Abstract: Lateral Geniculate Nucleus (LGN) is the relay center
in the visual pathway as it receives most of the input information
from retinal ganglion cells (RGC) and sends to visual cortex. Low
threshold calcium currents (IT) at the membrane are the unique
indicator to characterize this firing functionality of the LGN neurons
gained by the RGC input. According to the LGN functional
requirements such as functional mapping of RGC to LGN, the
morphologies of the LGN neurons were developed. During the
neurological disorders like glaucoma, the mapping between RGC and
LGN is disconnected and hence stimulating LGN electrically using
deep brain electrodes can restore the functionalities of LGN. A
computational model was developed for simulating the LGN neurons
with three predominant morphologies each representing different
functional mapping of RGC to LGN. The firings of action potentials
at LGN neuron due to IT were characterized by varying the
stimulation parameters, morphological parameters and orientation. A
wide range of stimulation parameters (stimulus amplitude, duration
and frequency) represents the various strengths of the electrical
stimulation with different morphological parameters (soma size,
dendrites size and structure). The orientation (0-1800) of LGN
neuron with respect to the stimulating electrode represents the angle
at which the extracellular deep brain stimulation towards LGN
neuron is performed. A reduced dendrite structure was used in the
model using Bush–Sejnowski algorithm to decrease the
computational time while conserving its input resistance and total
surface area. The major finding is that an input potential of 0.4 V is
required to produce the action potential in the LGN neuron which is
placed at 100 μm distance from the electrode. From this study, it can
be concluded that the neuroprostheses under design would need to
consider the capability of inducing at least 0.4V to produce action
potentials in LGN.
Abstract: The main cause of Alzheimer disease (AD) was
believed to be mainly due to the accumulation of free radicals owing
to oxidative stress (OS) in brain tissue. The mechanism of the
neurotoxicity of Aluminum chloride (AlCl3) induced AD in
hippocampus Albino wister rat brain tissue, the curative & the
protective effects of Lipidium sativum group (LS) water extract were
assessed after 8 weeks by attenuated total reflection spectroscopy
ATR-IR and histologically by light microscope. ATR-IR results
revealed that the membrane phospholipid undergo free radical
attacks, mediated by AlCl3, primary affects the polyunsaturated fatty
acids indicated by the increased of the olefinic -C=CH sub-band area
around 3012 cm-1 from the curve fitting analysis. The narrowing in
the half band width (HBW) of the sνCH2 sub-band around 2852 cm-1
due to Al intoxication indicates the presence of trans form fatty acids
rather than gauch rotomer. The degradation of hydrocarbon chain to
shorter chain length, increasing in membrane fluidity, disorder, and
decreasing in lipid polarity in AlCl3 group indicated by the detected
changes in certain calculated area ratios compared to the control.
Administration of LS was greatly improved these parameters
compared to the AlCl3 group. Al influences the Aβ aggregation and
plaque formation, which in turn interferes to and disrupts the
membrane structure. The results also showed a marked increase in
the β-parallel and antiparallel structure, that characterize the Aβ
formation in Al-induced AD hippocampal brain tissue, indicated by
the detected increase in both amide I sub-bands around 1674, 1692
cm-1. This drastic increase in Aβ formation was greatly reduced in the
curative and protective groups compared to the AlCl3 group and
approached nearly the control values. These results supported too by
the light microscope. AlCl3 group showed significant marked
degenerative changes in hippocampal neurons. Most cells appeared
small, shrieked and deformed. Interestingly, the administration of LS
in curative and protective groups markedly decreases the amount of
degenerated cells compared to the non-treated group. In addition, the
intensity of congo red stained cells was decreased. Hippocampal
neurons looked more/or less similar to those of control. This study showed a promising therapeutic effect of Lipidium
sativum group (LS) on AD rat model that seriously overcome the
signs of oxidative stress on membrane lipid and restore the protein
misfolding.
