A low-cost method to analyse footprint patterns.

Neurological dysfunction can be assessed by analysing footprint patterns and walking tracks. However, because such an analysis is very time consuming, we developed an MS-Windows program called FOOTPRINTS which facilitates the analysis of the commonly used measures and which is considerably quicker than manual scoring methods. The prints are scanned at a resolution of 75 dpi and stored as black and white bitmaps for further analysis. In order to validate the program, we analysed the footprint patterns of mice and rats, using both the program and the conventional manual scoring method. In the first study, the walking patterns of 3-, 14-, and 26-month-old Janvier Wistar rats were compared, and in the second the footprint patterns of C57BL mice were assessed. Comparison of the data obtained using the program and of the data obtained by manual scoring showed that the computer-based analysis gives reliable results. The program saves considerable time as the analysis took 1/8th of the time needed for manual evaluation.

Analysis, classification, and coding of multielectrode spike trains with hidden Markov models.

It is shown that hidden Markov models (HMMs) are a powerful tool in the analysis of multielectrode data. This is demonstrated for a 30-electrode measurement of neuronal spike activity in the monkey's visual cortex during the application of different visual stimuli. HMMs with optimized parameters code the information contained in the spatiotemporal discharge patterns as a probabilistic function of a Markov process and thus provide abstract dynamical models of the pattern-generating process. We compare HMMs obtained from vector-quantized data with models in which parametrized output processes such as multivariate Poisson or binomial distributions are assumed. In the latter cases the visual stimuli are recognized at rates of more than 90% from the neuronal spike patterns. An analysis of the models obtained reveals important aspects of the coding of information in the brain. For example, we identify relevant time scales and characterize the degree and nature of the spatiotemporal variations on these scales.