Verifying neural codes: the statistical pattern recognition approach

In recent years, a variety of statistical methods have been proposed for studying neuronal codes. Many methods are developed by researchers specifically for their own data and are rarely validated by outside groups. An alternative approach is to use methods that have been developed by experts in signal processing and statistics, that have passed through peer review, and that are widely available. My research group uses such methods to study neural coding in the cerebral cortex. We study simultaneously record neuronal spike trains and wideband field potentials in the cerebral cortex of animals that perform psychophysical and sensorimotor behavioral tasks. This approach allows us to assess neuronal codes that are behaviorally relevant, free from the effects of anesthesia, and may be represented across multiple scales. We define relevant features in neuronal activity using wavelet-based methods and then use pattern recognition methods to quantify the information content of the features on a trial-by-trial basis. Using these methods, we are able to interpret the network properties of neuronal codes and, using a cluster of workstations, we can now analyze neuronal ensemble data sets during the actual performance of behavioral tasks. This approach leads to a new kind of experimental neurophysiology that allows for the verification of neuronal codes.