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Self-organizing map (SOM) has been applied in many different fields of science and engineering. In this chapter it is shown how the SOM can be used to decode neural spike trains of an awake animal and associate external auditory stimuli with spike patterns of the brain. This chapter begins with an introduction of the SOM and highlights advantages of the SOM when compared to other multivariate statistical data analysis tools commonly used to analyze similar data sets. In this study, simultaneous multichannel recording from guinea pig auditory cortex is examined using the SOM to assess the effectiveness and potential of the SOM in neurophysiological studies.

We developed a biologically plausible, compartmental model of stellate cells in the cochlear nucleus (CN) to investigate the effects of synaptic distribution on spike activity using constant and amplitude-modulated (AM) tones. Differences in the spatial distribution of excitatory auditory nerve (AN) inputs and inhibitory non-cochlear inputs evoke a range of post-stimulus time histogram (PSTH) chopper responses from sustained to transient to onset. These simulations illustrate how intrinsic membrane properties and non-linear dendritic processing of AN inputs determine the temporal responses characteristic of stellate cells observed in PSTHs and modulation transfer functions (MTFs).

The cochlear nucleus (CN) is the gateway to the central auditory system. The auditory nerve branches and terminates in the CN forming separate tonotopic maps in each of the nucleus’s three subdivisions. Octopus cells are found exclusively in the posteroventral cochlear nucleus and project to the inferior colliculus. These cells are sensitive to physical properties of sound, such as frequency, intensity, and amplitude fluctuations, although their role in auditory processing is unknown. Over a 40 dB dynamic range, the octopus cell always fires an action potential at the onset of a pure tone and then fires randomly at much lower rates throughout the rest of the tone. This onset response has not yet been explained in terms of the cell’s known physiological and anatomical properties, which include a fast membrane, highly convergent primary afferent inputs, and few inhibitory inputs. A better understanding of the properties underlying this response will provide increased, quantitative insight into auditory processing.

We describe a computational model of the Limulus lateral eye to investigate the relationship between the neural coding of the retina and the visual performance of the animal. The network model, implemented on the massively parallel Connection Machine, computes the firing rates of optic-nerve fibers for simulated visual scenes. Intensity-response functions computed for individual nerve fibers that project to the brain agree with experimental measurements. Responses computed for the entire array of retinal neurons show that moving targets enhance the modulation of optic-nerve activity.