Paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9207620
Abstract: This paper proposes a Distributed Coding Spiking
Neural Network (DC-SNN) with a self-regulated learning algorithm to deal with pattern classification problems. DC-SNN
employs two hidden layers. First hidden layer has receptive
field neurons that convert the real-valued input features to spike
patterns and the second hidden layer employs LIF neurons with
inhibitory interconnections. The second hidden layer has been
termed as the distributed coding layer in the rest of the paper.
The inhibitory interconnections in distributed coding layer will
ensure that each neuron in this layer learns a distinct spike
pattern from input feature space. The synaptic weights between
layers and the weights of lateral inhibitory connections are
learned using a self-regulated learning algorithm. Self-regulation
identifies neurons for updating in the output layer and distributed
coding layer and also adapts the learning rate based on the
temporal separation between spikes in the output layer. It also
skips learning from samples which are correctly classified with
higher temporal separation and hence prevents over-training.
The detailed performance comparisons of DC-SNN with other
algorithms for SNNs in the literature using six benchmark
data set from the UCI machine learning repository has been
presented. Further, the performance of DC-SNN is evaluated on
a real-world brain computer interface problem for classification
of electroencephalogram (EEG) signals recorded during motorimagery tasks. The results clearly indicate that the proposed DCSNN architecture provides slightly better generalization ability
and is suitable for deep spiking networks.
Authors:
Pranav Machingal
Mohammed Thousif
Shirin Dora
Suresh Sundaram
Citation:
@INPROCEEDINGS{9207620,
author={Machingal, Pranav and Thousif, Mohammed and Dora, Shirin and Sundaram, Suresh},
booktitle={2020 International Joint Conference on Neural Networks (IJCNN)},
title={Self-regulated Learning Algorithm for Distributed Coding Based Spiking Neural Classifier},
year={2020},
volume={},
number={},
pages={1-7},
doi={10.1109/IJCNN48605.2020.9207620}}
