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A conventional weight in an artificial neural network has a single trainable real value and produces a linear relationship between the weight input and the weight output. A real synaptic cleft is also trainable but provides a more complex relationship. It is obvious to wonder if adding extra complexity to the conventional weight response would lead to more capable networks. This work describes a weight...
It is expensive to simulate large-scale neural networks on hardware while ensuring a high resemblance to the original neurons' behavior. This paper introduces a novel technique to facilitate digital implementation and computer simulation of neuron models that contain an exponential term. This technique is applied to a biologically realistic neuron model called Adaptive Exponential integrated and fire...
Astrocyte as one of the brain cells controls synaptic activity between neurons by providing feedback to neurons. A novel digital hardware is proposed for neuron-synapse-astrocyte network based on the biological Adaptive Exponential (AdEx) neuron and Postnov astrocyte cell model. The network can be used for implementation of large scale spiking neural networks. Synthesis of the designed circuits shows...
The selection of parameters is one of the most important tasks in the training of a neural network. The choice of activation and loss functions is particularly relevant as the formulation of training procedures strongly depends on the pairing of these functions. However, the very few works on the effect of different combinations of these functions do not present a comprehensive experimental study...
In the field of deep neural networks, several generative methods have been proposed to address the challenges from generative and discriminative tasks, e.g., natural language process, image caption and image generation. In this paper, a conditional recurrent variational autoencoder is proposed for multi-digit image synthesis. This model is capable of generating multi-digit images from the given number...
To generate reliable forecasts, we need good estimates of both the current system state and the model parameters. Numerical weather prediction (NWP) uses atmospheric general circulation models (AGCMs) to predict weather based on current weather conditions. The process of entering observation data into mathematical model to generate the accurate initial conditions is called data assimilation (DA)....
Echo State Networks ESNs are specific kind of recurrent networks providing a black box modeling of dynamic non-linear problems. Their architecture is distinguished by a randomly recurrent hidden infra-structure called dynamic reservoir. Coming up with an efficient reservoir structure depends mainly on selecting the right parameters including the number of neurons and connectivity rate within it. Despite...
Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling temporal and sequential data need to capture long-term dependencies on datasets and represent them in hidden layers with a powerful model to capture more information from inputs. For modeling long-term dependencies in a dataset, the gating mechanism concept can help RNNs remember and forget previous information. Representing...
The analysis and study of complex networks are crucial to a number of applications. Vertex centrality measures are an important analysis mechanism to uncover or rank important elements of a given network. However, these metrics have high space and time complexity, which is a severe problem in applications that typically involve large networks. We propose and study the use of neural learning algorithms...
In this paper, a decentralized adaptive neural network sliding mode control scheme is proposed for trajectory tracking control problem of reconfigurable manipulators based on data-based modeling. This method can be implemented to reconfigurable manipulators with different configurations and degrees of freedom without modifying any control parameters. Different from the previous works, the proposed...
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted...
Convolutional neural networks play an increasingly important role in computer vision tasks, especially in the field of visual object recognition. Many prominent models, such as Inception, Maxout, ResNet, and NIN, have been proposed to significantly improve recognition performance. Inspired from those models, we propose a novel module called self-adaptive module (SAM). SAM consists of four passes and...
The ability of neural networks to perform pattern recognition, classification and associative memory, is essential to applications such as image and speech recognition, natural language understanding, decision making etc. In spiking neural networks (SNNs), information is encoded as sparsely distributed train of spikes, which allows learning through the spike-timing dependent plasticity (STDP) property...
Homeostatic plasticity in mammalian central nervous system is considered to maintain activity in neuronal circuits within a functional range. In the absence of homeostatic plasticity neuronal activity is prone to be destabilized because correlation-based synaptic modification, Hebbian plasticity, induces positive feedback change. Several studies on homeostatic plasticity assumed the existence of a...
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging techniques are used to image the inner portion of human body for medical diagnosis. In this research work, retinal colour fundus images and MRI brain images noise level has been improved. Fundus Fluorescein Angiography (FFA) is the invasive based technique used to give high...
An ability to predict collisions is essential for current vehicles and autonomous robots. In this paper, an integrated collision predication system is proposed based on neural subsystems inspired from Lobula giant movement detector (LGMD) and directional selective neurons (DSNs) which focus on different part of the visual field separately. The two type of neurons found in the visual pathways of insects...
This study deals with the identification of the behavior of an individual in a group of marching locusts, as observed under laboratory conditions. In particular, the study focuses on the intermittent motion (walking initiation and pausing) of the locusts using Adaptive Neuro-Fuzzy Inference System (ANFIS). Several possible fuzzy rules were examined in a trial-and-error approach, before establishing...
In the computer-aided design of chemical processes the quality of a process design is highly dependent on the reliability of the process models. Models for the prediction of physical properties of mixtures of substances play a crucial role here. In recent years, models with better prediction accuracy have been developed, but these models contain implicit equations which slow down the evaluation of...
Oscillatory Neural Networks (ONN) are becoming a popular neuromorphic computing model owing to their efficient parallel processing capabilities. Hoppensteadt and Izhikevich proposed an ONN architecture resembling associative memory, with Phase-Locked Loop (PLL) circuits as neurons. Unfortunately, there are shortcomings in realizing such architectures due to the inefficiencies of CMOS based implementations...
Many works have attempted to characterize the complexity of classification problems by measures extracted from their learning datasets. These indexes provide indicatives of the inherent difficulty in solving a given classification problem. Although regression problems are equally frequent, there is a lack of studies in Machine Learning dedicated to understanding their complexity. This paper proposes...
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