Summary
Objectives
: Multineuronal spike trains must be efficiently decoded in order to utilize them
for controlling artificial limbs and organs. Here we evaluated the efficiency of pooling
(averaging) and combining (vectorizing) activities of multiple neurons for decoding
neuronal information.
Methods
: Multineuronal activities in the monkey inferior temporal (IT) cortex were obtained
by classifying spikes of constituent neurons from multichannel data recorded with
a multisite microelectrode. We compared pooling and combining procedures for the amount
of visual information transferred by neurons, and for the success rate of stimulus
estimation based on neuronal activities in each trial.
Results
: Both pooling and combining activities of multiple neurons increased the amount of
information and the success rate with the number of neurons. However, the degree of
improvement obtained by increasing the number of neurons was higher when combining
activities as opposed to pooling them.
Conclusion: Combining the activities of multiple neurons is more efficient than pooling them
for obtaining a precise interpretation of neuronal signals.
Keywords
Information theory - correlation - spike sorting - vision - prosthesis
