Temporal pattern discrimination in the cat´s retinal cells and Markov system models

Markov system theory is applied to temporal pattern sensitivity in nervous systems. First, a Markov system model is given to describe the temporal pattern sensitivity in single neurons with one input and one output. This is extended to a two-input one-output system. In both cases the behavior of input-output relations is estimated by using Shannon´s information theory in single-access and multiple-access channels. The interaction mechanisms between excitatory and inhibitory input sequences in single neurons are also described. Finally, to characterize temporal pattern sensitivity in the visual pathway, the Markov system approach is introduced using three types of statistical spot stimuli with different second-order statistics (adjacent interstimulus interval): type 1 with positive, type 2 with negative correlation, and type 3 with independent intervals, but identical first-order statistics (mean, variance, and histogram in the interstimulus intervals). The differences in response to these three types of stimuli, i.e. the differences in interspike interval distribution, can be used to classify cat ganglion cells into two groups, type L and type N. A type L cell has an identical histogram in interspike intervals for all three stimuli (no sensitivity to the temporal pattern); on the other hand, a type N cell has different histograms depending on the type of stimulus and is therefore highly sensitive to the temporal pattern. Different features between type L and type N cells were stimulated by using the Markov system model having 2×2 state transition matrixes. By giving distinctive features to the state transition matrices, the interspike interval histograms of the model could be made to agree well with those of type L and type N cells.