Responses of central auditory neurons modeled with finite impulse response (FIR) neural networks

Tsai Rong Chang, E. Liang Chen, Paul Wai Fung Poon, Pau Choo Chung*, Tzai-Wen Chiu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


To simulate central auditory responses to complex sounds, a computational model was implemented. It consists of a multi-scale classification process, and an artificial neural network composed of two modules of finite impulse response (FIR) neural networks connected to a maximum network. Electrical activities of single auditory neurons were recorded at the rat midbrain in response to a repetitive pseudo-random frequency modulated (FM) sound. The multi-scale classification process divides the training dataset into either strong or weak response using a multiple-scale Gaussian filter that based on response probability. Two modules of FIR neural network are then independently trained to model the two types of responses. This caters for the possible differences in neuronal circuitry and transmission delay. Their outputs are connected to a maximum network to generate the final output. After training, we use a different set of FM responses collected from the same neuron to test the performance of the model. Two criteria are adopted for assessment. One measures the matching of the modeled output to the actual output on a point-to-point basis. Another measures the matching of bulk responses between the two. Results show that the proposed model predicts the responses of central auditory neurons satisfactorily.

Original languageEnglish
Pages (from-to)151-165
Number of pages15
JournalComputer Methods and Programs in Biomedicine
Issue number2
StatePublished - 1 May 2004


  • Central auditory neuron modeling
  • Finite impulse response neural networks
  • Midbrain
  • STRF

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