Efficient Signal Processing of Multineuronal Activities for Neural Interface and Prosthesis

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

• References

• 1. Tamura H, Kaneko H, Kawasaki K, Fujita I. Presumed inhibitory neurons in the macaque inferior temporal cortex: visual response properties and functional interactions with adjacent neurons. J Neurophysiol 2004; 91 (06) 2782-2796.
• 2. McNaughton BL, O’Keefe J, Barnes CA. The stereotrode: anew technique for simultaneous isolation of several single units in the central nervous system from multiple unit records. J Neurosci Methods 1983; 8 (04) 391-397.
• 3. Kaneko H, Suzuki SS, Okada J, Akamatsu M. Multineuronal spike classification based on multisite electrode recording, whole-waveform analysis, and hierarchical clustering. IEEE Trans Biomed Eng 1999; 46 (03) 280-290.
• 4. Gochin PM, Colombo M, Dorfman GA, Gerstein GL, Gross CG. Neural ensemble coding in inferior temporal cortex. J Neurophysiol 1994; 71 (06) 2325-2337.
• 5. Krzanowski WJ. Principles of multivariate analysis: a user’s perspective (revised edition). New York: Oxford University Press; 2000
• 6. Gochin PM, Miller EK, Gross CG, Gerstein GL. Functional interactions among neurons in inferior temporal cortex of the awake macaque. Exp Brain Res 1991; 84 (03) 505-516.
• 7. Tamura H, Kaneko H, Fujita I. Quantitative analysis of functional clustering of neurons in the macaque inferior temporal cortex. Neurosci Res 2005; 52 (04) 311-322.
• 8. Wang Y, Fujita I, Murayama Y. Neuronal mechanisms of selectivity for object features revealed by blocking inhibition in inferotemporal cortex. Nat Neurosci 2000; 3 (08) 807-813.
• 9. Gawne TJ, Richmond BJ. How independent are the messages carried by adjacent inferior temporal cortical neurons?. J Neurosci 1993; 13 (07) 2758-2771.
• 10. Schneidman E, Bialek W, Berry MJ. Synergy, redundancy, and independence in population codes. JNeurosci 2003; 23 (37) 11539-11553.
• 11. Andersen RA, Musallam S, Pesaran B. Selecting the signals for a brain-machine interface. Curr Opin Neurobiol 2004; 14 (06) 720-726.
• 12. Ginter J, Blinowska KJ, Kaminski M, Durka PJ, Pfurtscheller G, Neuper C. Propagation of EEG activity in the beta and gamma band during movement imagery in humans. Methods Inf Med 2005; 44 (01) 106-113.