In this study, we propose a model of the dendritic structure of the neuron (referred to as a neural network - NN), which can be viewed as an extension of the models that are currently used in the description of the potential on the neuron's membrane. The proposed extensions augment the generic model and offer a fuller description of the neuron's nature. The common assumption being used in most of the previous models stating a single channel (forming component of the neuron's membrane) can be positioned in only one of the two states (permissive - open and non-permissive - closed), is now relaxed by allowing the channel to be positioned in more states (five or eight states). The relationship between these states is expressed in terms of Markov kinetic schemes. In the paper, we demonstrate that the new approach is more suitable for a larger number of applications than the conventional Hodgkin-Huxley model.The study, by providing the mathematical background of the new extended model, forms a significant step towards a hardware implementation of the biologically realistic neural network (NN) of this type. To reduce the number of components required in such implementation, we propose a new optimization technique that significantly reduces the computational complexity of a single neuron. (C) 2017 Nalecz Institute of Biocybemetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

The article presents novel idea of a hardware accelerated image processing algorithm for embedded systems. The system is based on the well known Fast Retina Keypoint (FREAK) local image description algorithm. The solution utilizes Field Programmable Gate Array (FPGA) as a flexible module that is used to implement hardware acceleration of a given part of the image processing algorithm. The approach presented in this paper is slightly different. Since we are using very fast FREAK descriptor it is not our purpose to implement full feature extraction algorithm in hardware but just its most time-consuming part which is brute force matcher based on the Hamming distance. Moreover our goal was to design very flexible system so that the feature detection and extraction algorithm can be replaced without any interruption in the hardware accelerated part.

Time synchronization in a distributed sensor network is a key issue. Data from the sensors are properly synchronized are very good material for further analysis. In the paper a network of medical sensors is presented. It is important to obtain a properly synchronized data from the sensors. This guarantee that the data can be processed to detect correlation between different signals. For the purpose of accurate time synchronization, the simple and efficient algorithm is presented.

The paper presents a modification of the structure of a biological neural network (BNN) based on spiking neuron models. The proposed modification allows to influence the level of the stimulus response of particular neurons in the BNN. We consider an extended, three-dimensional Hodgkin-Huxley model of the neural cell. A typical BNN composed of such neural cells have been expanded by addition of resistors in each branch point. The resistors can be treated as the weights in such BNN. We demonstrate that adding these elements to the BNN significantly affects the waveform of the potential on the membrane of the neuron, causing an uncontrolled excitation. This provides a better description of processes that take place in nervous cell. Such BNN enables an easy adaptation of the learning rules used in artificial or spiking neural networks. The modified BNN has been implemented on Graphics Processing Unit (GPU) in the CUDA C language. This platform enables a parallel data processing, which is an important feature in such applications.

This paper presents a new distance calculation circuit (DCC) that in artificial neural networks is used to calculate distances between vectors of signals. The proposed circuit is a digital, fully parallel and asynchronous solution. The complexity of the circuit strongly depends on the type of the distance measure. Considering two popular measures i.e. the Euclidean (L2) and the Manhattan (L1) one, it is shown that in the L2 case the number of transistors is even ten times larger than in the L1 case. Investigations carried out on the system level show that the L1 measure is a good estimate of the L2 one. For the L1 measure, for an example case of 4 inputs, for 10 bits of resolution of the signals, the number of transistors is equal to c. 2500. As transistors of minimum sizes can be used, the chip area of a single DCC, if realized in the CMOS 180 nm technology, is less than 0.015 mm 2 .

The purpose of this work is to speed up simulations of neural tissues based on the stochastic version of the Hodgkin–Huxley model. Authors achieve that by introducing the system providing random values with desired distribution in simulation process. System consists of two parts. The first one is a high entropy fast parallel random number generator consisting of a hardware true random number generator and graphics processing unit implementation of pseudorandom generation algorithm. The second part of the system is Gaussian distribution approximation algorithm based on a set of generators of uniform distribution. Authors present hardware implementation details of the system, test results of the mentioned parts separately and of the whole system in neural cell simulation task.

This paper presents data processing method for Attitude Heading and Reference System (AHRS) based on Artificial Neural Networks (ANN). The system consist of MEMS (Micro Electro-Mechanical Systems) based on Inertial Measurement Unit (IMU) consisting of tri-axis gyroscopes, accelerometers and magnetometers providing three dimensional linear accelerations and angular rates. Training data was generated by simulation fusion of samples collected during the flight of Quadcopter. The presented results shows proper functioning of the neural network. Moreover, the presented system provide the possibility to easily add other sensors e.g. GPS, in order to achieve better performance.

In presented paper new approach to Predator-prey algorithm was proposed. With the additional mechanism based on more complex biological behaviours like “adrenalin boost” and usage of Spiking Neural Network minimisation of implementation cost and maximisation of algorithm efficiencies was obtained. Thanks to that algorithm can be easily implemented as on of abstract layers in complex standalone robotic systems.

The purpose of this work was to implement fast simulator of stochastic dendritic neurons based on Hodgkin - Huxley model. In order to achieve satisfactory simulation speed we used parallel computing based on NVidia GPU. We present simulation algorithm written in CUDA C and results for simple dendritic neuron network in tree-like structure.

The tool was created to support the auxiliary clinical observation of the eyeballs movements in cases of different states of consciousness. It enables performing objective observation of the patient reaction to a given external light stimuli. The tool analyzes the coordination of the eyeballs movement with the position of the light stimuli. Images are obtained by camera equipped with infrared filters blocking all visible light with the cutoff light length 715nm.

Nowadays, when automation and computerization have significant influence on civilization development, scientists are attempting to fulfill human needs. Replacing people with more efficient and precise automated devices becomes more and more frequent (let’s take KUKA or FANUC manipulators for instance). Biorobotics can be mentioned as an example of strongly evolving discipline of automatic science. It’s aim is to create machines based on patterns observed in nature. The biggest challenge for scientists is to create a humanoid robot which could communicate with people and also act out emotions. Walking robots can be classified into following groups:  one-legged robots,  two-legged robots,  four-legged robots,  multiple-legged robots. Two and four legged robots are most dominant, but multi-legged solutions offers better stability and easiness in moving on rough surfaces. In this paper we present our proposition of design approach and construction details of octaped robot. Next section says about solutions chosen in design path. Mechanical calculations and parts design was described in section III, also used software was listed there. Section IV specifies electronics layer of robot: circuits, connections etc. OpenGL mechanic visualizations software made in this project is depicted in section V. Section VI says about robot firmware and PC software development. Finally in section VII short conclusion was made and future plans were presented

The purpose of this work was to implement automatic synthesizable VHLD code generation from neural networks models algorithm in Matlab. We present generation algorithm usage and structure. VHDL models are generated with pipelined architecture, also there is hierarchical structure maintained in it. Example of generate model synthesis using Xilinx ISE 10.1 software result was presented.

In this paper we are focusing on the kinetic extension [4] of classic model of Hodgkin and Huxley [2]. We are showing the descent gradient method used in the learning process of neuron, which is described with stochastic kinetic model. In comparison with [1] we use only 3 weights instead of 9: gNa, gK and gL. We show that this model behaves equally accurate as the model of Hodgkin and Huxley with slighter system description.