Abstract: It has been known that a characteristic
Burst-Suppression (BS) pattern appears in EEG during the early
recovery period following Cardiac Arrest (CA). Here, to explore the
relationship between cortical and subcortical neural activities
underlying BS, extracellular activity in the parietal cortex and the
centromedian nucleus of the thalamus and extradural EEG were
recorded in a rodent CA model. During the BS, the cortical firing rate
is extraordinarily high, and that bursts in EEG correlate to dense spikes
in cortical neurons. Newly observed phenomena are that 1) thalamic
activity reemerges earlier than cortical activity following CA, and 2)
the correlation coefficient of cortical and thalamic activities rises
during BS period. These results would help elucidate the underlying
mechanism of brain recovery after CA injury.
Abstract: To explore how the brain may recognise objects in its
general,accurate and energy-efficient manner, this paper proposes the
use of a neuromorphic hardware system formed from a Dynamic
Video Sensor (DVS) silicon retina in concert with the SpiNNaker
real-time Spiking Neural Network (SNN) simulator. As a first step
in the exploration on this platform a recognition system for dynamic
hand postures is developed, enabling the study of the methods used
in the visual pathways of the brain. Inspired by the behaviours of
the primary visual cortex, Convolutional Neural Networks (CNNs)
are modelled using both linear perceptrons and spiking Leaky
Integrate-and-Fire (LIF) neurons.
In this study’s largest configuration using these approaches, a
network of 74,210 neurons and 15,216,512 synapses is created and
operated in real-time using 290 SpiNNaker processor cores in parallel
and with 93.0% accuracy. A smaller network using only 1/10th of the
resources is also created, again operating in real-time, and it is able
to recognise the postures with an accuracy of around 86.4% - only
6.6% lower than the much larger system. The recognition rate of the
smaller network developed on this neuromorphic system is sufficient
for a successful hand posture recognition system, and demonstrates
a much improved cost to performance trade-off in its approach.
Abstract: This paper presents an optimization method for
reducing the number of input channels and the complexity of the
feed-forward NARX neural network (NN) without compromising the
accuracy of the NN model. By utilizing the correlation analysis
method, the most significant regressors are selected to form the input
layer of the NN structure. An application of vehicle dynamic model
identification is also presented in this paper to demonstrate the
optimization technique and the optimal input layer structure and the
optimal number of neurons for the neural network is investigated.
Abstract: This work assesses the cortical and the sub-cortical
neural activity recorded from rodents using entropy and mutual
information based approaches to study how hypothermia affects neural
activity. By applying the multi-scale entropy and Shannon entropy, we
quantify the degree of the regularity embedded in the cortical and
sub-cortical neurons and characterize the dependency of entropy of
these regions on temperature. We study also the degree of the mutual
information on thalamocortical pathway depending on temperature.
The latter is most likely an indicator of coupling between these highly
connected structures in response to temperature manipulation leading
to arousal after global cerebral ischemia.
Abstract: This paper provides a quantitative measure of the
time-varying multiunit neuronal spiking activity using an entropy
based approach. To verify the status embedded in the neuronal activity
of a population of neurons, the discrete wavelet transform (DWT) is
used to isolate the inherent spiking activity of MUA. Due to the
de-correlating property of DWT, the spiking activity would be
preserved while reducing the non-spiking component. By evaluating
the entropy of the wavelet coefficients of the de-noised MUA, a
multiresolution Shannon entropy (MRSE) of the MUA signal is
developed. The proposed entropy was tested in the analysis of both
simulated noisy MUA and actual MUA recorded from cortex in rodent
model. Simulation and experimental results demonstrate that the
dynamics of a population can be quantified by using the proposed
entropy.
Abstract: Electroencephalogram (EEG) is a noninvasive
technique that registers signals originating from the firing of neurons
in the brain. The Emotiv EEG Neuroheadset is a consumer product
comprised of 14 EEG channels and was used to record the reactions
of the neurons within the brain to two forms of stimuli in 10
participants. These stimuli consisted of auditory and visual formats
that provided directions of ‘right’ or ‘left.’ Participants were
instructed to raise their right or left arm in accordance with the
instruction given. A scenario in OpenViBE was generated to both
stimulate the participants while recording their data. In OpenViBE,
the Graz Motor BCI Stimulator algorithm was configured to govern
the duration and number of visual stimuli. Utilizing EEGLAB under
the cross platform MATLAB®, the electrodes most stimulated during
the study were defined. Data outputs from EEGLAB were analyzed
using IBM SPSS Statistics® Version 20. This aided in determining
the electrodes to use in the development of a brain-machine interface
(BMI) using real-time EEG signals from the Emotiv EEG
Neuroheadset. Signal processing and feature extraction were
accomplished via the Simulink® signal processing toolbox. An
Arduino™ Duemilanove microcontroller was used to link the Emotiv
EEG Neuroheadset and the right and left Mecha TE™ Hands.
