Responses of central auditory neurons modeled with finite impulse response (FIR) neural networks
| Autor: | Pau-Choo Chung, Tsai-Rong Chang, Tzai-Wen Chiu, Paul W.F. Poon, E-Liang Chen |
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| Rok vydání: | 2004 |
| Předmět: |
Auditory Pathways
Finite impulse response Matching (graph theory) Transmission delay Computer science Speech recognition Action Potentials Health Informatics Midbrain symbols.namesake medicine Humans Neurons Spiking neural network Artificial neural network Time delay neural network business.industry Process (computing) Pattern recognition Computer Science Applications Gaussian filter medicine.anatomical_structure symbols Neuron Artificial intelligence Nerve Net business Software |
| Zdroj: | Computer Methods and Programs in Biomedicine. 74:151-165 |
| ISSN: | 0169-2607 |
| DOI: | 10.1016/s0169-2607(03)00077-4 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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