The detection of individual brain cells activity is essential to studying brain function. Most brain cells communicate by firing action potentials (spikes). Brain information is thought to be encoded in the timing between successive spikes. Neural data collected from tetrodes directly implanted in the neural tissue seems to provide highly accurate data. Detecting spikes and identifying the firing times of a specific neuron require analyzing the signals from the four tetrode conductors simultaneously and filtering useful information from any background noise. The computational simplicity of voltage thresholding makes it the most common method or the detecting action potentials buried in noise. Nevertheless, it is not straightforward to apply the technique to several channels simultaneously. The proposed algorithm combines the four tetrode signals and compares the output against an adaptive voltage threshold detector. The signals are combined using an analog signal multiplication that emphasizes the times when all four signals are spiking. The adaptive threshold detector sets itself to the background noise level. The entire operation is done in analog and therefore makes the system low power and highly suitable for hardware implementation. This technique has been demonstrated in Matlab simulation using real tetrode data and is being adapted for an all-analog circuit implementation.
展开▼