Abstract: Neurons in the nervous system communicate with
each other by producing electrical signals called spikes. To
investigate the physiological function of nervous system it is essential
to study the activity of neurons by detecting and sorting spikes in the
recorded signal. In this paper a method is proposed for considering
the spike sorting problem which is based on the nonlinear modeling
of spikes using exponential autoregressive model. The genetic
algorithm is utilized for model parameter estimation. In this regard
some selected model coefficients are used as features for sorting
purposes. For optimal selection of model coefficients, self-organizing
feature map is used. The results show that modeling of spikes with
nonlinear autoregressive model outperforms its linear counterpart.
Also the extracted features based on the coefficients of exponential
autoregressive model are better than wavelet based extracted features
and get more compact and well-separated clusters. In the case of
spikes different in small-scale structures where principal component
analysis fails to get separated clouds in the feature space, the
proposed method can obtain well-separated cluster which removes
the necessity of applying complex classifiers.
Abstract: In this work, neural networks methods MLP type were
applied to a database from an array of six sensors for the detection of
three toxic gases. The choice of the number of hidden layers and the
weight values are influential on the convergence of the learning
algorithm. We proposed, in this article, a mathematical formula to
determine the optimal number of hidden layers and good weight
values based on the method of back propagation of errors. The results
of this modeling have improved discrimination of these gases and
optimized the computation time. The model presented here has
proven to be an effective application for the fast identification of
toxic gases.
Abstract: In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.
Abstract: We have built universal central pattern generator (CPG) hardware by interconnecting Hodgkin-Huxley neurons with reciprocally inhibitory synapses. We investigate the dynamics of neuron oscillations as a function of the time delay between current steps applied to individual neurons. We demonstrate stimulus dependent switching between spiking polyrhythms and map the phase portraits of the neuron oscillations to reveal the basins of attraction of the system. We experimentally study the dependence of the attraction basins on the network parameters: The neuron response time and the strength of inhibitory connections.
Abstract: PH, temperature and time of extraction of each stage,
agitation speed and delay time between stages effect on efficiency of
zinc extraction from concentrate. In this research, efficiency of zinc
extraction was predicted as a function of mentioned variable by
artificial neural networks (ANN). ANN with different layer was
employed and the result show that the networks with 8 neurons in
hidden layer has good agreement with experimental data.
Abstract: This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate
crosstalk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still
lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds, since biological information is inherently included inside the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons.
Abstract: Learning the gradient of neuron's activity function
like the weight of links causes a new specification which is
flexibility. In flexible neural networks because of supervising and
controlling the operation of neurons, all the burden of the learning is
not dedicated to the weight of links, therefore in each period of
learning of each neuron, in fact the gradient of their activity function,
cooperate in order to achieve the goal of learning thus the number of
learning will be decreased considerably.
Furthermore, learning neurons parameters immunes them against
changing in their inputs and factors which cause such changing.
Likewise initial selecting of weights, type of activity function,
selecting the initial gradient of activity function and selecting a fixed
amount which is multiplied by gradient of error to calculate the
weight changes and gradient of activity function, has a direct affect
in convergence of network for learning.
Abstract: Retinal prostheses have been successful in eliciting
visual responses in implanted subjects. As these prostheses progress,
one of their major limitations is the need for increased resolution. As
an alternative to increasing the number of electrodes, virtual
electrodes may be used to increase the effective resolution of current
electrode arrays. This paper presents a virtual electrode technique
based upon time-offsets between stimuli. Two adjacent electrodes are
stimulated with identical pulses with too short of pulse widths to
activate a neuron, but one has a time offset of one pulse width. A
virtual electrode of twice the pulse width was then shown to appear in
the center, with a total width capable of activating a neuron. This can
be used in retinal implants by stimulating electrodes with pulse
widths short enough to not elicit responses in neurons, but with their
combined pulse width adequate to activate a neuron in between them.
