scholarly journals A Neuroscience-Inspired Spiking Neural Network for Auditory Spatial Attention Detection Using Single-Trial EEG

2021 ◽  
Author(s):  
Faramarz Faghihi ◽  
Siqi Cai ◽  
Ahmed Moustafa
Keyword(s):  
Neural Network ◽  
Spatial Attention ◽  
Sparse Coding ◽  
Spiking Neurons ◽  
Training Data ◽  
Spiking Neural Network ◽  
Single Trial ◽  
Auditory Spatial Attention ◽  
Eeg Data

Recently, studies have shown that the alpha band (8-13 Hz) EEG signals enable the decoding of auditory spatial attention. However, deep learning methods typically requires a large amount of training data. Inspired by sparse coding in cortical neurons, we propose a spiking neural network model for auditory spatial attention detection. The model is composed of three neural layers, two of them are spiking neurons. We formulate a new learning rule that is based on firing rate of pre synaptic and post-synaptic neurons in the first layer and the second layer of spiking neurons. The third layer consists of 10 spiking neurons that the pattern of their firing rate after training is used in test phase of the method. The proposed method extracts the patterns of recorded EEG of leftward and rightward attention, independently, and uses them to train network to detect the auditory spatial attention. In addition, a computational approach is presented to find the best single-trial EEG data as training samples of leftward and rightward attention EEG. In this model, the role of using low connectivity rate of the layers and specific range of learning parameters in sparse coding is studied. Importantly, unlike most prior model, our method requires 10% of EEG data as training data and has shown 90% accuracy in average. This study suggests new insights into the role of sparse coding in both biological networks and brain-inspired machine learning.

scholarly journals Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1065
Author(s):  
Moshe Bensimon ◽  
Shlomo Greenberg ◽  
Moshe Haiut
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Analog Circuits ◽  
Continuous Time ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Sound Classification ◽  
Classification Framework ◽  
Neural Network Structure ◽  
Simple Analog

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

scholarly journals Pengaruh Latar Belakang Warna pada Objek Gambar terhadap Hasil Ekstraksi Sinyal EEG

2017 ◽  
Vol 7 (2) ◽  
pp. 161 ◽  
Author(s):  
Catur Atmaji ◽  
Zandy Yudha Perwira
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training System ◽  
Training Data ◽  
Spectrum Estimation ◽  
Quality Classification ◽  
Eeg Data ◽  
Two Systems ◽  
Artificial Neural ◽  
Color Background

In this study, observation on the differences in features quality of EEG records as a result of training on subjects has been made. The features of EEG records were extracted using two different methods, the root mean square which is acquired from the range between 0.5 and 5 seconds and the average of power spectrum estimation from the frequency range between 20 and 40Hz. All of the data consists of a 4-channel recording and produce good quality classification on artificial neural network, with each of which generates training data accuracy over 90%. However, different results are occured when the trained system is tested on other test data. The test results show that the two systems which are trained using training data with object with color background produce higher accuracy than the other two systems which are trained using training data with object without background color, 63.98% and 60.22% compared to 59.68% and 56.45% accuracy respectively. From the use of the features on the artificial neural network classification system, it can be concluded that the training system using EEG data records derived from the visualization of object with color background produces better features than the visualization of object without color background.

Control chart pattern clustering using a new self-organizing spiking neural network

2008 ◽  
Vol 222 (10) ◽  
pp. 1201-1211 ◽  
Author(s):  
D T Pham ◽  
M S Packianather ◽  
E Y A Charles
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Neural Network Model ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Pattern Clustering ◽  
Control Chart Patterns ◽  
Chart Patterns ◽  
Spiking Neural Network Model ◽  
Self Organizing

This paper focuses on the architecture and learning algorithm associated with using a new self-organizing delay adaptation spiking neural network model for clustering control chart patterns. This temporal coding spiking neural network model employs a Hebbian-based rule to shift the connection delays instead of the previous approaches of delay selection. Here the tuned delays compensate the differences in the input firing times of temporal patterns and enables them to coincide. The coincidence detection capability of the spiking neuron has been utilized for pattern clustering. The structure of the network is similar to that of a Kohonen self-organizing map (SOM) except that the output layer neurons are coincidence detecting spiking neurons. An input pattern is represented by the neuron that is the first to fire among all the competing spiking neurons. Clusters within the input data are identified with the location of the winning neurons and their firing times. The proposed self-organized delay adaptation spiking neural network (SODA_SNN) has been utilized to cluster control chart patterns. The trained network obtained an average clustering accuracy of 96.1 per cent on previously unseen test data. This was achieved with a network of 8 × 8 spiking neurons trained for 20 epochs containing 1000 training examples. The improvement in clustering accuracy achieved by the proposed SODA_SNN on the unseen test data was twice as much as that on the training data when compared to the SOM.

scholarly journals Investigating the role of orientation information in face processing within a spiking neural network

2021 ◽  
Vol 21 (9) ◽  
pp. 2766
Author(s):  
Matthew Bennett ◽  
Tushar Chauhan ◽  
Benoît Cottereau ◽  
Valerie Goffaux
Keyword(s):  
Neural Network ◽  
Face Processing ◽  
Spiking Neural Network ◽  
Orientation Information
Sign in / Sign up

Export Citation Format

Share Document