TempUnit: A bio-inspired neural network model for signal processing

– We have developed and tested a novel artificial neural network for the processing of temporal signals. The working of the units (TempUnit) is based on the mechanism of temporal summation as observed in biological neurons. Its particularity is to adapt its basis function by supervised learning. The model was tested on cortical and associated muscular (EMG) recordings from the behaving primate. The TempUnit showed a 2.3 times better performance in mapping spiking to EMG activity than a time delay multi-layer perceptron. Data compression capacity of the TempUnit was tested on audio data and compared to the MP3 compression standard. For a similar reproduction quality, we found a compression rate 5 times higher than in MP3.

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Olivier F. Manette, Marc A. Maier
• Keywords: Artificial Neural Network, Data Compression Capacity, Delay Multi-layer Perceptron, IJCNN 2006