Abstract
Understanding the causal relation between neural inputs and movements is very important for the success of brain-machine interfaces (BMIs). In this study, we analyze 104 neurons’ firings using statistical, information theoretic, and fractal analysis. The latter include Fano factor analysis, multifractal adaptive fractal analysis (MF-AFA), and wavelet multifractal analysis. We find neuronal firings are highly non-stationary, and Fano factor analysis always indicates long-range correlations in neuronal firings, irrespective of whether those firings are correlated with movement trajectory or not, and thus does not reveal any actual correlations between neural inputs and movements. On the other hand, MF-AFA and wavelet multifractal analysis clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal correlations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal correlations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a “re-setting” effect at the start of each reaching task, in the sense that within the movement correlated neurons the spike trains’ long-range dependences persisted about the length of time the monkey used to switch between task executions. A new task execution re-sets their activity, making them only weakly correlated with their prior activities on longer time scales. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.
Highlights
Brain-machine interface (BMI) is aimed to provide a method for people with damaged sensory and motor functions to use their brain to control artificial devices and restore their lost ability via the devices
More than half of the neurons behaved like this. (iii) While neurons 2–4, plotted in Figures 1C–E, appear to have strong correlations with the hand movement trajectory, the degree of correlation varies with time considerably
It should be mentioned that albeit neuron 5, shown in Figure 1F, fired a lot during all these three periods, it had “quiet” periods even though the monkey was actively grabbing food to mouth. These observations suggest (i) different neurons have different degree of importance in determining the causal relation between neural inputs and hand movements, and (ii) even for the same neuron, this degree of importance varies with time considerably
Summary
Brain-machine interface (BMI) is aimed to provide a method for people with damaged sensory and motor functions to use their brain to control artificial devices and restore their lost ability via the devices. The adaptive models proposed usually contain a very large number of parameters and require very extensive training [25,26,27] They assume that neuronal firings in the cortex are stationary, while in reality this rarely can be true. To help gain fundamental understanding of the causal relation between neural inputs and hand movements, in this study, we examine long-range temporal correlations (or long-range dependence, LRD in short) and multifractality in a large group of neuronal firings. We employ three different types of fractal and multifractal analysis methods to explore how temporal LRD may be associated with the causal relations between neural inputs and movement trajectory
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