Auditory stimulus optimization with feedback from fuzzy clustering of neuronal responses

The primary focus of this paper was to develop a high-performance computer system for optimizing auditory stimuli based on neuronal feedback. Using the algorithm of pattern extraction (ALOPEX) extra-cellular action potentials (APs) recorded from frog (Rana Pipiens) auditory neurons were used as feedback to optimize sound stimuli. This computer-based system works in real time to iteratively find the neuron's best excitatory frequency (BEF). Three programmable (positive and negative) threshold logic levels are used to collect 300 APs in response to normalized pure tones. Fuzzy logic is then used to separate up to five fuzzy centers (templates) from the 300 APs. The fuzzy centers are used for on-line fuzzy mapping of future responses. The five fuzzy centers allow the system to monitor up to five neighboring neurons. To study the auditory neurons of the frog, one, two, and three simultaneous tones are used as the stimulus for optimization of the best combination of frequencies. Testing with the response calculated as a parabolic function of a single best frequency demonstrated system dynamics and reliability for up to nine simultaneous tones. Experiments using one pure tone and ten stimulus presentations per iteration showed that the automated system is able to repeatedly converge to the best frequency within 100 iterations. Studies using one, two, and then three pure tones played simultaneously on the same group of neurons has shown that these tones converged on the same best frequencies by properly mixing the tones available to produce the optimal complex sound.