in the
smart home systems. Smart home networks consist of a wide range of
wired or wireless devices, there is possibility that illegal access to
some restricted data or devices may happen. Password-based
authentication is widely used to identify authorize users, because this
method is cheap, easy and quite accurate. In this paper, a neural
network is trained to store the passwords instead of using verification
table. This method is useful in solving security problems that
happened in some authentication system. The conventional way to
train the network using Backpropagation (BPN) requires a long
training time. Hence, a faster training algorithm, Resilient
Backpropagation (RPROP) is embedded to the MLPs Neural
Network to accelerate the training process. For the Data Part, 200
sets of UserID and Passwords were created and encoded into binary
as the input. The simulation had been carried out to evaluate the
performance for different number of hidden neurons and combination
of transfer functions. Mean Square Error (MSE), training time and
number of epochs are used to determine the network performance.
From the results obtained, using Tansig and Purelin in hidden and
output layer and 250 hidden neurons gave the better performance. As
a result, a password-based user authentication system for smart home
by using neural network had been developed successfully.
Abstract: Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematical modeling of satellite earth station power system which is required for simulating the system.Aswan is selected to be the site under consideration because it is a rich region with solar energy. The complete power system is simulated using MATLAB–SIMULINK.An artificial neural network (ANN) based model has been developed for the optimum operation of earth station power system. An ANN is trained using a back propagation with Levenberg–Marquardt algorithm. The best validation performance is obtained for minimum mean square error. The regression between the network output and the corresponding target is equal to 96% which means a high accuracy. Neural network controller architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the satellite earth station power system.
Abstract: Traditionally, VLSI implementations of spiking
neural nets have featured large neuron counts for fixed computations
or small exploratory, configurable nets. This paper presents the
system architecture of a large configurable neural net system
employing a dedicated mapping algorithm for projecting the targeted
biology-analog nets and dynamics onto the hardware with its
attendant constraints.
Abstract: The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.
Abstract: The competitive learning is an adaptive process in
which the neurons in a neural network gradually become sensitive to
different input pattern clusters. The basic idea behind the Kohonen-s
Self-Organizing Feature Maps (SOFM) is competitive learning.
SOFM can generate mappings from high-dimensional signal spaces
to lower dimensional topological structures. The main features of this
kind of mappings are topology preserving, feature mappings and
probability distribution approximation of input patterns. To overcome
some limitations of SOFM, e.g., a fixed number of neural units and a
topology of fixed dimensionality, Growing Self-Organizing Neural
Network (GSONN) can be used. GSONN can change its topological
structure during learning. It grows by learning and shrinks by
forgetting. To speed up the training and convergence, a new variant
of GSONN, twin growing cell structures (TGCS) is presented here.
This paper first gives an introduction to competitive learning, SOFM
and its variants. Then, we discuss some GSONN with fixed
dimensionality, which include growing cell structures, its variants
and the author-s model: TGCS. It is ended with some testing results
comparison and conclusions.
Abstract: Dynamic Causal Modeling (DCM) functional
Magnetic Resonance Imaging (fMRI) is a promising technique to
study the connectivity among brain regions and effects of stimuli
through modeling neuronal interactions from time-series
neuroimaging. The aim of this study is to study characteristics of a
mirror neuron system (MNS) in elderly group (age: 60-70 years old).
Twenty volunteers were MRI scanned with visual stimuli to study a
functional brain network. DCM was employed to determine the
mechanism of mirror neuron effects. The results revealed major
activated areas including precentral gyrus, inferior parietal lobule,
inferior occipital gyrus, and supplementary motor area. When visual
stimuli were presented, the feed-forward connectivity from visual
area to conjunction area was increased and forwarded to motor area.
Moreover, the connectivity from the conjunction areas to premotor
area was also increased. Such findings can be useful for future
diagnostic process for elderly with diseases such as Parkinson-s and
Alzheimer-s.
Abstract: For over a decade, the Pulse Coupled Neural Network
(PCNN) based algorithms have been successfully used in image
interpretation applications including image segmentation. There are
several versions of the PCNN based image segmentation methods,
and the segmentation accuracy of all of them is very sensitive to the
values of the network parameters. Most methods treat PCNN
parameters like linking coefficient and primary firing threshold as
global parameters, and determine them by trial-and-error. The
automatic determination of appropriate values for linking coefficient,
and primary firing threshold is a challenging problem and deserves
further research. This paper presents a method for obtaining global as
well as local values for the linking coefficient and the primary firing
threshold for neurons directly from the image statistics. Extensive
simulation results show that the proposed approach achieves
excellent segmentation accuracy comparable to the best accuracy
obtainable by trial-and-error for a variety of images.
Abstract: Space Vector Modulation (SVM) is an optimum Pulse Width Modulation (PWM) technique for an inverter used in a variable frequency drive applications. It is computationally rigorous and hence limits the inverter switching frequency. Increase in switching frequency can be achieved using Neural Network (NN) based SVM, implemented on application specific chips. This paper proposes a neural network based SVM technique for a Voltage Source Inverter (VSI). The network proposed is independent of switching frequency. Different architectures are investigated keeping the total number of neurons constant. The performance of the inverter is compared for various switching frequencies for different architectures of NN based SVM. From the results obtained, the network with minimum resource and appropriate word length is identified. The bit precision required for this application is identified. The network with 8-bit precision is implemented in the IC XCV 400 and the results are presented. The performance of NN based general purpose SVM with higher bit precision is discussed.
Abstract: This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.
