Decoding of auditory cortex signals with a LAMSTAR neural network

Objectives: Each neuron has a specific set of stimuli, which it preferentially responds to (the receptive field of the neuron). For implantable cortical prosthetic devices specific points of the cortex (or groups of neurons) have to be stimulated to create perceptions of sensory stimulus with specific attributes (such as frequency, temporal characteristics, etc). Such applications would need real time decoding of signals. Previously mathematical techniques, such as computing the receptive field (using electropnysiology data) and artificial neural networks (Kohonen network or SOM and back propagation network) have been used to decode neural signals. Methods: A Large Adaptive Memory Storage and Retrieval (LAMSTAR) neural-network-based decoder was designed to decode responses recorded from neurons in the auditory cortex. It was designed to identify the frequency of the tonal stimuli that elicited a particular discharge rate pattern recorded on two channels of a tungsten wire electrode array. Results: The network functioned efficiently as a decoder with 100% accuracy for the small sample of stimulus-response data used. Discussion: The results show that the network is effective in studying the functional organization of the auditory cortex and other sensory systems. Depending on the input sub-word, information about the kind of stimuli that activates particular parts of the sensory cortex can be studied.