scholarly journals Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
A. Espinal ◽  
H. Rostro-Gonzalez ◽  
M. Carpio ◽  
E. I. Guerra-Hernandez ◽  
M. Ornelas-Rodriguez ◽  
...  
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Pattern Generator ◽  
Quadruped Robot ◽  
Spiking Neural Network ◽  
Resource Usage ◽  
Hexapod Robot ◽  
Biologically Inspired ◽  
Robot Locomotion ◽  
Locomotion System

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases.

Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network

2011 ◽  
Vol 11 (2) ◽  
pp. 2035-2041 ◽  
Author(s):  
Aboul Ella Hassanien ◽  
Hameed Al-Qaheri ◽  
El-Sayed A. El-Dahshan
Keyword(s):  
Neural Network ◽  
Boundary Detection ◽  
Ultrasound Images ◽  
Spiking Neural Network ◽  
Biologically Inspired

scholarly journals Multi-expert learning of adaptive legged locomotion

2020 ◽  
Vol 5 (49) ◽  
pp. eabb2174
Author(s):  
Chuanyu Yang ◽  
Kai Yuan ◽  
Qiuguo Zhu ◽  
Wanming Yu ◽  
Zhibin Li
Keyword(s):  
Neural Network ◽  
Motor Skills ◽  
Deep Neural Network ◽  
Legged Locomotion ◽  
Quadruped Robot ◽  
Adaptive Skills ◽  
Robot Locomotion ◽  
Specialized Experts ◽  
Adaptive Policies ◽  
Fall Recovery

Achieving versatile robot locomotion requires motor skills that can adapt to previously unseen situations. We propose a multi-expert learning architecture (MELA) that learns to generate adaptive skills from a group of representative expert skills. During training, MELA is first initialized by a distinct set of pretrained experts, each in a separate deep neural network (DNN). Then, by learning the combination of these DNNs using a gating neural network (GNN), MELA can acquire more specialized experts and transitional skills across various locomotion modes. During runtime, MELA constantly blends multiple DNNs and dynamically synthesizes a new DNN to produce adaptive behaviors in response to changing situations. This approach leverages the advantages of trained expert skills and the fast online synthesis of adaptive policies to generate responsive motor skills during the changing tasks. Using one unified MELA framework, we demonstrated successful multiskill locomotion on a real quadruped robot that performed coherent trotting, steering, and fall recovery autonomously and showed the merit of multi-expert learning generating behaviors that can adapt to unseen scenarios.

scholarly journals Image Edge Detector with Gabor Type Filters Using a Spiking Neural Network of Biologically Inspired Neurons

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 165 ◽  
Author(s):  
Krishnamurthy V. Vemuru
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Image Classification ◽  
Spiking Neural Network ◽  
Edge Detector ◽  
Time Step ◽  
Biologically Inspired ◽  
Total Exposure Time ◽  
Canny Edge ◽  
Low Exposure

We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower exposure times, that is, for 1 < t < T /4, where t is the number of milliseconds in a simulated exposure frame and T is the total exposure time, with reference to a Sobel edge or Canny edge detection layer in the pipeline. These results pave the way for developing novel cognitive neuromorphic computing architectures for millisecond timescale detection and object classification applications using event or spike cameras.

Neuromorphic Memristive Chips: Design and Technology

2021 ◽  
Vol 23 (6) ◽  
pp. 285-294
Author(s):  
N.V. Andreeva ◽  
◽  
V.V. Luchinin ◽  
E.A. Ryndin ◽  
M.G. Anchkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Resistive Switching ◽  
Hardware Implementation ◽  
Functional Materials ◽  
Design And Technology ◽  
Spiking Neural Network ◽  
Chip Design ◽  
Neuromorphic Hardware ◽  
Metal Insulator Metal

Memristive neuromorphic chips exploit a prospective class of novel functional materials (memristors) to deploy a new architecture of spiking neural networks for developing basic blocks of brain-like systems. Memristor-based neuromorphic hardware solutions for multi-agent systems are considered as challenges in frontier areas of chip design for fast and energy-efficient computing. As functional materials, metal oxide thin films with resistive switching and memory effects (memristive structures) are recognized as a potential elemental base for new components of neuromorphic engineering, enabling a combination of both data storage and processing in a single unit. A key design issue in this case is a hardware defined functionality of neural networks. The gradient change of resistive properties of memristive elements and its non-volatile memory behavior ensure the possibility of spiking neural network organization with unsupervised learning through hardware implementation of basic synaptic mechanisms, such as Hebb's learning rules including spike — timing dependent plasticity, long-term potentiation and depression. This paper provides an overview of scientific researches carrying out at Saint Petersburg Electrotechnical University "LETI" since 2014 in the field of novel electronic components for neuromorphic hardware solutions of brain-like chip design. Among the most promising concepts developed by ETU "LETI" are: the design of metal-insulator-metal structures exhibiting multilevel resistive switching (gradient tuning of resistive properties and bipolar resistive switching are combined together in a sin¬gle memristive element) for further use as artificial synaptic devices in neuromorphic chips; computing schemes for spatio-temporal pattern recognition based on spiking neural network architecture implementation; breadboard models of analogue circuits of hardware implementation of neuromorphic blocks for brain-like system developing.

A Biologically Inspired Evolving Spiking Neural Model with Rank-Order Population Coding and a Taste Recognition System Case Study

2011 ◽  
pp. 136-155 ◽  
Author(s):  
S. Soltic ◽  
N. Kasabov
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Rank Order ◽  
Neural Model ◽  
Recognition System ◽  
Population Coding ◽  
Spiking Neural Network ◽  
Biologically Inspired ◽  
Information Encoding ◽  
Evolving Spiking Neural Network

The human brain has an amazing ability to recognize hundreds of thousands of different tastes. The question is: can we build artificial systems that can achieve this level of complexity? Such systems would be useful in biosecurity, the chemical and food industry, security, in home automation, etc. The purpose of this chapter is to explore how spiking neurons could be employed for building biologically plausible and efficient taste recognition systems. It presents an approach based on a novel spiking neural network model, the evolving spiking neural network with population coding (ESNN-PC), which is characterized by: (i) adaptive learning, (ii) knowledge discovery and (iii) accurate classification. ESNN-PC is used on a benchmark taste problem where the effectiveness of the information encoding, the quality of extracted rules and the model’s adaptive properties are explored. Finally, applications of ESNN-PC in recognition of the increasing interest in robotics and pervasive computing are suggested.

A Distributed Neural Network Architecture for Hexapod Robot Locomotion

1992 ◽  
Vol 4 (3) ◽  
pp. 356-365 ◽  
Author(s):  
Randall D. Beer ◽  
Hillel J. Chiel ◽  
Roger D. Quinn ◽  
Kenneth S. Espenschied ◽  
Patrik Larsson
Keyword(s):  
Neural Network ◽  
Real World ◽  
Network Architecture ◽  
Command Neuron ◽  
Legged Robot ◽  
Hexapod Robot ◽  
Neural Network Architecture ◽  
Insect Locomotion ◽  
Robot Locomotion ◽  
Continuous Range

We present fully distributed neural network architecture for controlling the locomotion of a hexapod robot. The design of this network is directly based on work on the neuroethology of insect locomotion. Previously, we demonstrated in simulation that this controller could generate a continuous range of statically stable insect-like gaits as the activity of a single command neuron was varied and that it was robust to a variety of lesions. We now report that the controller can be utilized to direct the locomotion of an actual six-legged robot, and that it exhibits a range of gaits and degree of robustness in the real world that is quite similar to that observed in simulation.

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