Abstract: In this paper, we consider a two-neuron system with time-delayed connections between neurons. By analyzing the associated characteristic transcendental equation, its linear stability is investigated and Hopf bifurcation is demonstrated. Some explicit formulae for determining the stability and the direction of the Hopf bifurcation periodic solutions bifurcating from Hopf bifurcations are obtained by using the normal form theory and center manifold theory. Some numerical simulation results are given to support the theoretical predictions. Finally, main conclusions are given.
Abstract: .Hardware realization of a Neural Network (NN), to a large extent depends on the efficient implementation of a single neuron. FPGA-based reconfigurable computing architectures are suitable for hardware implementation of neural networks. FPGA realization of ANNs with a large number of neurons is still a challenging task. This paper discusses the issues involved in implementation of a multi-input neuron with linear/nonlinear excitation functions using FPGA. Implementation method with resource/speed tradeoff is proposed to handle signed decimal numbers. The VHDL coding developed is tested using Xilinx XC V50hq240 Chip. To improve the speed of operation a lookup table method is used. The problems involved in using a lookup table (LUT) for a nonlinear function is discussed. The percentage saving in resource and the improvement in speed with an LUT for a neuron is reported. An attempt is also made to derive a generalized formula for a multi-input neuron that facilitates to estimate approximately the total resource requirement and speed achievable for a given multilayer neural network. This facilitates the designer to choose the FPGA capacity for a given application. Using the proposed method of implementation a neural network based application, namely, a Space vector modulator for a vector-controlled drive is presented
Abstract: Statistical analysis of electrophysiological recordings
obtained under, e.g. tactile, stimulation frequently suggests participation
in the network dynamics of experimentally unobserved “hidden"
neurons. Such interneurons making synapses to experimentally
recorded neurons may strongly alter their dynamical responses to
the stimuli. We propose a mathematical method that formalizes this
possibility and provides an algorithm for inferring on the presence
and dynamics of hidden neurons based on fitting of the experimental
data to spike trains generated by the network model. The model
makes use of Integrate and Fire neurons “chemically" coupled
through exponentially decaying synaptic currents. We test the method
on simulated data and also provide an example of its application to
the experimental recording from the Dorsal Column Nuclei neurons
of the rat under tactile stimulation of a hind limb.
Abstract: Saturated hydraulic conductivity is one of the soil
hydraulic properties which is widely used in environmental studies
especially subsurface ground water. Since, its direct measurement is
time consuming and therefore costly, indirect methods such as
pedotransfer functions have been developed based on multiple linear
regression equations and neural networks model in order to estimate
saturated hydraulic conductivity from readily available soil
properties e.g. sand, silt, and clay contents, bulk density, and organic
matter. The objective of this study was to develop neural networks
(NNs) model to estimate saturated hydraulic conductivity from
available parameters such as sand and clay contents, bulk density,
van Genuchten retention model parameters (i.e. r
θ , α , and n) as well
as effective porosity. We used two methods to calculate effective
porosity: : (1) eff s FC φ =θ -θ , and (2) inf φ =θ -θ eff s , in which s
θ is
saturated water content, FC θ is water content retained at -33 kPa
matric potential, and inf θ is water content at the inflection point.
Total of 311 soil samples from the UNSODA database was divided
into three groups as 187 for the training, 62 for the validation (to
avoid over training), and 62 for the test of NNs model. A commercial
neural network toolbox of MATLAB software with a multi-layer
perceptron model and back propagation algorithm were used for the
training procedure. The statistical parameters such as correlation
coefficient (R2), and mean square error (MSE) were also used to
evaluate the developed NNs model. The best number of neurons in
the middle layer of NNs model for methods (1) and (2) were
calculated 44 and 6, respectively. The R2 and MSE values of the test
phase were determined for method (1), 0.94 and 0.0016, and for
method (2), 0.98 and 0.00065, respectively, which shows that method
(2) estimates saturated hydraulic conductivity better than method (1).