# The Application of Liquid State Machines in Robot Path Planning

@article{Zhang2009TheAO, title={The Application of Liquid State Machines in Robot Path Planning}, author={Yanduo Zhang and Kun Wang}, journal={J. Comput.}, year={2009}, volume={4}, pages={1182-1186} }

This paper discusses the Liquid state machines and does some researches on spiking neural network and Parallel Delta Rule, using them to solve the robot path planning optimization problems, at the same time we do simulation by Matlab, the result of the experimental reveal that the LSM can solve these problems effectively.

#### 13 Citations

Intelligent Reservoir Generation for Liquid State Machines using Evolutionary Optimization

- Computer Science
- 2019 International Joint Conference on Neural Networks (IJCNN)
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This paper employs the reservoir computing technique and genetic algorithms in order to develop useful networks that can be deployed on neuromorphic hardware and discusses the complexities of determining whether or not to use the genetic algorithms approach for liquid state machine generation. Expand

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A new low-cost methodology to implement high-density LSM by using Boolean gates is shown, based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. Expand

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The trained stochastic kinetic model of neuron is tested in solving the problem of approximation, where for the approximated function the membrane potential obtained using different models of a biological neuron was chosen. Expand

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition

- Computer Science, Medicine
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The simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks. Expand

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- Computer Science, Medicine
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A new neuro-inspired, hardware-friendly readout stage for the liquid state machine (LSM), a popular model for reservoir computing, which can attain better performance with significantly less synaptic resources making it attractive for VLSI implementation. Expand

MOTION LEARNING USING SPATIO-TEMPORAL NEURAL NETWORK

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This study proposes motion learning using spatio temporal neural network based on reward-modulated spike-timing-dependent plasticity (STDP), whereby the learning weight adjustment provided by the standard STDP is modulated by the reinforcement. Expand

Handwritten signatures recognition using Liquid State Machine

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This work investigated a recently proposed model 'Liquid State Machine (LSM) using spiking neural network' and its applicability for recognition of the 'handwritten signature problem'. Expand

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A novel model of Key-Threshold based Spiking Neural Network (KTSNN) is proposed, which consists of quasi-neurons oriented to recognize any key-spikes distributed in time (sequence of spikes) or in space (in synapses). Expand

Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity

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This work studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised learning, with an output layer joined by DA-STDP for supervised learning, to perform pattern classification and confirmed that this network could performpattern classification using the STDP effect. Expand

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This review discusses areas where metaheuristics are used in the echo state network—a pioneer in the reservoir computing field and trends and research gaps are discussed. Expand

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