  Understanding the function of complex cortical circuits requires the
simultaneous recording of action potentials from many neurons in awake and
behaving animals. Practically, this can be achieved by extracellularly
recording from multiple brain sites using single wire electrodes. However, in
densely packed neural structures such as the human hippocampus, a single
electrode can record the activity of multiple neurons. Thus, analytic
techniques that differentiate action potentials of different neurons are
required. Offline spike sorting approaches are currently used to detect and
sort action potentials after finishing the experiment. Because the
opportunities to record from the human brain are relatively rare, it is
desirable to analyze large numbers of simultaneous recordings quickly using
online sorting and detection algorithms. In this way, the experiment can be
optimized for the particular response properties of the recorded neurons. Here
we present and evaluate a method that is capable of detecting and sorting
extracellular single-wire recordings in realtime. We demonstrate the utility of
the method by applying it to an extensive data set we acquired from
chronically-implanted depth electrodes in the hippocampus of human epilepsy
patients. This dataset is particularly challenging because it was recorded in a
noisy clinical environment. This method will allow the development of
closed-loop experiments, which immediately adapt the experimental stimuli
and/or tasks to the neural response observed.
