Efficient Signal Processing of Multineuronal Activities for Neural Interface and Prosthesis

Evaluating Auditory Neural Activities and Information Transfer Using Phase and Spike Train Correlation Algorithms

The coherence of neural activities among different areas in the brain has received great attention because it is valuable in understanding the functional mechanism of brain structures. While many methodologies, such as time-frequency and entropy analysis, have been applied to evaluate relations between neural signals, these techniques haven't been effective in assessing neural communication in order to reach conclusions. Considering various measurements, the results analyzed by the above-mentioned algorithms may be influenced by the types of neural signals and their amplitudes, which affect their reliability and consistency. In this study, we introduced two new methods, phase-phase and spike train correlations, to analyze the neural signals communications among various areas of the brain, aiming to decipher neural information communications between different brain structures of normal rats and those with noise-induced tinnitus, a ringing condition in the ear or head. To test the proposed methodologies, a set of electrophysiological recordings of tinnitus-related spontaneous activities were conducted in the auditory cortex (AC), inferior colliculus (IC), and dorsal cochlear nucleus (DCN). The results using the two proposed algorithms were demonstrated and compared to those obtained by the transfer entropy (TE) method using the same experimental data set. Both algorithms yielded a result in a consistent scale of zero to one indicating the strength of correlation and showed a similar trend to results by TE. The experimental results on rats have shown information flow within and between most structures with a stronger correlation at lower frequencies.

Multineuronal vectorization is more efficient than time-segmental vectorization for information extraction from neuronal activities in the inferior temporal cortex

In order for patients with disabilities to control assistive devices with their own neural activity, multineuronal spike trains must be efficiently decoded because only limited computational resources can be used to generate prosthetic control signals in portable real-time applications. In this study, we compare the abilities of two vectorizing procedures (multineuronal and time-segmental) to extract information from spike trains during the same total neuron-seconds. In the multineuronal vectorizing procedure, we defined a response vector whose components represented the spike counts of one to five neurons. In the time-segmental vectorizing procedure, a response vector consisted of components representing a neuron's spike counts for one to five time-segment(s) of a response period of 1 s. Spike trains were recorded from neurons in the inferior temporal cortex of monkeys presented with visual stimuli. We examined whether the amount of information of the visual stimuli carried by these neurons differed between the two vectorizing procedures. The amount of information calculated with the multineuronal vectorizing procedure, but not the time-segmental vectorizing procedure, significantly increased with the dimensions of the response vector. We conclude that the multineuronal vectorizing procedure is superior to the time-segmental vectorizing procedure in efficiently extracting information from neuronal signals.