Abstract: Bone marrow-derived stem cells have been widely
studied as an alternative source of stem cells. Mesenchymal stem
cells (MSCs) were mostly investigated and studies showed MSCs can
promote neurogenesis. Little is known about the non-mesenchymal
mononuclear cell fraction, which contains both hematopoietic and
nonhematopoietic cells, including monocytes and endothelial
progenitor cells. This study focused on unfractionated bone marrow
mononuclear cells (BMMCs), which remained 72 h after MSCs were
adhered to the culture plates. We showed that BMMC-conditioned
medium promoted morphological changes of human SH-SY5Y
neuroblastoma cells from an epithelial-like phenotype towards a
neuron-like phenotype as indicated by an increase in neurite
outgrowth, like those observed in retinoic acid (RA)-treated cells.
The result could be explained by the effects of trophic factors
released from BMMCs, as shown in the RT-PCR results that
BMMCs expressed nerve growth factor (NGF), brain-derived
neurotrophic factor (BDNF), and ciliary neurotrophic factor (CNTF).
Similar results on the cell proliferation rate were also observed
between RA-treated cells and cells cultured in BMMC-conditioned
medium, suggesting that cells creased proliferating and differentiated
into a neuronal phenotype. Using real-time RT-PCR, a significantly
increased expression of tyrosine hydroxylase (TH) mRNA in SHSY5Y
cells indicated that BMMC-conditioned medium induced
catecholaminergic identities in differentiated SH-SY5Y cells.
Abstract: This paper presents a critical study about the
application of Neural Networks to ion-exchange process. Ionexchange
is a complex non-linear process involving many factors
influencing the ions uptake mechanisms from the pregnant solution.
The following step includes the elution. Published data presents
empirical isotherm equations with definite shortcomings resulting in
unreliable predictions. Although Neural Network simulation
technique encounters a number of disadvantages including its “black
box", and a limited ability to explicitly identify possible causal
relationships, it has the advantage to implicitly handle complex
nonlinear relationships between dependent and independent
variables. In the present paper, the Neural Network model based on
the back-propagation algorithm Levenberg-Marquardt was developed
using a three layer approach with a tangent sigmoid transfer function
(tansig) at hidden layer with 11 neurons and linear transfer function
(purelin) at out layer. The above mentioned approach has been used
to test the effectiveness in simulating ion exchange processes. The
modeling results showed that there is an excellent agreement between
the experimental data and the predicted values of copper ions
removed from aqueous solutions.
Abstract: Clinically, chickens showed progressively developed
clinical signs represented by sever cyanosis of the comb and wattles
with hemorrhage at the shanks, depression, and ruffling feathers with
loss of appetite and high daily mortalities. The ducks showed severe
neurological signs as torticollus, seizures and inability to stand with
mild signs of diarrhea and depression. Grossly, chickens showed
hemorrhages and congestion in most of the organs particularly lung,
liver, spleen, trachea and kidney. The examined ducks showed
multiple petechial hemorrhages, multifocal hemorrhagic necrosis in
the pancreas, pulmonary edema, congestion and hemorrhage in
meninges and congestion in the skeletal muscles. Histopathology
revealed severe congestion and hemorrhages in most of the organs
particularly lung, liver and kidney. Microscopic erosive tracheitis,
sever pulmonary congestion and perivascular oedema and lymphogranulocytic
pneumonia were constant. The liver showed hepatocyts
necrosis and lympho-granulocytic infiltration. The kidney showed
renal tubular necrosis and diffuse congestion. Multifocal, neuronal
necrosis, hemorrhages, multifocal glial nodules, lympho- histiocytic
perivascular cuffing, and occasional neuronophagia were observed in
the cerebrum. Other organs showed moderate changes.
Abstract: The present study focuses on the discussion over the
parameter of Artificial Neural Network (ANN). Sensitivity analysis is
applied to assess the effect of the parameters of ANN on the prediction
of turbidity of raw water in the water treatment plant. The result shows
that transfer function of hidden layer is a critical parameter of ANN.
When the transfer function changes, the reliability of prediction of
water turbidity is greatly different. Moreover, the estimated water
turbidity is less sensitive to training times and learning velocity than
the number of neurons in the hidden layer. Therefore, it is important to
select an appropriate transfer function and suitable number of neurons
in the hidden layer in the process of parameter training and validation.
Abstract: A neuron can emit spikes in an irregular time basis and by averaging over a certain time window one would ignore a lot of information. It is known that in the context of fast information processing there is no sufficient time to sample an average firing rate of the spiking neurons. The present work shows that the spiking neurons are capable of computing the radial basis functions by storing the relevant information in the neurons' delays. One of the fundamental findings of the this research also is that when using overlapping receptive fields to encode the data patterns it increases the network-s clustering capacity. The clustering algorithm that is discussed here is interesting from computer science and neuroscience point of view as well as from a perspective.
Abstract: Functional Magnetic Resonance Imaging(fMRI) is a
noninvasive imaging technique that measures the hemodynamic
response related to neural activity in the human brain. Event-related
functional magnetic resonance imaging (efMRI) is a form of
functional Magnetic Resonance Imaging (fMRI) in which a series of
fMRI images are time-locked to a stimulus presentation and averaged
together over many trials. Again an event related potential (ERP) is a
measured brain response that is directly the result of a thought or
perception. Here the neuronal response of human visual cortex in
normal healthy patients have been studied. The patients were asked
to perform a visual three choice reaction task; from the relative
response of each patient corresponding neuronal activity in visual
cortex was imaged. The average number of neurons in the adult
human primary visual cortex, in each hemisphere has been estimated
at around 140 million. Statistical analysis of this experiment was
done with SPM5(Statistical Parametric Mapping version 5) software.
The result shows a robust design of imaging the neuronal activity of
human visual cortex.